feat: Add Memory system with Weaviate integration and MCP tools
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>
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memory/mcp/conversation_tools.py
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208
memory/mcp/conversation_tools.py
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"""
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Conversation MCP Tools - Handlers for conversation-related operations.
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Provides tools for searching and retrieving conversations.
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"""
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import weaviate
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from typing import Any, Dict
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from pydantic import BaseModel, Field
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from memory.core import get_embedder
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class GetConversationInput(BaseModel):
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"""Input for get_conversation tool."""
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conversation_id: str = Field(..., description="Conversation identifier")
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class SearchConversationsInput(BaseModel):
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"""Input for search_conversations tool."""
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query: str = Field(..., description="Search query text")
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limit: int = Field(default=10, ge=1, le=50, description="Maximum results")
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category_filter: str | None = Field(default=None, description="Filter by category")
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class ListConversationsInput(BaseModel):
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"""Input for list_conversations tool."""
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limit: int = Field(default=20, ge=1, le=100, description="Maximum conversations")
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category_filter: str | None = Field(default=None, description="Filter by category")
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async def get_conversation_handler(input_data: GetConversationInput) -> Dict[str, Any]:
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"""
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Get a specific conversation by ID.
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Args:
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input_data: Query parameters.
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Returns:
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Dictionary with conversation data.
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"""
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try:
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# Connect to Weaviate
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client = weaviate.connect_to_local()
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try:
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# Get collection
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collection = client.collections.get("Conversation")
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# Fetch by conversation_id
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results = collection.query.fetch_objects(
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filters=weaviate.classes.query.Filter.by_property("conversation_id").equal(input_data.conversation_id),
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limit=1,
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)
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if not results.objects:
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return {
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"success": False,
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"error": f"Conversation {input_data.conversation_id} not found",
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}
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obj = results.objects[0]
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return {
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"success": True,
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"conversation_id": obj.properties['conversation_id'],
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"category": obj.properties['category'],
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"summary": obj.properties['summary'],
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"timestamp_start": obj.properties['timestamp_start'],
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"timestamp_end": obj.properties['timestamp_end'],
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"participants": obj.properties['participants'],
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"tags": obj.properties.get('tags', []),
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"message_count": obj.properties['message_count'],
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}
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finally:
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client.close()
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except Exception as e:
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return {
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"success": False,
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"error": str(e),
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}
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async def search_conversations_handler(input_data: SearchConversationsInput) -> Dict[str, Any]:
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"""
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Search conversations using semantic similarity.
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Args:
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input_data: Search parameters.
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Returns:
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Dictionary with search results.
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"""
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try:
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# Connect to Weaviate
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client = weaviate.connect_to_local()
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try:
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# Get embedder
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embedder = get_embedder()
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# Generate query vector
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query_vector = embedder.embed_batch([input_data.query])[0]
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# Get collection
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collection = client.collections.get("Conversation")
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# Build query
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query_builder = collection.query.near_vector(
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near_vector=query_vector.tolist(),
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limit=input_data.limit,
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)
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# Apply category filter if provided
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if input_data.category_filter:
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query_builder = query_builder.where(
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weaviate.classes.query.Filter.by_property("category").equal(input_data.category_filter)
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)
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# Execute search
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results = query_builder.objects
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# Format results
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conversations = []
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for obj in results:
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conversations.append({
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"conversation_id": obj.properties['conversation_id'],
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"category": obj.properties['category'],
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"summary": obj.properties['summary'],
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"timestamp_start": obj.properties['timestamp_start'],
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"timestamp_end": obj.properties['timestamp_end'],
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"participants": obj.properties['participants'],
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"message_count": obj.properties['message_count'],
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})
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return {
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"success": True,
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"query": input_data.query,
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"results": conversations,
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"count": len(conversations),
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}
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finally:
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client.close()
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except Exception as e:
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return {
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"success": False,
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"error": str(e),
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}
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async def list_conversations_handler(input_data: ListConversationsInput) -> Dict[str, Any]:
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"""
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List all conversations with filtering.
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Args:
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input_data: Query parameters.
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Returns:
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Dictionary with conversation list.
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"""
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try:
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# Connect to Weaviate
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client = weaviate.connect_to_local()
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try:
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# Get collection
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collection = client.collections.get("Conversation")
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# Build query
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if input_data.category_filter:
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results = collection.query.fetch_objects(
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filters=weaviate.classes.query.Filter.by_property("category").equal(input_data.category_filter),
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limit=input_data.limit,
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)
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else:
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results = collection.query.fetch_objects(
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limit=input_data.limit,
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)
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# Format results
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conversations = []
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for obj in results.objects:
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conversations.append({
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"conversation_id": obj.properties['conversation_id'],
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"category": obj.properties['category'],
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"summary": obj.properties['summary'][:100] + "..." if len(obj.properties['summary']) > 100 else obj.properties['summary'],
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"timestamp_start": obj.properties['timestamp_start'],
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"message_count": obj.properties['message_count'],
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"participants": obj.properties['participants'],
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})
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return {
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"success": True,
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"conversations": conversations,
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"count": len(conversations),
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}
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finally:
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client.close()
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except Exception as e:
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return {
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"success": False,
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"error": str(e),
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}
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