Replace inline type annotations with proper class-validator DTOs for the
semantic and hybrid search endpoints. Adds SemanticSearchBodyDto,
HybridSearchBodyDto (query: @IsString @MaxLength(500), status:
@IsOptional @IsEnum(EntryStatus)), and SemanticSearchQueryDto (page/limit
with @IsInt @Min/@Max validation). Includes 22 new tests covering DTO
validation edge cases and controller integration.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Updated semantic search to use OllamaEmbeddingService instead of OpenAI:
- Replaced EmbeddingService with OllamaEmbeddingService in SearchService
- Added configurable similarity threshold (SEMANTIC_SEARCH_SIMILARITY_THRESHOLD)
- Updated both semanticSearch() and hybridSearch() methods
- Added comprehensive tests for semantic search functionality
- Updated controller documentation to reflect Ollama requirement
- All tests passing with 85%+ coverage
Related changes:
- Updated knowledge.service.versions.spec.ts to include OllamaEmbeddingService
- Added similarity threshold environment variable to .env.example
Fixes#70
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add support for filtering search results by tags in the main search endpoint.
Changes:
- Add tags parameter to SearchQueryDto (comma-separated tag slugs)
- Implement tag filtering in SearchService.search() method
- Update SQL query to join with knowledge_entry_tags when tags provided
- Entries must have ALL specified tags (AND logic)
- Add tests for tag filtering (2 controller tests, 2 service tests)
- Update endpoint documentation
- Fix non-null assertion linting error
The search endpoint now supports:
- Full-text search with ranking (ts_rank)
- Snippet generation with highlighting (ts_headline)
- Status filtering
- Tag filtering (new)
- Pagination
Example: GET /api/knowledge/search?q=api&tags=documentation,tutorial
All tests pass (25 total), type checking passes, linting passes.
Fixes#66
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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
