David Blanc Brioir
2f34125ef6
MEMORY SYSTEM ARCHITECTURE: - Weaviate-based memory storage (Thought, Message, Conversation collections) - GPU embeddings with BAAI/bge-m3 (1024-dim, RTX 4070) - 9 MCP tools for Claude Desktop integration CORE MODULES (memory/): - core/embedding_service.py: GPU embedder singleton with PyTorch - schemas/memory_schemas.py: Weaviate schema definitions - mcp/thought_tools.py: add_thought, search_thoughts, get_thought - mcp/message_tools.py: add_message, get_messages, search_messages - mcp/conversation_tools.py: get_conversation, search_conversations, list_conversations FLASK TEMPLATES: - conversation_view.html: Display single conversation with messages - conversations.html: List all conversations with search - memories.html: Browse and search thoughts FEATURES: - Semantic search across thoughts, messages, conversations - Privacy levels (private, shared, public) - Thought types (reflection, question, intuition, observation) - Conversation categories with filtering - Message ordering and role-based display DATA (as of 2026-01-08): - 102 Thoughts - 377 Messages - 12 Conversations DOCUMENTATION: - memory/README_MCP_TOOLS.md: Complete API reference and usage examples All MCP tools tested and validated (see test_memory_mcp_tools.py in archive). Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
32 lines
667 B
Python
32 lines
667 B
Python
"""
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Memory Core Module - GPU Embedding Service and Utilities.
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This module provides core functionality for the unified RAG system:
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- GPU-accelerated embeddings (RTX 4070 + PyTorch CUDA)
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- Singleton embedding service
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- Weaviate connection utilities
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Usage:
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from memory.core import get_embedder, embed_text
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# Get singleton embedder
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embedder = get_embedder()
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# Embed text
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embedding = embed_text("Hello world")
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"""
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from memory.core.embedding_service import (
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GPUEmbeddingService,
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get_embedder,
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embed_text,
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embed_texts,
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)
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__all__ = [
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"GPUEmbeddingService",
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"get_embedder",
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"embed_text",
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"embed_texts",
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]
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