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

src/memory/retrieval/__init__.py

@@ -0,0 +1,12 @@
+"""
+Memory retrieval module for Mai conversation search.
+
+This module provides various search strategies for retrieving conversations
+including semantic search, context-aware search, and timeline-based filtering.
+"""
+
+from .semantic_search import SemanticSearch
+from .context_aware import ContextAwareSearch
+from .timeline_search import TimelineSearch
+
+__all__ = ["SemanticSearch", "ContextAwareSearch", "TimelineSearch"]