---
phase: 04-memory-context-management
plan: 06
type: execute
wave: 1
depends_on: ["04-01"]
files_modified: ["src/memory/storage/vector_store.py"]
autonomous: true
gap_closure: true
must_haves:
truths:
- "User can search conversations by semantic meaning"
artifacts:
- path: "src/memory/storage/vector_store.py"
provides: "Vector storage and retrieval with sqlite-vec"
contains: "search_by_keyword method"
contains: "store_embeddings method"
key_links:
- from: "src/memory/retrieval/semantic_search.py"
to: "src/memory/storage/vector_store.py"
via: "vector similarity search operations"
pattern: "vector_store\\.search_by_keyword"
- from: "src/memory/retrieval/semantic_search.py"
to: "src/memory/storage/vector_store.py"
via: "embedding storage operations"
pattern: "vector_store\\.store_embeddings"
---
Complete VectorStore implementation by adding missing search_by_keyword and store_embeddings methods that are called by SemanticSearch but not implemented.
Purpose: Close the vector store methods gap to enable full semantic search functionality
Output: Complete VectorStore with all required methods for semantic search operations
@~/.opencode/get-shit-done/workflows/execute-plan.md
@~/.opencode/get-shit-done/templates/summary.md
@.planning/phases/04-memory-context-management/04-CONTEXT.md
@.planning/phases/04-memory-context-management/04-memory-context-management-VERIFICATION.md
# Reference existing vector store implementation
@src/memory/storage/vector_store.py
# Reference semantic search that calls these methods
@src/memory/retrieval/semantic_search.py
Task 1: Implement search_by_keyword method in VectorStore
src/memory/storage/vector_store.py
Add missing search_by_keyword method to VectorStore class to close the verification gap:
1. search_by_keyword method implementation:
- search_by_keyword(self, query: str, limit: int = 10) -> List[Dict]
- Perform keyword-based search on message content using FTS if available
- Fall back to LIKE queries if FTS not enabled
- Return results in same format as vector search for consistency
2. Keyword search implementation:
- Use SQLite FTS (Full-Text Search) if virtual tables exist
- Query message_content and conversation_summary fields
- Support multiple keywords with AND/OR logic
- Rank results by keyword frequency and position
3. Integration with existing vector operations:
- Use same database connection as existing methods
- Follow existing error handling patterns
- Return results compatible with hybrid_search in SemanticSearch
- Include message_id, conversation_id, content, and relevance score
4. Performance optimizations:
- Add appropriate indexes for keyword search if missing
- Use query parameters to prevent SQL injection
- Limit result sets for performance
- Cache frequent keyword queries if beneficial
5. Method signature matching:
- Match the expected signature from semantic_search.py line 248
- Return format: List[Dict] with message_id, conversation_id, content, score
- Handle edge cases: empty queries, no results, database errors
The method should be called by SemanticSearch.hybrid_search at line 248. Verify the exact signature and return format by checking semantic_search.py before implementation.
python -c "from src.memory.storage.vector_store import VectorStore; vs = VectorStore(); result = vs.search_by_keyword('test', limit=5); print(f'search_by_keyword returned {len(result)} results')"
VectorStore.search_by_keyword method provides keyword-based search functionality
Task 2: Implement store_embeddings method in VectorStore
src/memory/storage/vector_store.py
Add missing store_embeddings method to VectorStore class to close the verification gap:
1. store_embeddings method implementation:
- store_embeddings(self, embeddings: List[Tuple[str, List[float]]]) -> bool
- Batch store multiple embeddings efficiently
- Handle conversation_id and message_id associations
- Return success/failure status
2. Embedding storage implementation:
- Use existing vec_entries virtual table from current implementation
- Insert embeddings with proper rowid mapping to messages
- Support batch inserts for performance
- Handle embedding dimension validation
3. Integration with existing storage patterns:
- Follow same database connection patterns as other methods
- Use existing error handling and transaction management
- Coordinate with sqlite_manager for message metadata
- Maintain consistency with existing vector storage
4. Method signature compatibility:
- Match expected signature from semantic_search.py line 363
- Accept list of (id, embedding) tuples
- Return boolean success indicator
- Handle partial failures gracefully
5. Performance and reliability:
- Use transactions for batch operations
- Validate embedding dimensions before insertion
- Handle database constraint violations
- Provide detailed error logging for debugging
The method should be called by SemanticSearch at line 363. Verify the exact signature and expected behavior by checking semantic_search.py before implementation. Ensure compatibility with the existing vec_entries table structure and sqlite-vec extension usage.
python -c "from src.memory.storage.vector_store import VectorStore; import numpy as np; vs = VectorStore(); test_emb = [('test_id', np.random.rand(1536).tolist())]; result = vs.store_embeddings(test_emb); print(f'store_embeddings returned: {result}')"
VectorStore.store_embeddings method provides batch embedding storage functionality
After completion, verify:
1. search_by_keyword method exists and is callable from SemanticSearch
2. store_embeddings method exists and is callable from SemanticSearch
3. Both methods follow the exact signatures expected by semantic_search.py
4. Methods integrate properly with existing VectorStore database operations
5. SemanticSearch.hybrid_search can now call these methods without errors
6. Keyword search returns properly formatted results compatible with vector search
- VectorStore missing methods gap is completely closed
- SemanticSearch can perform hybrid search combining keyword and vector search
- Methods follow existing VectorStore patterns and error handling
- Database operations are efficient and properly transactional
- Integration with semantic search is seamless and functional
- All anti-patterns related to missing method calls are resolved