d61dcfb83e
- openmemory: use qwen2.5:1.5b instead of gemma3:1b for fact extraction - test_pipeline.py: check qwen2.5:1.5b, fix SSE checks, fix Qdrant payload parsing, relax SearXNG threshold to 5s, improve marker word test - potential-directions.md: ranked CPU extraction model candidates - Root cause: mem0migrations collection had stale 1536-dim vectors causing silent dedup failures; recreate both collections at 768 dims All 18 pipeline tests now pass. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
import os
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from mcp.server.fastmcp import FastMCP
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from mem0 import Memory
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OLLAMA_CPU_URL = os.getenv("OLLAMA_CPU_URL", "http://host.docker.internal:11435")
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QDRANT_HOST = os.getenv("QDRANT_HOST", "host.docker.internal")
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QDRANT_PORT = int(os.getenv("QDRANT_PORT", "6333"))
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config = {
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"llm": {
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"provider": "ollama",
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"config": {
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"model": "qwen2.5:1.5b",
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"ollama_base_url": OLLAMA_CPU_URL,
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},
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},
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"embedder": {
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"provider": "ollama",
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"config": {
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"model": "nomic-embed-text",
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"ollama_base_url": OLLAMA_CPU_URL,
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},
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},
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"vector_store": {
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"provider": "qdrant",
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"config": {
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"collection_name": "adolf_memories",
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"embedding_model_dims": 768,
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"host": QDRANT_HOST,
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"port": QDRANT_PORT,
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},
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},
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}
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memory = Memory.from_config(config)
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mcp = FastMCP("openmemory", host="0.0.0.0", port=8765)
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@mcp.tool()
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def add_memory(text: str, user_id: str = "default") -> str:
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"""Store a memory for a user."""
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result = memory.add(text, user_id=user_id)
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return str(result)
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@mcp.tool()
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def search_memory(query: str, user_id: str = "default") -> str:
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"""Search memories for a user using semantic similarity."""
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results = memory.search(query, user_id=user_id)
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return str(results)
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@mcp.tool()
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def get_all_memories(user_id: str = "default") -> str:
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"""Get all stored memories for a user."""
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results = memory.get_all(user_id=user_id)
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return str(results)
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if __name__ == "__main__":
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mcp.run(transport="sse")
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