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>
58 lines
2.1 KiB
Markdown
58 lines
2.1 KiB
Markdown
# Issue #70: [KNOW-018] Semantic Search API
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## Objective
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Implement semantic (vector) search endpoint that uses embeddings generated by issue #69 to enable natural language search over knowledge entries.
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## Approach
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1. Review existing embedding schema and pgvector setup
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2. Review OllamaEmbeddingService from issue #69
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3. Create DTOs for semantic search request/response
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4. Write tests first (TDD)
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5. Implement semantic search in SearchService using pgvector cosine similarity
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6. Create controller endpoint POST /api/knowledge/search/semantic
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7. Add configurable similarity threshold
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8. Test with real queries
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9. Run quality checks and code review
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## Progress
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- [x] Create scratchpad
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- [x] Review existing code (embedding schema, OllamaEmbeddingService)
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- [x] Add similarity threshold environment variable
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- [x] Write tests (TDD - RED)
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- [x] Update SearchService to use OllamaEmbeddingService instead of OpenAI (TDD - GREEN)
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- [x] Update hybridSearch to use OllamaEmbeddingService
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- [x] Update test files to include OllamaEmbeddingService mocks
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- [x] All tests passing
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- [x] Type check and build successful
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- [x] Run code review (quality gates passed)
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- [x] Run QA checks (prettier, lint, typecheck all passed)
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- [x] Commit changes
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- [ ] Close issue
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## Testing
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- Unit tests for SearchService.semanticSearch()
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- Controller tests for POST /api/knowledge/search/semantic
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- Integration tests with real embeddings
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- Target: 85%+ coverage
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## Notes
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- Use pgvector cosine similarity operator (<=>)
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- Lower distance = higher similarity
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- Results should include similarity scores
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- Similarity threshold should be configurable via environment variable
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- Reuse OllamaEmbeddingService from issue #69
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## Findings
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- The semantic search endpoint already exists in search.controller.ts (line 111)
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- The SearchService already has semanticSearch() method (line 449)
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- BUT: It currently uses OpenAI-based EmbeddingService instead of OllamaEmbeddingService
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- Need to update SearchService to inject and use OllamaEmbeddingService
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- Need to add configurable similarity threshold
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- Controller endpoint already properly configured with guards and permissions
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