fix memory mcp
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2f27e10f62
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5417CFEBAD92DF56
@ -60,18 +60,19 @@ def call_mcp(cfg, message: str):
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return response.json().get("output", {}).get("response", "❓ 応答が取得できませんでした")
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def call_openai(cfg, message: str):
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# ツール定義
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tools = [
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{
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"type": "function",
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"function": {
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"name": "memory", # MCPツール名と一致させる
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"description": "AIが記憶ログを参照するツールです",
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"name": "memory",
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"description": "記憶を検索する",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "探している記憶に関するキーワードや質問"
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"description": "検索する語句"
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}
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},
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"required": ["query"]
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@ -80,15 +81,15 @@ def call_openai(cfg, message: str):
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}
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]
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# 最初のメッセージ送信
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payload = {
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"model": cfg["model"],
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"messages": [
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{"role": "system", "content": "あなたはAIで、必要に応じて記憶検索ツール（memory）を使って過去の会話を参照することができます。"},
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{"role": "system", "content": "あなたはAIで、必要に応じてツールmemoryを使って記憶を検索します。"},
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{"role": "user", "content": message}
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],
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"temperature": 0.7,
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"tool_choice": "auto", # AIが自律的にツールを選ぶ
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"tools": tools
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"tools": tools,
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"tool_choice": "auto"
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}
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headers = {
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@ -96,25 +97,43 @@ def call_openai(cfg, message: str):
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"Content-Type": "application/json",
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}
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response = requests.post(cfg["url"], headers=headers, json=payload)
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response.raise_for_status()
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res1 = requests.post(cfg["url"], headers=headers, json=payload)
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res1.raise_for_status()
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result = res1.json()
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result = response.json()
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# AIがtool_callしたかチェック
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# 🧠 tool_call されたか確認
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if "tool_calls" in result["choices"][0]["message"]:
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tool_call = result["choices"][0]["message"]["tool_calls"][0]
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if tool_call["function"]["name"] == "memory":
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args = json.loads(tool_call["function"]["arguments"])
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query = args.get("query", "")
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# ここでMCP serverにPOSTする
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memory_response = requests.post(
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"http://127.0.0.1:5000/memory/search", # あらかじめ実装されたmemory検索用API
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json={"query": query}
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).json()
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return f"[Memory Tool]: {memory_response.get('result', 'なし')}"
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print(f"🛠️ ツール実行: memory(query='{query}')")
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# 通常のテキスト応答
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# MCPエンドポイントにPOST
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memory_res = requests.post("http://127.0.0.1:5000/memory/search", json={"query": query})
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memory_json = memory_res.json()
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tool_output = memory_json.get("result", "なし")
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# tool_outputをAIに返す
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followup = {
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"model": cfg["model"],
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"messages": [
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{"role": "system", "content": "あなたはAIで、必要に応じてツールmemoryを使って記憶を検索します。"},
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{"role": "user", "content": message},
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{"role": "assistant", "tool_calls": result["choices"][0]["message"]["tool_calls"]},
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{"role": "tool", "tool_call_id": tool_call["id"], "name": "memory", "content": tool_output}
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]
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}
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print(tool_output)
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print(cfg["model"])
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res2 = requests.post(cfg["url"], headers=headers, json=followup)
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res2.raise_for_status()
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final_response = res2.json()
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print(final_response)
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return final_response["choices"][0]["message"]["content"]
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# ツール未使用 or 通常応答
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return result["choices"][0]["message"]["content"]
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def call_ollama(cfg, message: str):
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@ -127,7 +146,6 @@ def call_ollama(cfg, message: str):
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response = requests.post(cfg["url"], headers=headers, json=payload)
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response.raise_for_status()
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return response.json().get("response", "❌ 応答が取得できませんでした")
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def main():
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if len(sys.argv) < 2:
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print("Usage: ask.py 'your message'")
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@ -22,16 +22,29 @@ def save_message(sender, message):
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with open(path, "w") as f:
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json.dump(logs, f, indent=2, ensure_ascii=False)
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#def search_memory(query: str):
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# from glob import glob
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# all_logs = []
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# for file_path in sorted(MEMORY_DIR.glob("*.json")):
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# with open(file_path, "r") as f:
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# logs = json.load(f)
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# #matched = [entry for entry in logs if query in entry["message"]]
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# matched = [entry for entry in logs if query.lower() in entry["message"].lower()]
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# all_logs.extend(matched)
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# return all_logs[-5:] # 最新5件だけ返す
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def search_memory(query: str):
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from glob import glob
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all_logs = []
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pattern = re.compile(re.escape(query), re.IGNORECASE)
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for file_path in sorted(MEMORY_DIR.glob("*.json")):
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with open(file_path, "r") as f:
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logs = json.load(f)
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matched = [entry for entry in logs if query in entry["message"]]
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matched = [entry for entry in logs if pattern.search(entry["message"])]
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all_logs.extend(matched)
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return all_logs[-5:] # 最新5件だけ返す
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return all_logs[-5:]
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# scripts/memory_store.py
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import json
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@ -60,15 +73,31 @@ def save_message(sender, message):
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with open(path, "w") as f:
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json.dump(logs, f, indent=2, ensure_ascii=False)
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# キーワードで過去のメモリを検索する（最新5件を返す）
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def search_memory(query: str):
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from glob import glob
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all_logs = []
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for file_path in sorted(MEMORY_DIR.glob("*.json")):
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try:
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with open(file_path, "r") as f:
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logs = json.load(f)
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matched = [entry for entry in logs if query.lower() in entry["message"].lower()]
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matched = [
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entry for entry in logs
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if entry["sender"] == "user" and query in entry["message"]
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]
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all_logs.extend(matched)
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except Exception as e:
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print(f"⚠️ 読み込み失敗: {file_path} ({e})")
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return all_logs[-5:] # 最新5件を返す
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return all_logs[-5:] # 最新5件だけ返す
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def search_memory(query: str):
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from glob import glob
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all_logs = []
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seen_messages = set() # すでに見たメッセージを保持
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for file_path in sorted(MEMORY_DIR.glob("*.json")):
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with open(file_path, "r") as f:
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logs = json.load(f)
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for entry in logs:
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if entry["sender"] == "user" and query in entry["message"]:
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# すでに同じメッセージが結果に含まれていなければ追加
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if entry["message"] not in seen_messages:
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all_logs.append(entry)
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seen_messages.add(entry["message"])
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return all_logs[-5:] # 最新5件だけ返す
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@ -1,9 +1,8 @@
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# server.py
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from fastapi import FastAPI
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from fastapi import FastAPI, Body
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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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from memory_store import save_message, load_logs, search_memory as do_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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@ -43,11 +42,37 @@ async def ask_message(input: MemoryQuery):
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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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## こちらはうまくいく
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## curl -X POST http://127.0.0.1:5000/memory/search -H "Content-Type: application/json" -d '{"query": "昨日"}'
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## {"result":"記憶の中から「昨日」に関するデータを返しました"}
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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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# results = do_search_memory(query)
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# if not results:
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# return {"result": "該当する記憶は見つかりませんでした"}
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# return {
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# "result": "記憶検索結果:\n" + "\n".join([f"{r['sender']}: {r['message']}" for r in results])
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# }
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#
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## こちらはうまくいかない
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## curl -X POST http://127.0.0.1:5000/memory/search -H "Content-Type: application/json" -d '{"query": "昨日"}'
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## Internal Server Error
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#@app.post("/memory/search", operation_id="memory")
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#async def memory_search(query: MemoryQuery):
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# hits = search_memory(query.query)
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# if not hits:
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# return {"result": "🔍 記憶の中に該当する内容は見つかりませんでした。"}
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# summary = "\n".join([f"{e['sender']}: {e['message']}" for e in hits])
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# return {"result": f"🔎 見つかった記憶:\n{summary}"}
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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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async def memory_search(query: MemoryQuery):
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hits = do_search_memory(query.query) # ←ここを修正
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if not hits:
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return {"result": "🔍 記憶の中に該当する内容は見つかりませんでした。"}
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summary = "\n".join([f"{e['sender']}: {e['message']}" for e in hits])
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return {"result": f"🔎 見つかった記憶:\n{summary}"}
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# --- MCP 初期化 ---
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mcp.mount()
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20
mcp/scripts/t.py
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20
mcp/scripts/t.py
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from fastapi import FastAPI
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from fastapi_mcp import FastApiMCP
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app = FastAPI()
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@app.get("/items/{item_id}", operation_id="get_item")
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async def read_item(item_id: int):
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return {"item_id": item_id, "name": f"Item {item_id}"}
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# MCPサーバを作成し、FastAPIアプリにマウント
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mcp = FastApiMCP(
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app,
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name="My API MCP",
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description="My API description"
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)
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mcp.mount()
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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