Mai/.planning/phases/04-memory-context-management/04-07-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	07	execute	1	04-01	src/memory/storage/sqlite_manager.py	true	true	truths artifacts key_links Context-aware search prioritizes current topic discussions path provides contains src/memory/storage/sqlite_manager.py SQLite database operations and schema management get_conversation_metadata method from to via pattern src/memory/retrieval/context_aware.py src/memory/storage/sqlite_manager.py conversation metadata for topic analysis sqlite_manager.get_conversation_metadata

Complete SQLiteManager by adding missing get_conversation_metadata method to enable ContextAwareSearch topic analysis functionality.

Purpose: Close the metadata integration gap to enable context-aware search prioritization Output: Complete SQLiteManager with metadata access for topic-based search enhancement

<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 sqlite manager implementation

@src/memory/storage/sqlite_manager.py

Reference context aware search that needs this method

@src/memory/retrieval/context_aware.py

Task 1: Implement get_conversation_metadata method in SQLiteManager src/memory/storage/sqlite_manager.py Add missing get_conversation_metadata method to SQLiteManager class to close the verification gap:

1. get_conversation_metadata method implementation:

   • get_conversation_metadata(self, conversation_ids: List[str]) -> Dict[str, Dict]
   • Retrieve comprehensive metadata for specified conversations
   • Include topics, timestamps, message counts, user engagement metrics
   • Return structured data suitable for topic analysis

2. Metadata fields to include:

   • Conversation metadata: title, summary, created_at, updated_at
   • Topic information: main_topics, topic_frequency, topic_sentiment
   • Engagement metrics: message_count, user_message_ratio, response_times
   • Temporal data: time_of_day patterns, day_of_week patterns
   • Context clues: related_conversations, conversation_chain_position

3. Database queries for metadata:

   • Query conversations table for basic metadata
   • Aggregate message data for engagement metrics
   • Join with message metadata if available
   • Calculate topic statistics from existing topic fields
   • Use existing indexes for efficient querying

4. Integration with existing SQLiteManager patterns:

   • Follow same connection and cursor management
   • Use existing error handling and transaction patterns
   • Return data in formats compatible with existing methods
   • Handle missing or incomplete data gracefully

5. Performance optimizations:

   • Batch queries when multiple conversation_ids provided
   • Use appropriate indexes for metadata fields
   • Cache frequently accessed metadata
   • Limit result size for large conversation sets

The method should support the needs identified in ContextAwareSearch for topic analysis. Check context_aware.py to understand the specific metadata requirements and expected return format. python -c "from src.memory.storage.sqlite_manager import SQLiteManager; sm = SQLiteManager(); result = sm.get_conversation_metadata(['test_id']); print(f'get_conversation_metadata returned: {type(result)} with keys: {list(result.keys()) if result else "None"}')" SQLiteManager.get_conversation_metadata method provides comprehensive conversation metadata

Task 2: Integrate metadata access in ContextAwareSearch src/memory/retrieval/context_aware.py Update ContextAwareSearch to use the new get_conversation_metadata method for proper topic analysis:

1. Import and use sqlite_manager.get_conversation_metadata:

   • Update imports if needed to access sqlite_manager
   • Replace any mock or placeholder metadata calls with real method
   • Integrate metadata results into topic analysis algorithms
   • Handle missing metadata gracefully

2. Topic analysis enhancement:

   • Use real conversation metadata for topic relevance scoring
   • Incorporate temporal patterns and engagement metrics
   • Weight recent conversations appropriately in topic matching
   • Use conversation chains and relationships for context

3. Context-aware search improvements:

   • Enhance topic analysis with real metadata
   • Improve current topic discussion prioritization
   • Better handle multi-topic conversations
   • More accurate context relevance scoring

4. Error handling and fallbacks:

   • Handle cases where metadata is incomplete or missing
   • Provide fallback to basic topic analysis
   • Log metadata access issues for debugging
   • Maintain search functionality even with metadata failures

5. Integration verification:

   • Ensure ContextAwareSearch calls sqlite_manager.get_conversation_metadata
   • Verify metadata is properly used in topic analysis
   • Test with various conversation metadata scenarios
   • Confirm search results improve with real metadata

Update the existing ContextAwareSearch implementation to leverage the new metadata capability while maintaining backward compatibility and handling edge cases appropriately. python -c "from src.memory.retrieval.context_aware import ContextAwareSearch; cas = ContextAwareSearch(); print('ContextAwareSearch ready for metadata integration')" ContextAwareSearch integrates with SQLiteManager metadata for enhanced topic analysis

After completion, verify: 1. get_conversation_metadata method exists in SQLiteManager and is callable 2. Method returns comprehensive metadata suitable for topic analysis 3. ContextAwareSearch successfully calls and uses the metadata method 4. Topic analysis is enhanced with real conversation metadata 5. Context-aware search results are more accurate with metadata integration 6. No broken method calls or missing imports remain

<success_criteria>

• Metadata integration gap is completely closed
• ContextAwareSearch can access conversation metadata for topic analysis
• Topic analysis is enhanced with real engagement and temporal data
• Current topic discussion prioritization works with real metadata
• Integration follows existing patterns and maintains performance
• All verification issues related to metadata access are resolved </success_criteria>

After completion, create `.planning/phases/04-memory-context-management/04-07-SUMMARY.md`