import os
from mcp.server.fastmcp import FastMCP
from mem0 import Memory

OLLAMA_CPU_URL = os.getenv("OLLAMA_CPU_URL", "http://host.docker.internal:11435")
QDRANT_HOST = os.getenv("QDRANT_HOST", "host.docker.internal")
QDRANT_PORT = int(os.getenv("QDRANT_PORT", "6333"))

config = {
    "llm": {
        "provider": "ollama",
        "config": {
            "model": "qwen2.5:1.5b",
            "ollama_base_url": OLLAMA_CPU_URL,
        },
    },
    "embedder": {
        "provider": "ollama",
        "config": {
            "model": "nomic-embed-text",
            "ollama_base_url": OLLAMA_CPU_URL,
        },
    },
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "adolf_memories",
            "embedding_model_dims": 768,
            "host": QDRANT_HOST,
            "port": QDRANT_PORT,
        },
    },
}

memory = Memory.from_config(config)

mcp = FastMCP("openmemory", host="0.0.0.0", port=8765)


@mcp.tool()
def add_memory(text: str, user_id: str = "default") -> str:
    """Store a memory for a user."""
    result = memory.add(text, user_id=user_id)
    return str(result)


@mcp.tool()
def search_memory(query: str, user_id: str = "default") -> str:
    """Search memories for a user using semantic similarity."""
    results = memory.search(query, user_id=user_id)
    return str(results)


@mcp.tool()
def get_all_memories(user_id: str = "default") -> str:
    """Get all stored memories for a user."""
    results = memory.get_all(user_id=user_id)
    return str(results)


if __name__ == "__main__":
    mcp.run(transport="sse")