26543d0402
Tasks completed: 2/2 - Implemented search_by_keyword method with FTS/LIKE hybrid search - Implemented store_embeddings method with transactional batch operations - Fixed VectorStore schema for sqlite-vec extension compatibility - Resolved all missing method calls from SemanticSearch.hybrid_search SUMMARY: .planning/phases/04-memory-context-management/04-06-SUMMARY.md Updated STATE.md to reflect Phase 4 completion
109 lines
4.2 KiB
Markdown
109 lines
4.2 KiB
Markdown
---
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phase: 04-memory-context-management
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plan: 06
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subsystem: memory
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tags: sqlite-vec, vector-search, keyword-search, embeddings, storage
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# Dependency graph
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requires:
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- phase: 04-memory-context-management
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provides: Vector store infrastructure with sqlite-vec extension and metadata tables
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- phase: 04-01
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provides: Semantic search implementation that calls missing methods
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provides:
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- Complete VectorStore implementation with search_by_keyword and store_embeddings methods
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- Keyword-based search functionality with FTS and LIKE fallback support
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- Batch embedding storage with transactional safety and error handling
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- Vector store compatibility with SemanticSearch.hybrid_search operations
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affects:
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- 04-memory-context-management
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- semantic search functionality
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- conversation memory indexing and retrieval
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# Tech tracking
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tech-stack:
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added: sqlite-vec extension, batch transaction patterns, error handling
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patterns: hybrid FTS/LIKE search, separated vector/metadata tables, transactional batch operations
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key-files:
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created: []
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modified: src/memory/storage/vector_store.py
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key-decisions:
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- "Separated vector and metadata tables for sqlite-vec compatibility"
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- "Implemented hybrid FTS/LIKE search for keyword queries"
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- "Added transactional batch operations for embedding storage"
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- "Fixed Row object handling throughout search methods"
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patterns-established:
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- "Pattern 1: Hybrid search with FTS priority and LIKE fallback"
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- "Pattern 2: Transactional batch operations with partial failure handling"
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- "Pattern 3: Schema separation for vector extension compatibility"
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# Metrics
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duration: 19min
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completed: 2026-01-28
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---
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# Phase 4 Plan 6: VectorStore Gap Closure Summary
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**Implemented missing search_by_keyword and store_embeddings methods in VectorStore to enable full semantic search functionality**
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## Performance
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- **Duration:** 19 min
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- **Started:** 2026-01-28T18:10:03Z
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- **Completed:** 2026-01-28T18:29:27Z
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- **Tasks:** 2
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- **Files modified:** 1
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## Accomplishments
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- Implemented search_by_keyword method with FTS and LIKE fallback support
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- Implemented store_embeddings method for batch embedding storage with transactions
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- Fixed VectorStore schema to work with sqlite-vec extension requirements
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- Resolved all missing method calls from SemanticSearch.hybrid_search
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- Added comprehensive error handling and validation for both methods
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## Task Commits
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Each task was committed atomically:
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1. **Task 1: Implement search_by_keyword method in VectorStore** - `0bf6266` (feat)
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2. **Task 2: Implement store_embeddings method in VectorStore** - `cc24b54` (feat)
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**Plan metadata:** None created (methods implemented in same file)
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## Files Created/Modified
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- `src/memory/storage/vector_store.py` - Added search_by_keyword and store_embeddings methods, updated schema for sqlite-vec compatibility
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## Decisions Made
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- Separated vector and metadata tables to work with sqlite-vec extension constraints
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- Implemented hybrid FTS/LIKE search to provide robust keyword search capabilities
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- Added transactional batch operations with partial failure handling for reliability
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- Fixed Row object handling throughout all search methods for consistency
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## Deviations from Plan
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None - plan executed exactly as written.
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## Issues Encountered
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- **sqlite-vec extension loading:** Initial attempts to load extension failed due to path issues
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- **Resolution:** Used sqlite_vec.loadable_path() to get correct extension path
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- **Schema compatibility:** Original vec0 virtual table definition included unsupported column types
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- **Resolution:** Separated vector storage from metadata tables for proper sqlite-vec compatibility
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- **Row object handling:** Mixed tuple/dict row handling caused runtime errors
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- **Resolution:** Standardized on dictionary-style access for sqlite3.Row objects throughout all methods
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## User Setup Required
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None - no external service configuration required.
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## Next Phase Readiness
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- VectorStore now has all required methods for SemanticSearch operations
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- Hybrid search combining keyword and vector similarity is fully functional
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- Memory system ready for conversation indexing and retrieval operations
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- All anti-patterns related to missing method calls are resolved
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---
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*Phase: 04-memory-context-management*
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*Completed: 2026-01-28* |