docs(04): create gap closure plans for memory and context management

Phase 04: Memory & Context Management
- 3 gap closure plans to address verification issues
- 04-05: Personality learning integration (PersonalityAdaptation, MemoryManager integration, src/personality.py)
- 04-06: Vector Store missing methods (search_by_keyword, store_embeddings)
- 04-07: Context-aware search metadata integration (get_conversation_metadata)
- All gaps from verification report addressed
- Updated roadmap to reflect 7 total plans

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

@@ -0,0 +1,161 @@
+---
+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"
+---
+
+<objective>
+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
+</objective>
+
+<execution_context>
+@~/.opencode/get-shit-done/workflows/execute-plan.md
+@~/.opencode/get-shit-done/templates/summary.md
+</execution_context>
+
+<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
+</context>
+
+<tasks>
+
+<task type="auto">
+  <name>Task 1: Implement search_by_keyword method in VectorStore</name>
+  <files>src/memory/storage/vector_store.py</files>
+  <action>
+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.
+  </action>
+  <verify>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')"</verify>
+  <done>VectorStore.search_by_keyword method provides keyword-based search functionality</done>
+</task>
+
+<task type="auto">
+  <name>Task 2: Implement store_embeddings method in VectorStore</name>
+  <files>src/memory/storage/vector_store.py</files>
+  <action>
+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.
+  </action>
+  <verify>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}')"</verify>
+  <done>VectorStore.store_embeddings method provides batch embedding storage functionality</done>
+</task>
+
+</tasks>
+
+<verification>
+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
+</verification>
+
+<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>
+
+<output>
+After completion, create `.planning/phases/04-memory-context-management/04-06-SUMMARY.md`
+</output>