# PR Description

## What problem does this solve?

This PR implements Phase 2 of the AI-Mem Orchestr8 system - the backend API service. According to the implementation plan in `thoughts/shared/plans/ai-mem-orchestr8-implementation.md`, Phase 2 establishes the core backend infrastructure needed for team collaboration and real-time synchronization of AI memory content.

The Phase 1 CLI foundation was completed successfully, and this Phase 2 work provides the API service that the CLI and future frontend will integrate with.

## What changes were made?

### User-facing changes
- **New API endpoints** available at `http://localhost:8000/docs` with interactive documentation
- **Health monitoring** endpoints (`/health`, `/ready`) for system status checks
- **Prometheus metrics** endpoint (`/metrics`) for observability
- **Complete REST API** for thoughts, teams, search, and real-time sync operations
- **CORS enabled** for frontend integration with configurable origins

### Implementation details

**FastAPI Application Structure:**
- Async FastAPI application with SQLAlchemy integration
- Comprehensive middleware setup (CORS, TrustedHost)
- Application lifecycle management with proper startup/shutdown
- Structured routing with API versioning (`/api/v1`)

**Database Models (SQLAlchemy + PostgreSQL):**
- `User` model with authentication fields and team relationships
- `Team` model with slug-based access and member management
- `Thought` model with full-text search capabilities, tags, and git integration
- `TeamMember` model for role-based access control
- Base mixins for UUID primary keys and automatic timestamps
- Comprehensive indexing for performance optimization

**API Schemas (Pydantic):**
- Request/response schemas for all entities with validation
- Search schemas supporting both fulltext and semantic search
- Team management schemas with role-based permissions
- Thought schemas with metadata and content analysis

**Core API Endpoints:**

*Health & Monitoring:*
- `GET /health` - Basic service health check
- `GET /ready` - Readiness check with database connectivity
- `GET /metrics` - Prometheus metrics for monitoring

*Thoughts API:*
- `GET /api/v1/thoughts/` - List with filtering, pagination, search
- `POST /api/v1/thoughts/` - Create new thoughts with auto-generated hashes
- `GET /api/v1/thoughts/{id}` - Retrieve specific thought
- `PUT /api/v1/thoughts/{id}` - Update thought with content analysis
- `DELETE /api/v1/thoughts/{id}` - Delete thought
- `GET /api/v1/thoughts/{id}/related` - Find related thoughts via tags

*Search Service:*
- `POST /api/v1/search/` - Full-text and semantic search with scoring
- `GET /api/v1/search/suggestions/` - Search autocomplete suggestions

*Teams Management:*
- `GET /api/v1/teams/` - List teams with member details
- `POST /api/v1/teams/` - Create team with unique slug validation
- `GET /api/v1/teams/{id}` - Team details with relationships
- `PUT /api/v1/teams/{id}` - Update team information
- `DELETE /api/v1/teams/{id}` - Soft delete team
- Full member management endpoints with role-based access
- Slug-based team lookup for friendly URLs

*WebSocket Sync (Framework):*
- Connection management for real-time team collaboration
- Event broadcasting for thought changes
- Team-based connection isolation
- Ping/pong and subscription support

**Configuration & Infrastructure:**
- Environment-based configuration with Pydantic Settings
- Security settings for JWT tokens and CORS
- Database connection pooling and async session management
- Redis integration preparation for caching
- Comprehensive dependency management with `pyproject.toml`

## How to verify it

### Automated verification
- [ ] Unit tests pass: `pytest` (not yet implemented - dev dependency available)
- [ ] Linting passes: `ruff check .` (not yet implemented - dev dependency available)  
- [ ] Type checking passes: `mypy .` (not yet implemented - dev dependency available)
- [x] FastAPI server starts successfully: `cd backend && uv run uvicorn aimem_api.main:app --host 127.0.0.1 --port 8000`
- [x] API documentation generates correctly: Visit `http://127.0.0.1:8000/docs`
- [x] Health endpoints respond correctly: `curl http://127.0.0.1:8000/health`

### Manual verification
- [x] Feature works as expected in development environment - API server runs and serves documentation
- [x] All endpoints are documented in Swagger UI with proper schemas
- [x] Database models are properly structured with relationships
- [x] CORS middleware allows frontend integration
- [ ] Database connectivity works (requires PostgreSQL setup)
- [ ] WebSocket connections establish properly (requires frontend testing)
- [ ] Search functionality works with actual data (requires database with content)

## Breaking changes

None - this is a new backend service with no existing API to break.

**Migration steps required:**
- PostgreSQL database setup (connection string in environment variables)
- Redis server setup for caching (optional for basic functionality)  
- Environment variables configuration (see `config.py` for all settings)

## Changelog entry

```
- Added: Complete FastAPI backend API service for AI-Mem system
- Added: Database models for Users, Teams, and Thoughts with PostgreSQL
- Added: REST API endpoints for CRUD operations on all entities
- Added: Search service with full-text capabilities and suggestion engine
- Added: WebSocket framework for real-time team collaboration
- Added: Health monitoring and Prometheus metrics endpoints
- Added: CORS middleware and security configuration
- Added: Comprehensive API documentation via Swagger UI
```

## Additional context

**Architecture Decisions:**
- **Async-first**: All database operations use SQLAlchemy async for high concurrency
- **PostgreSQL**: Chosen for JSONB support (metadata, tags) and full-text search
- **Pydantic v2**: Latest validation with enhanced performance
- **UUID primary keys**: For distributed system compatibility
- **Slug-based team access**: User-friendly URLs and team identification
- **Content hash tracking**: For duplicate detection and change management

**Database Schema Design:**
- Soft deletes for teams/users to preserve referential integrity
- Composite indexes for query performance (team+path, team+title)
- JSONB fields for flexible metadata and tag storage
- Git integration fields for version control alignment

**Performance Considerations:**
- Database connection pooling (20 base, 30 overflow)
- Query optimization with strategic indexes
- Pagination for all list endpoints
- Content excerpts for search results to reduce payload

**Security Features:**
- JWT token authentication framework (schemas ready)
- CORS configuration for frontend integration  
- Trusted host middleware for additional security
- Password hashing preparation with bcrypt

**API Design:**
- RESTful conventions with consistent response formats
- API versioning (`/api/v1`) for future compatibility
- Comprehensive input validation and error handling
- Search result relevance scoring and excerpts

**Testing Strategy Ready:**
- Development dependencies include pytest, pytest-asyncio, httpx
- Test database isolation patterns established
- Mock framework ready for external service testing

This implementation provides a solid foundation for Phase 3 (Web Frontend) and Phase 4 (Kubernetes deployment) as outlined in the AI-Mem implementation plan.