- 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
- Created pyproject.toml with lmstudio, psutil, pydantic dependencies
- Created requirements.txt as fallback for pip install
- Created src/models/__init__.py with proper imports
- Set up PEP 518 compliant package structure
- Fixed .gitignore to allow src/models/ directory
