feat(04-02): create semantic search with embedding-based retrieval

- 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

requirements.txt

@@ -5,4 +5,7 @@ pyyaml>=6.0
 gpu-tracker>=5.0.1
 bandit>=1.7.7
 semgrep>=1.99
-docker>=7.0.0
+docker>=7.0.0
+sqlite-vec>=0.1.0
+numpy>=1.24.0
+sentence-transformers>=2.2.2