feat(memory): add SQLite memory service

[summerx96]

Summary

• add stdlib-only SQLite memory service with optional FTS5 and LIKE fallback
• implement upsert/idempotency and snippet-based memory entries
• register sqlite:// memory scheme with tests
• document SQLite usage in memory sample

Testing

• .venv/bin/python -m pytest tests/unittests/memory/test_sqlite_memory_service.py tests/unittests/cli/test_service_registry.py

[google-cla]

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[gemini-code-assist]

Summary of Changes

Hello @summerx96, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a robust, persistent local memory solution for the ADK by integrating a new SQLite-based memory service. This enhancement allows for reliable storage and retrieval of session data, improving the framework's ability to maintain context across interactions. The service is designed for efficiency with idempotent updates and intelligent search capabilities, making it a valuable addition for applications requiring durable memory without external database dependencies.

Highlights

• New SQLite Memory Service: A new memory service has been added that uses SQLite for persistent local storage, built using only standard Python libraries. It includes optional support for FTS5 for advanced full-text search, with a fallback to SQL LIKE queries if FTS5 is unavailable.
• Idempotent Session Management: The service implements upsert logic and content hashing to ensure that adding a session is idempotent, meaning sessions are only updated if their content has genuinely changed, preventing redundant writes.
• Snippet-Based Memory Entries: Memory entries returned from searches are now snippet-based, providing concise and relevant portions of the stored session content, improving search result readability.
• Service Registry Integration: The new SQLite memory service is registered with the ADK's service registry, allowing it to be easily configured and used via a sqlite:// URI scheme in both programmatic and CLI contexts.
• Documentation and Testing: Usage of the SQLite memory service is documented in the memory sample README, and comprehensive unit tests have been added to cover its core functionalities, including idempotency, persistence, and search.

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[adk-bot]

Response from ADK Triaging Agent

Hello @summerx96, thank you for creating this PR!

Before we can review your contribution, could you please sign the Contributor License Agreement (CLA)? It seems the check is currently failing.

Also, for a new feature like this, could you please create and associate a GitHub issue with this PR? This helps us track new features and discussions around them.

Additionally, it would be very helpful for the reviewers if you could provide a screenshot or some logs demonstrating the new SQLite memory service in action.

This information will help reviewers to review your PR more efficiently. Thanks!

[gemini-code-assist]

Code Review

This pull request introduces a new SqliteMemoryService, providing a persistent, local memory option for ADK. The implementation is well-structured, featuring optional FTS5 full-text search with a LIKE fallback, idempotent session upserts, and a configurable extraction mechanism. The changes are accompanied by clear documentation and a good set of unit tests covering key functionalities like idempotency, updates, persistence, and FTS usage. My feedback focuses on improving the robustness of data parsing and enhancing code conciseness in a helper function.

[gemini-code-assist]

The logic for extracting text parts from an event can be made more concise by using a list comprehension with an assignment expression (walrus operator :=). This improves readability by reducing nesting and making the intent clearer.

  parts = [
      text
      for part in event.content.parts
      if part.text and not getattr(part, "thought", False)
      if (text := part.text.replace("\n", " ").strip())
  ]

[gemini-code-assist]

The json.loads calls could fail if the data in metadata_json or extracted_json is corrupted, which would cause the entire search request to fail. To make this more robust, consider wrapping them in try...except json.JSONDecodeError blocks to gracefully handle potential data corruption.

  if row["metadata_json"]:
    try:
      metadata["metadata"] = json.loads(row["metadata_json"])
    except json.JSONDecodeError:
      # Consider logging a warning about data corruption.
      pass
  if row["extracted_json"]:
    try:
      metadata["extracted"] = json.loads(row["extracted_json"])
    except json.JSONDecodeError:
      # Consider logging a warning about data corruption.
      pass

[summerx96]

Addressed this: wrapped metadata_json/extracted_json loads in try/except JSONDecodeError with warning logs so corrupted rows don't break search.