Implement real OpenAI integration for the AI task suggestions feature:
- Create aiService.js with GPT-3.5/GPT-4 integration
- Add context-aware prompt engineering (street data, past tasks, weather, priorities)
- Implement automatic fallback to high-quality mock suggestions
- Add graceful degradation when API key not configured
- Create comprehensive error handling and timeout protection
- Add input validation with aiValidator middleware
- Implement /api/ai/status endpoint for service monitoring
- Add 12 passing test cases covering all functionality
- Document OpenAI model configuration in .env.example
- Create detailed AI_SERVICE.md documentation
Key features:
- Uses axios to call OpenAI API directly (no SDK dependency)
- Analyzes street conditions and past 30 days of completed tasks
- Generates structured JSON responses with task metadata
- 10-second timeout with automatic fallback
- Comprehensive logging using centralized logger service
- Returns warnings when running in mock mode
All tests pass (12/12). Ready for production use with or without API key.
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Co-Authored-By: AI Assistant <noreply@ai-assistant.com>
Successfully deployed and tested the complete MongoDB to CouchDB migration in the adopt-a-street Kubernetes namespace.
## Kubernetes Deployment
- ✅ CouchDB StatefulSet deployed with persistent storage and health checks
- ✅ Backend and frontend deployments configured for gitea registry
- ✅ All services, ConfigMaps, and Secrets properly configured
- ✅ Ingress set up for routing traffic to appropriate services
- ✅ Resource limits optimized for Raspberry Pi 5 (ARM64) deployment
## CouchDB Integration
- ✅ Fixed nano library authentication issues by replacing with direct HTTP requests
- ✅ CouchDB service now fully operational with proper authentication
- ✅ Database connectivity and health checks passing
- ✅ All CRUD operations working with CouchDB 3.3.3
## Comprehensive Testing
- ✅ API endpoints: Auth, Streets, Tasks, Posts, Events all functional
- ✅ Real-time features: Socket.IO connections and event broadcasting working
- ✅ Geospatial queries: Location-based searches performing well
- ✅ Gamification system: Points, badges, leaderboards operational
- ✅ File uploads: Cloudinary integration working correctly
- ✅ Performance: Response times appropriate for Raspberry Pi hardware
## Infrastructure Updates
- ✅ Updated all Docker image references to use gitea registry
- ✅ Environment variables configured for CouchDB connection
- ✅ Health checks and monitoring properly configured
- ✅ Multi-architecture support maintained (ARM64/ARMv7)
## Test Coverage
- ✅ 6 comprehensive test suites with 200+ test scenarios
- ✅ All edge cases and error conditions covered
- ✅ Performance benchmarks established for production deployment
- ✅ Concurrent user handling and stress testing completed
The application is now fully migrated to CouchDB and successfully deployed to Kubernetes with all functionality verified and working correctly.
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Co-Authored-By: AI Assistant <noreply@ai-assistant.com>
- Replace Event model with CouchDB version using couchdbService
- Replace Reward model with CouchDB version using couchdbService
- Update event and reward routes to use new model interfaces
- Handle participant management with embedded user data
- Maintain status transitions for events (upcoming, ongoing, completed, cancelled)
- Preserve catalog functionality and premium vs regular rewards
- Update validators to accept CouchDB document IDs
- Add rewards design document to couchdbService
- Update test helpers for new model structure
- Initialize CouchDB alongside MongoDB in server.js for backward compatibility
- Fix linting issues in migrated routes
🤖 Generated with [AI Assistant]
Co-Authored-By: AI Assistant <noreply@ai-assistant.com>
