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| # Analytics Implementation Tasks | |
| ## Project Overview | |
| Implement basic analytics and user session tracking for the Atlas chat application in four phases. | |
| --- | |
| ## Phase 1: Foundation Setup | |
| **Goal**: Set up database connection and basic infrastructure | |
| ### Tasks | |
| - [x] **1.1** Add MongoDB dependencies to requirements.txt | |
| - Add `motor` (async MongoDB driver) | |
| - Add `python-dotenv` (already exists) | |
| - [x] **1.2** Set up MongoDB Atlas free account | |
| - Create cluster and database | |
| - Get connection string | |
| - Add to `.env` file | |
| - [x] **1.3** Create analytics module structure | |
| - Create `analytics/` directory | |
| - Create `analytics/__init__.py` | |
| - Create `analytics/database.py` with connection logic | |
| - [x] **1.4** Test database connection | |
| - Simple connection test function | |
| - Add to startup event in app.py | |
| **Deliverables**: Working MongoDB connection, basic module structure | |
| --- | |
| ## Phase 2: Data Collection | |
| **Goal**: Implement session tracking and message analytics | |
| ### Tasks | |
| - [x] **2.1** Create data models | |
| - Create `analytics/models.py` | |
| - Define Session and Message data classes | |
| - Add validation and helper methods | |
| - [x] **2.2** Implement session management | |
| - Create `analytics/collectors.py` | |
| - Session creation and tracking functions | |
| - Session ID generation and management | |
| - [x] **2.3** Add message tracking | |
| - Message analytics collection function | |
| - Performance timing decorators | |
| - Integration with chat endpoint | |
| - [x] **2.4** Update app.py for analytics | |
| - Import analytics functions | |
| - Add analytics calls to chat endpoint | |
| - Add session middleware | |
| **Deliverables**: Automatic data collection on all chat interactions | |
| --- | |
| ## Phase 3: Analytics Endpoints | |
| **Goal**: Create endpoints to view collected analytics data | |
| ### Tasks | |
| - [x] **3.1** Create analytics dashboard module | |
| - Create `analytics/dashboard.py` | |
| - Basic statistics calculation functions | |
| - Data aggregation utilities | |
| - [x] **3.2** Add basic stats endpoint | |
| - `GET /analytics/stats` endpoint | |
| - Return JSON with key metrics: | |
| - Total messages today/week | |
| - Average response time | |
| - Search usage percentage | |
| - Active sessions | |
| - [x] **3.3** Add simple HTML dashboard | |
| - `GET /analytics/dashboard` endpoint | |
| - Basic HTML template with charts | |
| - Real-time statistics display | |
| - [ ] **3.4** Add data export endpoint | |
| - `GET /analytics/export` endpoint | |
| - CSV/JSON export functionality | |
| - Date range filtering | |
| **Deliverables**: Functional analytics dashboard and API endpoints | |
| --- | |
| ## Phase 4: Enhancement & Polish | |
| **Goal**: Add advanced features and polish the implementation | |
| ### Tasks | |
| - [ ] **4.1** Add search analytics | |
| - Track search-specific metrics | |
| - Search performance analysis | |
| - Popular search terms (anonymized) | |
| - [ ] **4.2** Implement data retention | |
| - Automatic cleanup of old data | |
| - Configurable retention periods | |
| - Database optimization | |
| - [ ] **4.3** Add error tracking | |
| - Error analytics collection | |
| - Error rate monitoring | |
| - System health metrics | |
| - [ ] **4.4** Performance optimization | |
| - Database indexing | |
| - Async optimization | |
| - Memory usage monitoring | |
| - [ ] **4.5** Documentation and testing | |
| - Update README with analytics features | |
| - Add basic tests for analytics functions | |
| - API documentation updates | |
| **Deliverables**: Production-ready analytics system with monitoring | |
| --- | |
| ## Optional Enhancements | |
| *These can be added after core implementation* | |
| ### Advanced Features | |
| - [ ] **Real-time WebSocket dashboard** | |
| - [ ] **Email/Slack alerts for errors** | |
| - [ ] **Advanced data visualization** | |
| - [ ] **User behavior analysis** | |
| - [ ] **A/B testing framework** | |
| ### Integration Features | |
| - [ ] **Grafana dashboard integration** | |
| - [ ] **Prometheus metrics export** | |
| - [ ] **Log aggregation (ELK stack)** | |
| - [ ] **API rate limiting based on usage** | |
| --- | |
| ## Success Criteria | |
| ### Phase 1 Success | |
| - ✅ MongoDB connection established | |
| - ✅ Basic module structure created | |
| - ✅ Connection test passing | |
| ### Phase 2 Success | |
| - ✅ All chat messages tracked in database | |
| - ✅ Session management working | |
| - ✅ Performance metrics collected | |
| ### Phase 3 Success | |
| - ✅ Analytics dashboard accessible | |
| - ✅ Key metrics displayed correctly | |
| - ✅ Data export functionality working | |
| - ✅ MongoDB integration verified | |
| - ✅ Real-time data collection confirmed | |
| ### Phase 4 Success | |
| - ✅ Search analytics implemented | |
| - ✅ Data retention policies active | |
| - ✅ System monitoring in place | |
| --- | |
| ## Timeline Estimate | |
| - **Phase 1**: 2-3 hours | |
| - **Phase 2**: 4-5 hours | |
| - **Phase 3**: 3-4 hours | |
| - **Phase 4**: 3-4 hours | |
| **Total**: ~12-16 hours for complete implementation | |
| --- | |
| ## Dependencies | |
| - MongoDB Atlas account (free tier) | |
| - Python packages: `motor`, `python-dotenv` | |
| - Basic HTML/CSS knowledge for dashboard | |
| - Understanding of async Python programming |