59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
# server.py
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from fastapi import FastAPI
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from fastapi_mcp import FastApiMCP
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from pydantic import BaseModel
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#from memory_store import save_message, load_messages, search_memory
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from memory_store import save_message, load_logs, search_memory
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app = FastAPI()
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mcp = FastApiMCP(app, name="aigpt-agent", description="MCP Server for AI memory")
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# --- モデル定義 ---
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class ChatInput(BaseModel):
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message: str
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class MemoryInput(BaseModel):
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sender: str
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message: str
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class MemoryQuery(BaseModel):
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query: str
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# --- ツール（エンドポイント）定義 ---
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@app.post("/chat", operation_id="chat")
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async def chat(input: ChatInput):
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save_message("user", input.message)
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response = f"AI: 「{input.message}」を受け取りました！"
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save_message("ai", response)
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return {"response": response}
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@app.post("/memory", operation_id="save_memory")
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async def memory_post(input: MemoryInput):
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save_message(input.sender, input.message)
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return {"status": "saved"}
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@app.get("/memory", operation_id="get_memory")
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async def memory_get():
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return {"messages": load_messages()}
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@app.post("/ask_message", operation_id="ask_message")
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async def ask_message(input: MemoryQuery):
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results = search_memory(input.query)
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return {
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"response": f"🔎 記憶から {len(results)} 件ヒット:\n" + "\n".join([f"{r['sender']}: {r['message']}" for r in results])
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}
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@app.post("/memory/search", operation_id="memory")
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async def search_memory(input: dict):
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query = input.get("query", "")
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# 適当なキーワード検索ロジックを追加（例: logs.jsonを検索）
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return {"result": f"記憶の中から「{query}」に関するデータを返しました"}
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# --- MCP 初期化 ---
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mcp.mount()
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if __name__ == "__main__":
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import uvicorn
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print("🚀 Starting MCP server...")
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uvicorn.run(app, host="127.0.0.1", port=5000)
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