b9aba97086
- 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
13 lines
422 B
Python
13 lines
422 B
Python
"""
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Memory retrieval module for Mai conversation search.
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This module provides various search strategies for retrieving conversations
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including semantic search, context-aware search, and timeline-based filtering.
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"""
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from .semantic_search import SemanticSearch
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from .context_aware import ContextAwareSearch
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from .timeline_search import TimelineSearch
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__all__ = ["SemanticSearch", "ContextAwareSearch", "TimelineSearch"]
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