- Add extract_conversation_patterns method to PatternExtractor class
- Extract all pattern types (topic, sentiment, interaction, temporal, style)
- Calculate overall confidence score across all pattern types
- Close personality learning pipeline integration gap
- Implement PersonalityAdaptation class with time-weighted learning and stability controls
- Integrate PersonalityLearner with MemoryManager and export system
- Create memory-integrated personality system in src/personality.py
- Add core personality protection while enabling adaptive learning
- Close personality learning integration gap from verification report
- Added search_by_keyword method for keyword-based search functionality
- Supports FTS (Full-Text Search) when available, falls back to LIKE queries
- Includes helper methods _check_fts_available, _search_with_fts, _search_with_like
- Fixed schema to separate vector and metadata tables for sqlite-vec compatibility
- Returns properly formatted results compatible with SemanticSearch.hybrid_search
- Handles multiple keywords with AND/OR logic and relevance scoring
- Enhanced _calculate_topic_relevance with conversation metadata support
- Added metadata-based topic boosts for primary topics and engagement
- Incorporated temporal patterns for recent activity preference
- Updated prioritize_by_topic to use get_conversation_metadata
- Enhanced get_topic_summary with comprehensive metadata insights
- Added related conversation context and engagement metrics
- Maintained backward compatibility with existing functionality
- Created src/memory/personality/__init__.py module structure
- Implemented PatternExtractor class with multi-dimensional analysis:
- Topics: Track frequently discussed subjects and user interests
- Sentiment: Analyze emotional tone and sentiment patterns
- Interaction: Response times, question asking, information sharing
- Temporal: Communication style by time of day/week
- Response styles: Formality level, verbosity, emoji/humor use
- Pattern extraction methods for all dimensions with confidence scoring
- Lightweight analysis techniques to avoid computational overhead
- Pattern validation with stability tracking and outlier detection
- Completed Task 2: Context-aware and timeline search
- ContextAwareSearch class with topic classification and result prioritization
- TimelineSearch class with date-range filtering and temporal proximity
- Enhanced MemoryManager with unified search interface
- Supports semantic, keyword, context-aware, timeline, and hybrid search
- Added search result dataclasses with relevance scoring
- Integrated all search strategies into MemoryManager.search() method
All search modes operational:
- Semantic search with sentence-transformers embeddings
- Context-aware search with topic-based prioritization
- Timeline search with date filtering and recency weighting
- Hybrid search combining multiple strategies
Search results include conversation context and relevance scoring as required.
- Added sentence-transformers to requirements.txt for semantic embeddings
- Created src/memory/retrieval/ module with search capabilities
- Implemented SemanticSearch class with embedding generation and vector similarity
- Added SearchResult and SearchQuery dataclasses for structured search results
- Included hybrid search combining semantic and keyword matching
- Added conversation indexing for semantic search
- Followed lazy loading pattern for embedding model performance
Files created:
- src/memory/retrieval/__init__.py
- src/memory/retrieval/search_types.py
- src/memory/retrieval/semantic_search.py
- Updated src/memory/__init__.py with enhanced MemoryManager
Note: sentence-transformers installation requires proper venv setup in production
