add mcp

mcp/scripts/summarize.py

@@ -0,0 +1,76 @@
+# scripts/summarize.py
+import json
+from datetime import datetime
+from config import MEMORY_DIR, SUMMARY_DIR, load_config
+import requests
+
+def load_memory(date_str):
+    path = MEMORY_DIR / f"{date_str}.json"
+    if not path.exists():
+        print(f"⚠️ メモリファイルが見つかりません: {path}")
+        return None
+    with open(path, "r") as f:
+        return json.load(f)
+
+def save_summary(date_str, content):
+    SUMMARY_DIR.mkdir(parents=True, exist_ok=True)
+    path = SUMMARY_DIR / f"{date_str}_summary.json"
+    with open(path, "w") as f:
+        json.dump(content, f, indent=2, ensure_ascii=False)
+    print(f"✅ 要約を保存しました: {path}")
+
+def build_prompt(logs):
+    messages = [
+        {"role": "system", "content": "あなたは要約AIです。以下の会話ログを要約してください。"},
+        {"role": "user", "content": "\n".join(f"{entry['sender']}: {entry['message']}" for entry in logs)}
+    ]
+    return messages
+
+def summarize_with_llm(messages):
+    cfg = load_config()
+    if cfg["provider"] == "openai":
+        headers = {
+            "Authorization": f"Bearer {cfg['api_key']}",
+            "Content-Type": "application/json",
+        }
+        payload = {
+            "model": cfg["model"],
+            "messages": messages,
+            "temperature": 0.7
+        }
+        response = requests.post(cfg["url"], headers=headers, json=payload)
+        response.raise_for_status()
+        return response.json()["choices"][0]["message"]["content"]
+
+    elif cfg["provider"] == "ollama":
+        payload = {
+            "model": cfg["model"],
+            "prompt": "\n".join(m["content"] for m in messages),
+            "stream": False,
+        }
+        response = requests.post(cfg["url"], json=payload)
+        response.raise_for_status()
+        return response.json()["response"]
+
+    else:
+        raise ValueError(f"Unsupported provider: {cfg['provider']}")
+
+def main():
+    date_str = datetime.now().strftime("%Y-%m-%d")
+    logs = load_memory(date_str)
+    if not logs:
+        return
+
+    prompt_messages = build_prompt(logs)
+    summary_text = summarize_with_llm(prompt_messages)
+
+    summary = {
+        "date": date_str,
+        "summary": summary_text,
+        "total_messages": len(logs)
+    }
+
+    save_summary(date_str, summary)
+
+if __name__ == "__main__":
+    main()