fix mcp
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5417CFEBAD92DF56
@ -2,8 +2,37 @@
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import sys
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import json
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import requests
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from datetime import datetime
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from config import load_config
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from datetime import datetime, timezone
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def build_payload_openai(cfg, message: str):
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return {
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"model": cfg["model"],
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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": "ask_message",
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"description": "過去の記憶を検索します",
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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": "検索したい語句"
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}
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},
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"required": ["query"]
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}
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}
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}
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],
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"tool_choice": "auto",
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"messages": [
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{"role": "system", "content": "あなたは親しみやすいAIで、必要に応じて記憶から情報を検索して応答します。"},
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{"role": "user", "content": message}
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]
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}
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def build_payload_mcp(message: str):
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return {
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@ -30,15 +59,73 @@ def call_mcp(cfg, message: str):
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response.raise_for_status()
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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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# payload = build_payload_openai(cfg, message)
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# headers = {
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# "Authorization": f"Bearer {cfg['api_key']}",
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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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# return response.json()["choices"][0]["message"]["content"]
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def call_openai(cfg, message: str):
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payload = build_payload_openai(cfg, message)
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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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"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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}
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},
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"required": ["query"]
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}
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}
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}
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]
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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": "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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}
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headers = {
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"Authorization": f"Bearer {cfg['api_key']}",
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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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return response.json()["choices"][0]["message"]["content"]
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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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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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# 通常のテキスト応答
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return result["choices"][0]["message"]["content"]
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def main():
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if len(sys.argv) < 2:
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@ -79,7 +166,7 @@ def save_log(user_msg, ai_msg):
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else:
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logs = []
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now = datetime.utcnow().isoformat() + "Z"
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now = datetime.now(timezone.utc).isoformat()
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logs.append({"timestamp": now, "sender": "user", "message": user_msg})
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logs.append({"timestamp": now, "sender": "ai", "message": ai_msg})
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@ -1,37 +1,74 @@
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# scripts/memory_store.py
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from pathlib import Path
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import json
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from datetime import datetime
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MEMORY_DIR = Path.home() / ".config" / "aigpt" / "memory"
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MEMORY_DIR.mkdir(parents=True, exist_ok=True)
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def get_today_path():
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today = datetime.utcnow().strftime("%Y-%m-%d")
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return MEMORY_DIR / f"{today}.json"
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def save_message(sender: str, message: str):
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entry = {
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"timestamp": datetime.utcnow().isoformat(),
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"sender": sender,
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"message": message
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}
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path = get_today_path()
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data = []
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from pathlib import Path
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from config import MEMORY_DIR
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from datetime import datetime, timezone
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def load_logs(date_str=None):
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if date_str is None:
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date_str = datetime.now().strftime("%Y-%m-%d")
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path = MEMORY_DIR / f"{date_str}.json"
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if path.exists():
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with open(path, "r") as f:
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data = json.load(f)
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data.append(entry)
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with open(path, "w") as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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def load_messages():
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path = get_today_path()
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if not path.exists():
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return json.load(f)
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return []
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def save_message(sender, message):
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date_str = datetime.now().strftime("%Y-%m-%d")
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path = MEMORY_DIR / f"{date_str}.json"
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logs = load_logs(date_str)
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now = datetime.now(timezone.utc).isoformat()
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logs.append({"timestamp": now, "sender": sender, "message": 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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all_logs.extend(matched)
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return all_logs[-5:] # 最新5件だけ返す
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# scripts/memory_store.py
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import json
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from datetime import datetime
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from pathlib import Path
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from config import MEMORY_DIR
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# ログを読み込む（指定日または当日）
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def load_logs(date_str=None):
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if date_str is None:
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date_str = datetime.now().strftime("%Y-%m-%d")
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path = MEMORY_DIR / f"{date_str}.json"
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if path.exists():
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with open(path, "r") as f:
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return json.load(f)
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return []
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# メッセージを保存する
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def save_message(sender, message):
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date_str = datetime.now().strftime("%Y-%m-%d")
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path = MEMORY_DIR / f"{date_str}.json"
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logs = load_logs(date_str)
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#now = datetime.utcnow().isoformat() + "Z"
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now = datetime.now(timezone.utc).isoformat()
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logs.append({"timestamp": now, "sender": sender, "message": 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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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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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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@ -2,7 +2,8 @@
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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
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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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@ -15,6 +16,9 @@ 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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@ -32,6 +36,19 @@ async def memory_post(input: MemoryInput):
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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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