Mai/.planning/phases/04-memory-context-management/04-06-PLAN.md

phase, plan, type, wave, depends_on, files_modified, autonomous, gap_closure, must_haves

phase	plan	type	wave	depends_on	files_modified	autonomous	gap_closure	must_haves
04-memory-context-management	06	execute	1	04-01	src/memory/storage/vector_store.py	true	true	truths artifacts key_links User can search conversations by semantic meaning path provides contains contains src/memory/storage/vector_store.py Vector storage and retrieval with sqlite-vec search_by_keyword method store_embeddings method from to via pattern src/memory/retrieval/semantic_search.py src/memory/storage/vector_store.py vector similarity search operations vector_store.search_by_keyword from to via pattern src/memory/retrieval/semantic_search.py src/memory/storage/vector_store.py embedding storage operations 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

<execution_context> @/.opencode/get-shit-done/workflows/execute-plan.md @/.opencode/get-shit-done/templates/summary.md </execution_context>

@.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

<success_criteria>

• 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 </success_criteria>

After completion, create `.planning/phases/04-memory-context-management/04-06-SUMMARY.md`