Issues resolved:
- #68: pgvector Setup
* Added pgvector vector index migration for knowledge_embeddings
* Vector index uses HNSW algorithm with cosine distance
* Optimized for 1536-dimension OpenAI embeddings
- #69: Embedding Generation Pipeline
* Created EmbeddingService with OpenAI integration
* Automatic embedding generation on entry create/update
* Batch processing endpoint for existing entries
* Async generation to avoid blocking API responses
* Content preparation with title weighting
- #70: Semantic Search API
* POST /api/knowledge/search/semantic - pure vector search
* POST /api/knowledge/search/hybrid - RRF combined search
* POST /api/knowledge/embeddings/batch - batch generation
* Comprehensive test coverage
* Full documentation in docs/SEMANTIC_SEARCH.md
Technical details:
- Uses OpenAI text-embedding-3-small model (1536 dims)
- HNSW index for O(log n) similarity search
- Reciprocal Rank Fusion for hybrid search
- Graceful degradation when OpenAI not configured
- Async embedding generation for performance
Configuration:
- Added OPENAI_API_KEY to .env.example
- Optional feature - disabled if API key not set
- Falls back to keyword search in hybrid mode
- Add Personality model to Prisma schema with FormalityLevel enum
- Create migration and seed with 6 default personalities
- Implement CRUD API with TDD approach (97.67% coverage)
* PersonalitiesService: findAll, findOne, findDefault, create, update, remove
* PersonalitiesController: REST endpoints with auth guards
* Comprehensive test coverage (21 passing tests)
- Add Personality types to shared package
- Create frontend components:
* PersonalitySelector: dropdown for choosing personality
* PersonalityPreview: preview personality style and system prompt
* PersonalityForm: create/edit personalities with validation
* Settings page: manage personalities with CRUD operations
- Integrate with Ollama API:
* Support personalityId in chat endpoint
* Auto-inject system prompt from personality
* Fall back to default personality if not specified
- API client for frontend personality management
All tests passing with 97.67% backend coverage (exceeds 85% requirement)
- Create workspace listing page at /settings/workspaces
- List all user workspaces with role badges
- Create new workspace functionality
- Display member count per workspace
- Create workspace detail page at /settings/workspaces/[id]
- Workspace settings (name, ID, created date)
- Member management with role editing
- Invite member functionality
- Delete workspace (owner only)
- Add workspace components:
- WorkspaceCard: Display workspace info with role badge
- WorkspaceSettings: Edit workspace settings and delete
- MemberList: Display and manage workspace members
- InviteMember: Send invitations with role selection
- Add WorkspaceMemberWithUser type to shared package
- Follow existing app patterns for styling and structure
- Use mock data (ready for API integration)
Implements #9, #10
- Team model with workspace membership
- TeamMember model with role-based access (OWNER, ADMIN, MEMBER)
- Row-Level Security policies for tenant isolation on 19 tables
- Helper functions: current_user_id(), is_workspace_member(), is_workspace_admin()
- Developer utilities in src/lib/db-context.ts for easy RLS integration
- Comprehensive documentation in docs/design/multi-tenant-rls.md
Database migrations:
- 20260129220941_add_team_model: Adds Team and TeamMember tables
- 20260129221004_add_rls_policies: Enables RLS and creates policies
Security features:
- Complete database-level tenant isolation
- Automatic query filtering based on workspace membership
- Defense-in-depth security with application and database layers
- Performance-optimized with indexes on workspace_id
