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/thought_tools.py
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203
memory/mcp/thought_tools.py
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"""
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Thought MCP Tools - Handlers for thought-related operations.
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Provides tools for adding, searching, and retrieving thoughts from Weaviate.
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"""
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import weaviate
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from datetime import datetime, timezone
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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 AddThoughtInput(BaseModel):
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"""Input for add_thought tool."""
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content: str = Field(..., description="The thought content")
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thought_type: str = Field(default="reflection", description="Type: reflection, question, intuition, observation, etc.")
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trigger: str = Field(default="", description="What triggered this thought")
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concepts: list[str] = Field(default_factory=list, description="Related concepts/tags")
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privacy_level: str = Field(default="private", description="Privacy: private, shared, public")
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class SearchThoughtsInput(BaseModel):
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"""Input for search_thoughts tool."""
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query: str = Field(..., description="Search query text")
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limit: int = Field(default=10, ge=1, le=100, description="Maximum results")
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thought_type_filter: str | None = Field(default=None, description="Filter by thought type")
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async def add_thought_handler(input_data: AddThoughtInput) -> Dict[str, Any]:
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"""
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Add a new thought to Weaviate.
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Args:
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input_data: Thought data to add.
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Returns:
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Dictionary with success status and thought UUID.
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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 vector for thought content
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vector = embedder.embed_batch([input_data.content])[0]
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# Get collection
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collection = client.collections.get("Thought")
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# Insert thought
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uuid = collection.data.insert(
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properties={
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"content": input_data.content,
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"thought_type": input_data.thought_type,
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"trigger": input_data.trigger,
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"concepts": input_data.concepts,
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"privacy_level": input_data.privacy_level,
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"emotional_state": "",
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"context": "",
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},
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vector=vector.tolist()
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)
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return {
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"success": True,
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"uuid": str(uuid),
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"content": input_data.content[:100] + "..." if len(input_data.content) > 100 else input_data.content,
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"thought_type": input_data.thought_type,
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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_thoughts_handler(input_data: SearchThoughtsInput) -> Dict[str, Any]:
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"""
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Search thoughts 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("Thought")
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# Build query
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query = 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 thought_type filter if provided
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if input_data.thought_type_filter:
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query = query.where({
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"path": ["thought_type"],
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"operator": "Equal",
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"valueText": input_data.thought_type_filter,
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})
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# Execute search
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results = query.objects
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# Format results
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thoughts = []
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for obj in results:
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thoughts.append({
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"uuid": str(obj.uuid),
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"content": obj.properties['content'],
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"thought_type": obj.properties['thought_type'],
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"timestamp": obj.properties['timestamp'],
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"trigger": obj.properties.get('trigger', ''),
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"concepts": obj.properties.get('concepts', []),
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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": thoughts,
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"count": len(thoughts),
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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 get_thought_handler(uuid: str) -> Dict[str, Any]:
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"""
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Get a specific thought by UUID.
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Args:
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uuid: Thought UUID.
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Returns:
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Dictionary with thought 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("Thought")
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# Fetch by UUID
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obj = collection.query.fetch_object_by_id(uuid)
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if not obj:
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return {
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"success": False,
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"error": f"Thought {uuid} not found",
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}
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return {
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"success": True,
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"uuid": str(obj.uuid),
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"content": obj.properties['content'],
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"thought_type": obj.properties['thought_type'],
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"timestamp": obj.properties['timestamp'],
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"trigger": obj.properties.get('trigger', ''),
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"concepts": obj.properties.get('concepts', []),
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"privacy_level": obj.properties.get('privacy_level', 'private'),
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"emotional_state": obj.properties.get('emotional_state', ''),
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"context": obj.properties.get('context', ''),
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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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