5d348526de
Implemented three new API endpoints for knowledge graph visualization: 1. GET /api/knowledge/graph - Full knowledge graph - Returns all entries and links with optional filtering - Supports filtering by tags, status, and node count limit - Includes orphan detection (entries with no links) 2. GET /api/knowledge/graph/stats - Graph statistics - Total entries and links counts - Orphan entries detection - Average links per entry - Top 10 most connected entries - Tag distribution across entries 3. GET /api/knowledge/graph/:slug - Entry-centered subgraph - Returns graph centered on specific entry - Supports depth parameter (1-5) for traversal distance - Includes all connected nodes up to specified depth New Files: - apps/api/src/knowledge/graph.controller.ts - apps/api/src/knowledge/graph.controller.spec.ts Modified Files: - apps/api/src/knowledge/dto/graph-query.dto.ts (added GraphFilterDto) - apps/api/src/knowledge/entities/graph.entity.ts (extended with new types) - apps/api/src/knowledge/services/graph.service.ts (added new methods) - apps/api/src/knowledge/services/graph.service.spec.ts (added tests) - apps/api/src/knowledge/knowledge.module.ts (registered controller) - apps/api/src/knowledge/dto/index.ts (exported new DTOs) - docs/scratchpads/71-graph-data-api.md (implementation notes) Test Coverage: 21 tests (all passing) - 14 service tests including orphan detection, filtering, statistics - 7 controller tests for all three endpoints Follows TDD principles with tests written before implementation. All code quality gates passed (lint, typecheck, tests). Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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2.1 KiB
Issue #70: [KNOW-018] Semantic Search API
Objective
Implement semantic (vector) search endpoint that uses embeddings generated by issue #69 to enable natural language search over knowledge entries.
Approach
- Review existing embedding schema and pgvector setup
- Review OllamaEmbeddingService from issue #69
- Create DTOs for semantic search request/response
- Write tests first (TDD)
- Implement semantic search in SearchService using pgvector cosine similarity
- Create controller endpoint POST /api/knowledge/search/semantic
- Add configurable similarity threshold
- Test with real queries
- Run quality checks and code review
Progress
- Create scratchpad
- Review existing code (embedding schema, OllamaEmbeddingService)
- Add similarity threshold environment variable
- Write tests (TDD - RED)
- Update SearchService to use OllamaEmbeddingService instead of OpenAI (TDD - GREEN)
- Update hybridSearch to use OllamaEmbeddingService
- Update test files to include OllamaEmbeddingService mocks
- All tests passing
- Type check and build successful
- Run code review (quality gates passed)
- Run QA checks (prettier, lint, typecheck all passed)
- Commit changes
- Close issue
Testing
- Unit tests for SearchService.semanticSearch()
- Controller tests for POST /api/knowledge/search/semantic
- Integration tests with real embeddings
- Target: 85%+ coverage
Notes
- Use pgvector cosine similarity operator (<=>)
- Lower distance = higher similarity
- Results should include similarity scores
- Similarity threshold should be configurable via environment variable
- Reuse OllamaEmbeddingService from issue #69
Findings
- The semantic search endpoint already exists in search.controller.ts (line 111)
- The SearchService already has semanticSearch() method (line 449)
- BUT: It currently uses OpenAI-based EmbeddingService instead of OllamaEmbeddingService
- Need to update SearchService to inject and use OllamaEmbeddingService
- Need to add configurable similarity threshold
- Controller endpoint already properly configured with guards and permissions