add fastapi_mcp

.gitignore

@@ -3,3 +3,5 @@
 output.json
 config/*.db
 aigpt
+mcp/scripts/__*
+data

Cargo.toml

@@ -12,3 +12,4 @@ rusqlite = { version = "0.29", features = ["serde_json"] }
 shellexpand = "*"
 fs_extra = "1.3"
 rand = "0.9.1"
+reqwest = { version = "*", features = ["blocking", "json"] }

README.md

@@ -33,7 +33,7 @@ $ ./aigpt mcp chat "hello world!" --host http://localhost:11434 --model syui/ai
 
 ---
 # openai api
-$ ./aigpt mcp set-api -api sk-abc123
+$ ./aigpt mcp set-api --api sk-abc123
 $ ./aigpt mcp chat "こんにちは" -p openai -m gpt-4o-mini
 
 ---

mcp/cli.py

@@ -1,3 +1,28 @@
 # cli.py
+import sys
+import subprocess
+from pathlib import Path
+
+SCRIPT_DIR = Path.home() / ".config" / "aigpt" / "mcp" / "scripts"
+def run_script(name):
+    script_path = SCRIPT_DIR / f"{name}.py"
+    if not script_path.exists():
+        print(f"❌ スクリプトが見つかりません: {script_path}")
+        sys.exit(1)
+
+    args = sys.argv[2:]  # ← "ask" の後の引数を取り出す
+    result = subprocess.run(["python", str(script_path)] + args, capture_output=True, text=True)
+    print(result.stdout)
+    if result.stderr:
+        print(result.stderr)
 def main():
-    print("Hello MCP!")
+    if len(sys.argv) < 2:
+        print("Usage: mcp <script>")
+        return
+
+    command = sys.argv[1]
+
+    if command in {"summarize", "ask", "setup", "server"}:
+        run_script(command)
+    else:
+        print(f"❓ 未知のコマンド: {command}")

mcp/scripts/ask.py

@@ -1,55 +1,198 @@
-import os
+## scripts/ask.py
+import sys
 import json
-import httpx
+import requests
-import openai
+from config import load_config
+from datetime import datetime, timezone
 
-from context_loader import load_context_from_repo
+def build_payload_openai(cfg, message: str):
-from prompt_template import PROMPT_TEMPLATE
+    return {
+        "model": cfg["model"],
+        "tools": [
+            {
+                "type": "function",
+                "function": {
+                    "name": "ask_message",
+                    "description": "過去の記憶を検索します",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "query": {
+                                "type": "string",
+                                "description": "検索したい語句"
+                            }
+                        },
+                        "required": ["query"]
+                    }
+                }
+            }
+        ],
+        "tool_choice": "auto",
+        "messages": [
+            {"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。"},
+            {"role": "user", "content": message}
+        ]
+    }
 
-PROVIDER = os.getenv("PROVIDER", "ollama")  # "ollama" or "openai"
+def build_payload_mcp(message: str):
+    return {
+        "tool": "ask_message",  # MCPサーバー側で定義されたツール名
+        "input": {
+            "message": message
+        }
+    }
 
-# Ollama用
+def build_payload_openai(cfg, message: str):
-OLLAMA_HOST = os.getenv("OLLAMA_HOST", "http://localhost:11434")
+    return {
-OLLAMA_URL = f"{OLLAMA_HOST}/api/generate"
+        "model": cfg["model"],
-OLLAMA_MODEL = os.getenv("MODEL", "syui/ai")
+        "messages": [
+            {"role": "system", "content": "あなたは思いやりのあるAIです。"},
+            {"role": "user", "content": message}
+        ],
+        "temperature": 0.7
+    }
 
-# OpenAI用
+def call_mcp(cfg, message: str):
-OPENAI_BASE = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")
+    payload = build_payload_mcp(message)
-OPENAI_KEY = os.getenv("OPENAI_API_KEY", "")
+    headers = {"Content-Type": "application/json"}
-OPENAI_MODEL = os.getenv("MODEL", "gpt-4o-mini")
+    response = requests.post(cfg["url"], headers=headers, json=payload)
+    response.raise_for_status()
+    return response.json().get("output", {}).get("response", "❓ 応答が取得できませんでした")
 
-def ask_question(question, repo_path="."):
+def call_openai(cfg, message: str):
-    context = load_context_from_repo(repo_path)
+    # ツール定義
-    prompt = PROMPT_TEMPLATE.format(context=context[:10000], question=question)
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "memory",
+                "description": "記憶を検索する",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "検索する語句"
+                        }
+                    },
+                    "required": ["query"]
+                }
+            }
+        }
+    ]
 
-    if PROVIDER == "ollama":
+    # 最初のメッセージ送信
     payload = {
-            "model": OLLAMA_MODEL,
+        "model": cfg["model"],
-            "prompt": prompt,
+        "messages": [
+            {"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。"},
+            {"role": "user", "content": message}
+        ],
+        "tools": tools,
+        "tool_choice": "auto"
+    }
+
+    headers = {
+        "Authorization": f"Bearer {cfg['api_key']}",
+        "Content-Type": "application/json",
+    }
+
+    res1 = requests.post(cfg["url"], headers=headers, json=payload)
+    res1.raise_for_status()
+    result = res1.json()
+
+    # 🧠 tool_call されたか確認
+    if "tool_calls" in result["choices"][0]["message"]:
+        tool_call = result["choices"][0]["message"]["tool_calls"][0]
+        if tool_call["function"]["name"] == "memory":
+            args = json.loads(tool_call["function"]["arguments"])
+            query = args.get("query", "")
+            print(f"🛠️ ツール実行: memory(query='{query}')")
+
+            # MCPエンドポイントにPOST
+            memory_res = requests.post("http://127.0.0.1:5000/memory/search", json={"query": query})
+            memory_json = memory_res.json()
+            tool_output = memory_json.get("result", "なし")
+
+            # tool_outputをAIに返す
+            followup = {
+                "model": cfg["model"],
+                "messages": [
+                    {"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。"},
+                    {"role": "user", "content": message},
+                    {"role": "assistant", "tool_calls": result["choices"][0]["message"]["tool_calls"]},
+                    {"role": "tool", "tool_call_id": tool_call["id"], "name": "memory", "content": tool_output}
+                ]
+            }
+
+            res2 = requests.post(cfg["url"], headers=headers, json=followup)
+            res2.raise_for_status()
+            final_response = res2.json()
+            return final_response["choices"][0]["message"]["content"]
+            #print(tool_output)
+            #print(cfg["model"])
+            #print(final_response)
+
+    # ツール未使用 or 通常応答
+    return result["choices"][0]["message"]["content"]
+
+def call_ollama(cfg, message: str):
+    payload = {
+            "model": cfg["model"],
+            "prompt": message,  # `prompt` → `message` にすべき（変数未定義エラー回避）
             "stream": False
             }
-        response = httpx.post(OLLAMA_URL, json=payload, timeout=60.0)
+    headers = {"Content-Type": "application/json"}
-        result = response.json()
+    response = requests.post(cfg["url"], headers=headers, json=payload)
-        return result.get("response", "返答がありませんでした。")
+    response.raise_for_status()
+    return response.json().get("response", "❌ 応答が取得できませんでした")
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: ask.py 'your message'")
+        return
 
-    elif PROVIDER == "openai":
+    message = sys.argv[1]
-        import openai
+    cfg = load_config()
-        openai.api_key = OPENAI_KEY
-        openai.api_base = OPENAI_BASE
 
-        client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+    print(f"🔍 使用プロバイダー: {cfg['provider']}")
-        response = client.chat.completions.create(
-            model=OPENAI_MODEL,
-            messages=[{"role": "user", "content": prompt}]
-        )
-        return response.choices[0].message.content
 
+    try:
+        if cfg["provider"] == "openai":
+            response = call_openai(cfg, message)
+        elif cfg["provider"] == "mcp":
+            response = call_mcp(cfg, message)
+        elif cfg["provider"] == "ollama":
+            response = call_ollama(cfg, message)
         else:
-        return f"❌ 未知のプロバイダです: {PROVIDER}"
+            raise ValueError(f"未対応のプロバイダー: {cfg['provider']}")
 
+        print("💬 応答:")
+        print(response)
+
+        # ログ保存（オプション）
+        save_log(message, response)
+
+    except Exception as e:
+        print(f"❌ 実行エラー: {e}")
+
+def save_log(user_msg, ai_msg):
+    from config import MEMORY_DIR
+    date_str = datetime.now().strftime("%Y-%m-%d")
+    path = MEMORY_DIR / f"{date_str}.json"
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    if path.exists():
+        with open(path, "r") as f:
+            logs = json.load(f)
+    else:
+        logs = []
+
+    now = datetime.now(timezone.utc).isoformat()
+    logs.append({"timestamp": now, "sender": "user", "message": user_msg})
+    logs.append({"timestamp": now, "sender": "ai", "message": ai_msg})
+
+    with open(path, "w") as f:
+        json.dump(logs, f, indent=2, ensure_ascii=False)
 
 if __name__ == "__main__":
-    import sys
+    main()
-    question = " ".join(sys.argv[1:])
-    answer = ask_question(question)
-    print("\n🧠 回答:\n", answer)

mcp/scripts/config.py

@@ -0,0 +1,41 @@
+# scripts/config.py
+# scripts/config.py
+import os
+from pathlib import Path
+
+# ディレクトリ設定
+BASE_DIR = Path.home() / ".config" / "aigpt"
+MEMORY_DIR = BASE_DIR / "memory"
+SUMMARY_DIR = MEMORY_DIR / "summary"
+
+def init_directories():
+    BASE_DIR.mkdir(parents=True, exist_ok=True)
+    MEMORY_DIR.mkdir(parents=True, exist_ok=True)
+    SUMMARY_DIR.mkdir(parents=True, exist_ok=True)
+
+def load_config():
+    provider = os.getenv("PROVIDER", "ollama")
+    model = os.getenv("MODEL", "syui/ai" if provider == "ollama" else "gpt-4o-mini")
+    api_key = os.getenv("OPENAI_API_KEY", "")
+
+    if provider == "ollama":
+        return {
+            "provider": "ollama",
+            "model": model,
+            "url": f"{os.getenv('OLLAMA_HOST', 'http://localhost:11434')}/api/generate"
+        }
+    elif provider == "openai":
+        return {
+            "provider": "openai",
+            "model": model,
+            "api_key": api_key,
+            "url": f"{os.getenv('OPENAI_API_BASE', 'https://api.openai.com/v1')}/chat/completions"
+        }
+    elif provider == "mcp":
+        return {
+            "provider": "mcp",
+            "model": model,
+            "url": os.getenv("MCP_URL", "http://localhost:5000/chat")
+        }
+    else:
+        raise ValueError(f"Unsupported provider: {provider}")

mcp/scripts/memory_store.py

@@ -0,0 +1,92 @@
+# scripts/memory_store.py
+import json
+from pathlib import Path
+from config import MEMORY_DIR
+from datetime import datetime, timezone
+
+def load_logs(date_str=None):
+    if date_str is None:
+        date_str = datetime.now().strftime("%Y-%m-%d")
+    path = MEMORY_DIR / f"{date_str}.json"
+    if path.exists():
+        with open(path, "r") as f:
+            return json.load(f)
+    return []
+
+def save_message(sender, message):
+    date_str = datetime.now().strftime("%Y-%m-%d")
+    path = MEMORY_DIR / f"{date_str}.json"
+    logs = load_logs(date_str)
+    now = datetime.now(timezone.utc).isoformat()
+    logs.append({"timestamp": now, "sender": sender, "message": message})
+    with open(path, "w") as f:
+        json.dump(logs, f, indent=2, ensure_ascii=False)
+
+def search_memory(query: str):
+    from glob import glob
+    all_logs = []
+    pattern = re.compile(re.escape(query), re.IGNORECASE)
+
+    for file_path in sorted(MEMORY_DIR.glob("*.json")):
+        with open(file_path, "r") as f:
+            logs = json.load(f)
+            matched = [entry for entry in logs if pattern.search(entry["message"])]
+            all_logs.extend(matched)
+
+    return all_logs[-5:]
+
+# scripts/memory_store.py
+import json
+from datetime import datetime
+from pathlib import Path
+from config import MEMORY_DIR
+
+# ログを読み込む（指定日または当日）
+def load_logs(date_str=None):
+    if date_str is None:
+        date_str = datetime.now().strftime("%Y-%m-%d")
+    path = MEMORY_DIR / f"{date_str}.json"
+    if path.exists():
+        with open(path, "r") as f:
+            return json.load(f)
+    return []
+
+# メッセージを保存する
+def save_message(sender, message):
+    date_str = datetime.now().strftime("%Y-%m-%d")
+    path = MEMORY_DIR / f"{date_str}.json"
+    logs = load_logs(date_str)
+    #now = datetime.utcnow().isoformat() + "Z"
+    now = datetime.now(timezone.utc).isoformat()
+    logs.append({"timestamp": now, "sender": sender, "message": message})
+    with open(path, "w") as f:
+        json.dump(logs, f, indent=2, ensure_ascii=False)
+
+def search_memory(query: str):
+    from glob import glob
+    all_logs = []
+    for file_path in sorted(MEMORY_DIR.glob("*.json")):
+        with open(file_path, "r") as f:
+            logs = json.load(f)
+            matched = [
+                entry for entry in logs 
+                if entry["sender"] == "user" and query in entry["message"]
+            ]
+            all_logs.extend(matched)
+    return all_logs[-5:]  # 最新5件だけ返す
+def search_memory(query: str):
+    from glob import glob
+    all_logs = []
+    seen_messages = set()  # すでに見たメッセージを保持
+
+    for file_path in sorted(MEMORY_DIR.glob("*.json")):
+        with open(file_path, "r") as f:
+            logs = json.load(f)
+            for entry in logs:
+                if entry["sender"] == "user" and query in entry["message"]:
+                    # 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
+                    if entry["message"] not in seen_messages:
+                        all_logs.append(entry)
+                        seen_messages.add(entry["message"])
+
+    return all_logs[-5:]  # 最新5件だけ返す

mcp/scripts/server.py

@@ -0,0 +1,56 @@
+# server.py
+from fastapi import FastAPI, Body
+from fastapi_mcp import FastApiMCP
+from pydantic import BaseModel
+from memory_store import save_message, load_logs, search_memory as do_search_memory
+
+app = FastAPI()
+mcp = FastApiMCP(app, name="aigpt-agent", description="MCP Server for AI memory")
+
+class ChatInput(BaseModel):
+    message: str
+
+class MemoryInput(BaseModel):
+    sender: str
+    message: str
+
+class MemoryQuery(BaseModel):
+    query: str
+
+@app.post("/chat", operation_id="chat")
+async def chat(input: ChatInput):
+    save_message("user", input.message)
+    response = f"AI: 「{input.message}」を受け取りました！"
+    save_message("ai", response)
+    return {"response": response}
+
+@app.post("/memory", operation_id="save_memory")
+async def memory_post(input: MemoryInput):
+    save_message(input.sender, input.message)
+    return {"status": "saved"}
+
+@app.get("/memory", operation_id="get_memory")
+async def memory_get():
+    return {"messages": load_messages()}
+
+@app.post("/ask_message", operation_id="ask_message")
+async def ask_message(input: MemoryQuery):
+    results = search_memory(input.query)
+    return {
+        "response": f"🔎 記憶から {len(results)} 件ヒット:\n" + "\n".join([f"{r['sender']}: {r['message']}" for r in results])
+    }
+
+@app.post("/memory/search", operation_id="memory")
+async def memory_search(query: MemoryQuery):
+    hits = do_search_memory(query.query)
+    if not hits:
+        return {"result": "🔍 記憶の中に該当する内容は見つかりませんでした。"}
+    summary = "\n".join([f"{e['sender']}: {e['message']}" for e in hits])
+    return {"result": f"🔎 見つかった記憶:\n{summary}"}
+
+mcp.mount()
+
+if __name__ == "__main__":
+    import uvicorn
+    print("🚀 Starting MCP server...")
+    uvicorn.run(app, host="127.0.0.1", port=5000)

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()

mcp/setup.py

@@ -1,8 +1,8 @@
+# setup.py
 from setuptools import setup
 
 setup(
-    name='mcp',
+    name='aigpt-mcp',
-    version='0.1.0',
     py_modules=['cli'],
     entry_points={
         'console_scripts': [

src/chat.rs

@@ -5,11 +5,16 @@ use serde::Deserialize;
 use seahorse::Context;
 use crate::config::ConfigPaths;
 use crate::metrics::{load_user_data, save_user_data, update_metrics_decay};
+//use std::process::Stdio;
+//use std::io::Write;
+//use std::time::Duration;
+//use std::net::TcpStream;
 
 #[derive(Debug, Clone, PartialEq)]
 pub enum Provider {
     OpenAI,
     Ollama,
+    MCP,
 }
 
 impl Provider {
@@ -17,6 +22,7 @@ impl Provider {
         match s.to_lowercase().as_str() {
             "openai" => Some(Provider::OpenAI),
             "ollama" => Some(Provider::Ollama),
+            "mcp" => Some(Provider::MCP),
             _ => None,
         }
     }
@@ -25,6 +31,7 @@ impl Provider {
         match self {
             Provider::OpenAI => "openai",
             Provider::Ollama => "ollama",
+            Provider::MCP => "mcp",
         }
     }
 }
@@ -45,19 +52,11 @@ fn load_openai_api_key() -> Option<String> {
 pub fn ask_chat(c: &Context, question: &str) -> Option<String> {
     let config = ConfigPaths::new();
     let base_dir = config.base_dir.join("mcp");
-    let script_path = base_dir.join("scripts/ask.py");
     let user_path = config.base_dir.join("user.json");
 
     let mut user = load_user_data(&user_path);
     user.metrics = update_metrics_decay();
 
-    // Python 実行パス
-    let python_path = if cfg!(target_os = "windows") {
-        base_dir.join(".venv/Scripts/python.exe")
-    } else {
-        base_dir.join(".venv/bin/python")
-    };
-
     // 各種オプション
     let ollama_host = c.string_flag("host").ok();
     let ollama_model = c.string_flag("model").ok();
@@ -67,9 +66,44 @@ pub fn ask_chat(c: &Context, question: &str) -> Option<String> {
 
     println!("🔍 使用プロバイダー: {}", provider.as_str());
 
-    // Python コマンド準備
+    match provider {
+        Provider::MCP => {
+            let client = reqwest::blocking::Client::new();
+            let url = std::env::var("MCP_URL").unwrap_or("http://127.0.0.1:5000/chat".to_string());
+            let res = client.post(url)
+                .json(&serde_json::json!({"message": question}))
+                .send();
+
+            match res {
+                Ok(resp) => {
+                    if resp.status().is_success() {
+                        let json: serde_json::Value = resp.json().ok()?;
+                        let text = json.get("response")?.as_str()?.to_string();
+                        user.metrics.intimacy += 0.01;
+                        user.metrics.last_updated = chrono::Utc::now();
+                        save_user_data(&user_path, &user);
+                        Some(text)
+                    } else {
+                        eprintln!("❌ MCPエラー: HTTP {}", resp.status());
+                        None
+                    }
+                }
+                Err(e) => {
+                    eprintln!("❌ MCP接続失敗: {}", e);
+                    None
+                }
+            }
+        }
+        _ => {
+            // Python 実行パス
+            let python_path = if cfg!(target_os = "windows") {
+                base_dir.join(".venv/Scripts/mcp.exe")
+            } else {
+                base_dir.join(".venv/bin/mcp")
+            };
+
             let mut command = Command::new(python_path);
-    command.arg(script_path).arg(question);
+            command.arg("ask").arg(question);
 
             if let Some(host) = ollama_host {
                 command.env("OLLAMA_HOST", host);
@@ -101,4 +135,6 @@ pub fn ask_chat(c: &Context, question: &str) -> Option<String> {
                 );
                 None
             }
+        }
+    }
 }

src/commands/mcp.rs

@@ -32,8 +32,12 @@ pub fn mcp_setup() {
         "cli.py",
         "setup.py",
         "scripts/ask.py",
+        "scripts/server.py",
+        "scripts/config.py",
+        "scripts/summarize.py",
         "scripts/context_loader.py",
         "scripts/prompt_template.py",
+        "scripts/memory_store.py",
     ];
 
     for rel_path in files_to_copy {
@@ -76,6 +80,12 @@ pub fn mcp_setup() {
     let output = OtherCommand::new(&pip_path)
         .arg("install")
         .arg("openai")
+        .arg("requests")
+        .arg("fastmcp")
+        .arg("uvicorn")
+        .arg("fastapi")
+        .arg("fastapi_mcp")
+        .arg("mcp")
         .current_dir(&dest_dir)
         .output()
         .expect("pip install に失敗しました");