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Google Cloud Conversational Analytics API Integration

Repository for integrating Google Cloud Conversational Analytics API with stevei101 sub-repositories

This repository serves as the integration hub for leveraging Google Cloud's Conversational Analytics API (CA API) across the stevei101 organization's modular building blocks: agentnav, prompt-vault, and cursor-ide.

Overview

The Google Cloud Conversational Analytics API enables intelligent, data-driven conversational interfaces by combining:

  • Gemini AI - Advanced language understanding and generation
  • BigQuery - Scalable data storage and analytics
  • Looker - Semantic modeling and data exploration

This integration enhances the capabilities of our sub-repositories with conversational analytics, making them more intelligent, context-aware, and data-driven.

Related Issues

Resources

Integration Targets

1. Agentnav Integration

Current State: Multi-agent knowledge explorer for documents and codebases

Enhanced Capabilities:

  • Conversational navigation and route queries
  • Context-aware assistance based on data patterns
  • Intelligent document analysis using BigQuery insights
  • Natural language queries for codebase exploration

Use Cases:

  • "Show me all functions related to authentication"
  • "What are the most frequently modified files?"
  • "Analyze the relationship between these components"

2. Prompt-Vault Integration

Current State: Cursor IDE prompt storage and management

Enhanced Capabilities:

  • Intelligent prompt categorization using conversational analytics
  • Semantic search based on user intent and usage patterns
  • Prompt generation suggestions based on BigQuery analytics
  • Usage pattern analysis via Looker insights

Use Cases:

  • "Find prompts similar to this one"
  • "Suggest prompts for debugging Python code"
  • "Show me the most effective prompts for code generation"

3. Cursor-IDE Integration

Current State: Cursor IDE prompt storage utility

Enhanced Capabilities:

  • Context-aware code suggestions based on project data
  • Intelligent debugging assistance using analytics
  • Code performance insights from BigQuery data
  • Natural language queries for code exploration

Use Cases:

  • "Why is this function slow?"
  • "Show me similar code patterns in this codebase"
  • "What are the common error patterns in this project?"

Architecture

┌─────────────────────────────────────────────────────────┐
│         Google Cloud Conversational Analytics API       │
│                                                          │
│  ┌──────────┐    ┌──────────┐    ┌──────────┐         │
│  │  Gemini  │    │ BigQuery │    │  Looker  │         │
│  │    AI    │◄──►│   Data   │◄──►│ Semantic │         │
│  └──────────┘    └──────────┘    └──────────┘         │
└─────────────────────────────────────────────────────────┘
                        ▲
                        │
        ┌───────────────┼───────────────┐
        │               │               │
┌───────▼──────┐ ┌─────▼──────┐ ┌──────▼──────┐
│   agentnav   │ │prompt-vault│ │ cursor-ide  │
│              │ │            │ │             │
│ Multi-agent  │ │ Prompt     │ │ IDE        │
│ Knowledge    │ │ Storage    │ │ Integration│
│ Explorer     │ │            │ │             │
└──────────────┘ └────────────┘ └─────────────┘

Implementation Plan

Phase 1: Research & Analysis ✅

  • Review Google Cloud Conversational Analytics API documentation
  • Analyze video tutorial and examples
  • Identify integration points for each sub-repository
  • Define use cases and benefits

Phase 2: Foundation Setup

  • Set up Google Cloud Project with required APIs
  • Configure BigQuery datasets and tables
  • Set up Looker semantic models
  • Configure authentication and authorization
  • Create initial CA API client library

Phase 3: Integration Development

  • Agentnav Integration
    • Implement conversational query interface
    • Connect to BigQuery for data insights
    • Add context-aware navigation
  • Prompt-Vault Integration
    • Implement semantic prompt search
    • Add usage analytics via BigQuery
    • Create prompt recommendation engine
  • Cursor-IDE Integration
    • Add conversational code assistance
    • Implement performance analytics
    • Create debugging insights interface

Phase 4: Testing & Validation

  • Unit tests for CA API integration
  • Integration tests with each sub-repository
  • End-to-end testing with real data
  • Performance testing and optimization

Phase 5: Documentation & Deployment

  • Complete API documentation
  • Integration guides for each sub-repository
  • Deployment guides and runbooks
  • User documentation and examples

Technical Requirements

Prerequisites

  • Google Cloud Project with billing enabled
  • APIs enabled:
    • Gemini API
    • BigQuery API
    • Looker API (if using Looker)
  • Authentication configured (Service Account or OAuth)
  • Python 3.8+ or Node.js 16+ (depending on implementation)

Dependencies

Python:

google-cloud-bigquery
google-generativeai
google-cloud-aiplatform

TypeScript/JavaScript:

{
  "@google-cloud/bigquery": "^7.0.0",
  "@google/generative-ai": "^0.2.0"
}

Getting Started

1. Clone the Repository

git clone https://github.com/stevei101/Google-Cloud-Conversational-Analytics-API.git
cd Google-Cloud-Conversational-Analytics-API

2. Set Up Google Cloud

# Authenticate with Google Cloud
gcloud auth application-default login

# Set your project
gcloud config set project YOUR_PROJECT_ID

# Enable required APIs
gcloud services enable generativelanguage.googleapis.com
gcloud services enable bigquery.googleapis.com

3. Configure Environment

# Copy example environment file
cp .env.example .env

# Edit .env and add your configuration
# GCP_PROJECT_ID=your-project-id
# GEMINI_API_KEY=your-api-key
# BIGQUERY_DATASET=your-dataset

4. Install Dependencies

Python:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Node.js:

npm install

Project Structure

Google-Cloud-Conversational-Analytics-API/
├── README.md                    # This file
├── docs/                        # Documentation
│   ├── architecture.md          # Architecture details
│   ├── integration-guides/      # Integration guides per sub-repo
│   │   ├── agentnav.md
│   │   ├── prompt-vault.md
│   │   └── cursor-ide.md
│   └── api-reference.md          # API reference
├── src/                         # Source code
│   ├── python/                  # Python implementation
│   │   ├── ca_api_client.py     # CA API client
│   │   ├── bigquery_integration.py
│   │   └── looker_integration.py
│   └── typescript/              # TypeScript implementation
│       ├── ca-api-client.ts
│       ├── bigquery-integration.ts
│       └── looker-integration.ts
├── examples/                     # Example implementations
│   ├── agentnav-example.py
│   ├── prompt-vault-example.py
│   └── cursor-ide-example.py
├── tests/                        # Test suites
│   ├── unit/
│   ├── integration/
│   └── e2e/
├── scripts/                      # Utility scripts
│   ├── setup-gcp.sh
│   └── setup-bigquery.sh
└── .github/                      # GitHub workflows
    └── workflows/
        ├── ci.yml
        └── deploy.yml

Use Cases & Benefits

Enhanced Agent Capabilities

  • Natural language interfaces for complex queries
  • Context-aware responses based on data patterns
  • Intelligent routing and navigation

Improved Data Analysis

  • Real-time insights from BigQuery
  • Semantic modeling with Looker
  • Pattern recognition and anomaly detection

Intelligent Developer Tools

  • Context-aware code suggestions
  • Performance insights and recommendations
  • Intelligent debugging assistance

Contributing

This repository is part of the stevei101 organizational repository structure. See the main stevei101 repository for contribution guidelines.

License

[Add license information]

References

Status

🚧 In Planning Phase - This repository is currently being set up. Implementation will begin after the planning phase is complete.

Maintained by: stevei101
Part of: stevei101 Organizational Repository

About

Integration repository for Google Cloud Conversational Analytics API with Antigravity IDE (with possible support for other IDE's in the future). Leverages Gemini, BigQuery, and Looker for intelligent conversational analytics.

Topics

bigquery looker typescript mcp ide gemini google-cloud-platform bun data-intelligence antigravity agentic-workflows conversational-analytics

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