Commit Graph

4 Commits

Author SHA1 Message Date
Giancarmine Salucci
16436bfab2 fix(FEEDBACK-0001): complete iteration 0 - harden context search 2026-03-27 01:25:46 +01:00
Giancarmine Salucci
9519a66cef test(embeddings): fix 6 remaining test failures
- Fix schema.test.ts: use Unix timestamp integers instead of Date objects for snippet_embeddings.createdAt
- Fix embedding.service.test.ts: use 'local-default' profile instead of non-existent 'test-profile', remove require() calls and use proper ESM imports
- Fix hybrid.search.service.test.ts: update VectorSearch.vectorSearch() calls to use options object instead of positional parameters, remove manual FTS insert (triggers handle it automatically)
- Fix migration 0002: improve SQL formatting with line breaks after statement-breakpoint comments

All 459 tests now passing (18 skipped).
2026-03-25 19:41:24 +01:00
Giancarmine Salucci
169df4d984 feat(TRUEREF-0020): add embedding profiles, default local embeddings, and version-scoped semantic retrieval
- Add embedding_profiles table with provider registry pattern
- Install @xenova/transformers as runtime dependency
- Update snippet_embeddings with composite PK (snippet_id, profile_id)
- Seed default local profile using Xenova/all-MiniLM-L6-v2
- Add provider registry (local-transformers, openai-compatible)
- Update EmbeddingService to persist and retrieve by profileId
- Add version-scoped VectorSearch with optional versionId filtering
- Add searchMode (auto|keyword|semantic|hybrid) to HybridSearchService
- Update API /context route to load active profile, support searchMode/alpha params
- Extend MCP query-docs tool with searchMode and alpha parameters
- Update settings API to work with embedding_profiles table
- Add comprehensive test coverage for profiles, registry, version scoping

Status: 445/451 tests passing, core feature complete
2026-03-25 19:16:37 +01:00
Giancarmine Salucci
d3d577a2e2 feat(TRUEREF-0008): implement hybrid semantic search with RRF
- Cosine similarity vector search over stored embeddings
- Reciprocal Rank Fusion (K=60) combining FTS5 + vector rankings
- Configurable alpha weight between keyword and semantic search
- Graceful degradation to FTS5-only when no embedding provider configured

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-23 09:06:25 +01:00