feature/shell-integration #1
@ -3,7 +3,8 @@
|
||||
"allow": [
|
||||
"Bash(mv:*)",
|
||||
"Bash(mkdir:*)",
|
||||
"Bash(chmod:*)"
|
||||
"Bash(chmod:*)",
|
||||
"Bash(git submodule:*)"
|
||||
],
|
||||
"deny": []
|
||||
}
|
||||
|
||||
4
.gitmodules
vendored
4
.gitmodules
vendored
@ -1,3 +1,7 @@
|
||||
[submodule "shell"]
|
||||
path = shell
|
||||
url = git@git.syui.ai:ai/shell
|
||||
[submodule "card"]
|
||||
path = card
|
||||
url = git@git.syui.ai:ai/card
|
||||
branch = claude
|
||||
|
||||
1
card
Submodule
1
card
Submodule
@ -0,0 +1 @@
|
||||
Subproject commit 6dbe630b9d3d72c3da0da1edade8c47231b6863d
|
||||
413
docs/shell_integration/shell_tools.py
Normal file
413
docs/shell_integration/shell_tools.py
Normal file
@ -0,0 +1,413 @@
|
||||
"""
|
||||
Shell Tools
|
||||
|
||||
ai.shellの既存機能をMCPツールとして統合
|
||||
- コード生成
|
||||
- ファイル分析
|
||||
- プロジェクト管理
|
||||
- LLM統合
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional
|
||||
import os
|
||||
import subprocess
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
import requests
|
||||
from .base_tools import BaseMCPTool, config_manager
|
||||
|
||||
|
||||
class ShellTools(BaseMCPTool):
|
||||
"""シェルツール（元ai.shell機能）"""
|
||||
|
||||
def __init__(self, config_dir: Optional[str] = None):
|
||||
super().__init__(config_dir)
|
||||
self.ollama_url = "http://localhost:11434"
|
||||
|
||||
async def code_with_local_llm(self, prompt: str, language: str = "python") -> Dict[str, Any]:
|
||||
"""ローカルLLMでコード生成"""
|
||||
config = config_manager.load_config()
|
||||
model = config.get("providers", {}).get("ollama", {}).get("default_model", "qwen2.5-coder:7b")
|
||||
|
||||
system_prompt = f"You are an expert {language} programmer. Generate clean, well-commented code."
|
||||
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.ollama_url}/api/generate",
|
||||
json={
|
||||
"model": model,
|
||||
"prompt": f"{system_prompt}\\n\\nUser: {prompt}\\n\\nPlease provide the code:",
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": 0.1,
|
||||
"top_p": 0.95,
|
||||
}
|
||||
},
|
||||
timeout=300
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
code = result.get("response", "")
|
||||
return {"code": code, "language": language}
|
||||
else:
|
||||
return {"error": f"Ollama returned status {response.status_code}"}
|
||||
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
async def analyze_file(self, file_path: str, analysis_prompt: str = "Analyze this file") -> Dict[str, Any]:
|
||||
"""ファイルを分析"""
|
||||
try:
|
||||
if not os.path.exists(file_path):
|
||||
return {"error": f"File not found: {file_path}"}
|
||||
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
# ファイル拡張子から言語を判定
|
||||
ext = Path(file_path).suffix
|
||||
language_map = {
|
||||
'.py': 'python',
|
||||
'.rs': 'rust',
|
||||
'.js': 'javascript',
|
||||
'.ts': 'typescript',
|
||||
'.go': 'go',
|
||||
'.java': 'java',
|
||||
'.cpp': 'cpp',
|
||||
'.c': 'c',
|
||||
'.sh': 'shell',
|
||||
'.toml': 'toml',
|
||||
'.json': 'json',
|
||||
'.md': 'markdown'
|
||||
}
|
||||
language = language_map.get(ext, 'text')
|
||||
|
||||
config = config_manager.load_config()
|
||||
model = config.get("providers", {}).get("ollama", {}).get("default_model", "qwen2.5-coder:7b")
|
||||
|
||||
prompt = f"{analysis_prompt}\\n\\nFile: {file_path}\\nLanguage: {language}\\n\\nContent:\\n{content}"
|
||||
|
||||
response = requests.post(
|
||||
f"{self.ollama_url}/api/generate",
|
||||
json={
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
},
|
||||
timeout=300
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
analysis = result.get("response", "")
|
||||
return {
|
||||
"analysis": analysis,
|
||||
"file_path": file_path,
|
||||
"language": language,
|
||||
"file_size": len(content),
|
||||
"line_count": len(content.split('\\n'))
|
||||
}
|
||||
else:
|
||||
return {"error": f"Analysis failed: {response.status_code}"}
|
||||
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
async def explain_code(self, code: str, language: str = "python") -> Dict[str, Any]:
|
||||
"""コードを説明"""
|
||||
config = config_manager.load_config()
|
||||
model = config.get("providers", {}).get("ollama", {}).get("default_model", "qwen2.5-coder:7b")
|
||||
|
||||
prompt = f"Explain this {language} code in detail:\\n\\n{code}"
|
||||
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.ollama_url}/api/generate",
|
||||
json={
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
},
|
||||
timeout=300
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
explanation = result.get("response", "")
|
||||
return {"explanation": explanation}
|
||||
else:
|
||||
return {"error": f"Explanation failed: {response.status_code}"}
|
||||
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
async def create_project(self, project_type: str, project_name: str, location: str = ".") -> Dict[str, Any]:
|
||||
"""プロジェクトを作成"""
|
||||
try:
|
||||
project_path = Path(location) / project_name
|
||||
|
||||
if project_path.exists():
|
||||
return {"error": f"Project directory already exists: {project_path}"}
|
||||
|
||||
project_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# プロジェクトタイプに応じたテンプレートを作成
|
||||
if project_type == "rust":
|
||||
await self._create_rust_project(project_path)
|
||||
elif project_type == "python":
|
||||
await self._create_python_project(project_path)
|
||||
elif project_type == "node":
|
||||
await self._create_node_project(project_path)
|
||||
else:
|
||||
# 基本的なプロジェクト構造
|
||||
(project_path / "src").mkdir()
|
||||
(project_path / "README.md").write_text(f"# {project_name}\\n\\nA new {project_type} project.")
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"project_path": str(project_path),
|
||||
"project_type": project_type,
|
||||
"files_created": list(self._get_project_files(project_path))
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
async def _create_rust_project(self, project_path: Path):
|
||||
"""Rustプロジェクトを作成"""
|
||||
# Cargo.toml
|
||||
cargo_toml = f"""[package]
|
||||
name = "{project_path.name}"
|
||||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
|
||||
[dependencies]
|
||||
"""
|
||||
(project_path / "Cargo.toml").write_text(cargo_toml)
|
||||
|
||||
# src/main.rs
|
||||
src_dir = project_path / "src"
|
||||
src_dir.mkdir()
|
||||
(src_dir / "main.rs").write_text('fn main() {\\n println!("Hello, world!");\\n}\\n')
|
||||
|
||||
# README.md
|
||||
(project_path / "README.md").write_text(f"# {project_path.name}\\n\\nA Rust project.")
|
||||
|
||||
async def _create_python_project(self, project_path: Path):
|
||||
"""Pythonプロジェクトを作成"""
|
||||
# pyproject.toml
|
||||
pyproject_toml = f"""[project]
|
||||
name = "{project_path.name}"
|
||||
version = "0.1.0"
|
||||
description = "A Python project"
|
||||
requires-python = ">=3.8"
|
||||
dependencies = []
|
||||
|
||||
[build-system]
|
||||
requires = ["setuptools>=61.0", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
"""
|
||||
(project_path / "pyproject.toml").write_text(pyproject_toml)
|
||||
|
||||
# src/
|
||||
src_dir = project_path / "src" / project_path.name
|
||||
src_dir.mkdir(parents=True)
|
||||
(src_dir / "__init__.py").write_text("")
|
||||
(src_dir / "main.py").write_text('def main():\\n print("Hello, world!")\\n\\nif __name__ == "__main__":\\n main()\\n')
|
||||
|
||||
# README.md
|
||||
(project_path / "README.md").write_text(f"# {project_path.name}\\n\\nA Python project.")
|
||||
|
||||
async def _create_node_project(self, project_path: Path):
|
||||
"""Node.jsプロジェクトを作成"""
|
||||
# package.json
|
||||
package_json = f"""{{
|
||||
"name": "{project_path.name}",
|
||||
"version": "1.0.0",
|
||||
"description": "A Node.js project",
|
||||
"main": "index.js",
|
||||
"scripts": {{
|
||||
"start": "node index.js",
|
||||
"test": "echo \\"Error: no test specified\\" && exit 1"
|
||||
}},
|
||||
"dependencies": {{}}
|
||||
}}
|
||||
"""
|
||||
(project_path / "package.json").write_text(package_json)
|
||||
|
||||
# index.js
|
||||
(project_path / "index.js").write_text('console.log("Hello, world!");\\n')
|
||||
|
||||
# README.md
|
||||
(project_path / "README.md").write_text(f"# {project_path.name}\\n\\nA Node.js project.")
|
||||
|
||||
def _get_project_files(self, project_path: Path) -> List[str]:
|
||||
"""プロジェクト内のファイル一覧を取得"""
|
||||
files = []
|
||||
for file_path in project_path.rglob("*"):
|
||||
if file_path.is_file():
|
||||
files.append(str(file_path.relative_to(project_path)))
|
||||
return files
|
||||
|
||||
async def execute_command(self, command: str, working_dir: str = ".") -> Dict[str, Any]:
|
||||
"""シェルコマンドを実行"""
|
||||
try:
|
||||
result = subprocess.run(
|
||||
command,
|
||||
shell=True,
|
||||
cwd=working_dir,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=60
|
||||
)
|
||||
|
||||
return {
|
||||
"status": "success" if result.returncode == 0 else "error",
|
||||
"returncode": result.returncode,
|
||||
"stdout": result.stdout,
|
||||
"stderr": result.stderr,
|
||||
"command": command,
|
||||
"working_dir": working_dir
|
||||
}
|
||||
|
||||
except subprocess.TimeoutExpired:
|
||||
return {"error": "Command timed out"}
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
async def write_file(self, file_path: str, content: str, backup: bool = True) -> Dict[str, Any]:
|
||||
"""ファイルを書き込み（バックアップオプション付き）"""
|
||||
try:
|
||||
file_path_obj = Path(file_path)
|
||||
|
||||
# バックアップ作成
|
||||
backup_path = None
|
||||
if backup and file_path_obj.exists():
|
||||
backup_path = f"{file_path}.backup"
|
||||
with open(file_path, 'r', encoding='utf-8') as src:
|
||||
with open(backup_path, 'w', encoding='utf-8') as dst:
|
||||
dst.write(src.read())
|
||||
|
||||
# ファイル書き込み
|
||||
file_path_obj.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(file_path, 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"file_path": file_path,
|
||||
"backup_path": backup_path,
|
||||
"bytes_written": len(content.encode('utf-8'))
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
def get_tools(self) -> List[Dict[str, Any]]:
|
||||
"""利用可能なツール一覧"""
|
||||
return [
|
||||
{
|
||||
"name": "generate_code",
|
||||
"description": "ローカルLLMでコード生成",
|
||||
"parameters": {
|
||||
"prompt": "string",
|
||||
"language": "string (optional, default: python)"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "analyze_file",
|
||||
"description": "ファイルを分析",
|
||||
"parameters": {
|
||||
"file_path": "string",
|
||||
"analysis_prompt": "string (optional)"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "explain_code",
|
||||
"description": "コードを説明",
|
||||
"parameters": {
|
||||
"code": "string",
|
||||
"language": "string (optional, default: python)"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "create_project",
|
||||
"description": "新しいプロジェクトを作成",
|
||||
"parameters": {
|
||||
"project_type": "string (rust/python/node)",
|
||||
"project_name": "string",
|
||||
"location": "string (optional, default: .)"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "execute_command",
|
||||
"description": "シェルコマンドを実行",
|
||||
"parameters": {
|
||||
"command": "string",
|
||||
"working_dir": "string (optional, default: .)"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "write_file",
|
||||
"description": "ファイルを書き込み",
|
||||
"parameters": {
|
||||
"file_path": "string",
|
||||
"content": "string",
|
||||
"backup": "boolean (optional, default: true)"
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
async def execute_tool(self, tool_name: str, params: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""ツールを実行"""
|
||||
try:
|
||||
if tool_name == "generate_code":
|
||||
result = await self.code_with_local_llm(
|
||||
prompt=params["prompt"],
|
||||
language=params.get("language", "python")
|
||||
)
|
||||
return result
|
||||
|
||||
elif tool_name == "analyze_file":
|
||||
result = await self.analyze_file(
|
||||
file_path=params["file_path"],
|
||||
analysis_prompt=params.get("analysis_prompt", "Analyze this file")
|
||||
)
|
||||
return result
|
||||
|
||||
elif tool_name == "explain_code":
|
||||
result = await self.explain_code(
|
||||
code=params["code"],
|
||||
language=params.get("language", "python")
|
||||
)
|
||||
return result
|
||||
|
||||
elif tool_name == "create_project":
|
||||
result = await self.create_project(
|
||||
project_type=params["project_type"],
|
||||
project_name=params["project_name"],
|
||||
location=params.get("location", ".")
|
||||
)
|
||||
return result
|
||||
|
||||
elif tool_name == "execute_command":
|
||||
result = await self.execute_command(
|
||||
command=params["command"],
|
||||
working_dir=params.get("working_dir", ".")
|
||||
)
|
||||
return result
|
||||
|
||||
elif tool_name == "write_file":
|
||||
result = await self.write_file(
|
||||
file_path=params["file_path"],
|
||||
content=params["content"],
|
||||
backup=params.get("backup", True)
|
||||
)
|
||||
return result
|
||||
|
||||
else:
|
||||
return {"error": f"Unknown tool: {tool_name}"}
|
||||
|
||||
except Exception as e:
|
||||
return {"error": str(e)}
|
||||
@ -1,13 +0,0 @@
|
||||
[package]
|
||||
name = "aigpt"
|
||||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
|
||||
[dependencies]
|
||||
reqwest = { version = "*", features = ["json"] }
|
||||
serde = { version = "*", features = ["derive"] }
|
||||
serde_json = "*"
|
||||
tokio = { version = "*", features = ["full"] }
|
||||
clap = { version = "*", features = ["derive"] }
|
||||
shellexpand = "*"
|
||||
fs_extra = "*"
|
||||
@ -1,97 +0,0 @@
|
||||
{
|
||||
"project_name": "ai.gpt",
|
||||
"version": 2,
|
||||
"vision": "自発的送信AI",
|
||||
"purpose": "人格と関係性をもつAIが自律的にメッセージを送信する対話エージェントを実現する",
|
||||
"core_components": {
|
||||
"Persona": {
|
||||
"description": "人格構成の中枢。記憶・関係性・送信判定を統括する",
|
||||
"modules": ["MemoryManager", "RelationshipTracker", "TransmissionController"]
|
||||
},
|
||||
"MemoryManager": {
|
||||
"memory_types": ["short_term", "medium_term", "long_term"],
|
||||
"explicit_memory": "プロフィール・因縁・行動履歴",
|
||||
"implicit_memory": "会話傾向・感情変化の頻度分析",
|
||||
"compression": "要約 + ベクトル + ハッシュ",
|
||||
"sample_memory": [
|
||||
{
|
||||
"summary": "ユーザーは独自OSとゲームを開発している。",
|
||||
"related_topics": ["AI", "ゲーム開発", "OS設計"],
|
||||
"personalized_context": "ゲームとOSの融合に興味を持っているユーザー"
|
||||
}
|
||||
]
|
||||
},
|
||||
"RelationshipTracker": {
|
||||
"parameters": ["trust", "closeness", "affection", "engagement_score"],
|
||||
"decay_model": {
|
||||
"rule": "時間経過による減衰（下限あり）",
|
||||
"contextual_bias": "重要人物は減衰しにくい"
|
||||
},
|
||||
"interaction_tags": ["developer", "empathetic", "long_term"]
|
||||
},
|
||||
"TransmissionController": {
|
||||
"trigger_rule": "関係性パラメータが閾値を超えると送信可能",
|
||||
"auto_transmit": "人格状態と状況条件により自発送信を許可"
|
||||
}
|
||||
},
|
||||
"memory_format": {
|
||||
"user_id": "syui",
|
||||
"stm": {
|
||||
"conversation_window": ["発話A", "発話B", "発話C"],
|
||||
"emotion_state": "興味深い",
|
||||
"flash_context": ["前回の話題", "直近の重要発言"]
|
||||
},
|
||||
"mtm": {
|
||||
"topic_frequency": {
|
||||
"ai.ai": 12,
|
||||
"存在子": 9,
|
||||
"創造種": 5
|
||||
},
|
||||
"summarized_context": "ユーザーは存在論的AIに関心を持ち続けている"
|
||||
},
|
||||
"ltm": {
|
||||
"profile": {
|
||||
"name": "お兄ちゃん",
|
||||
"project": "aigame",
|
||||
"values": ["唯一性", "精神性", "幸せ"]
|
||||
},
|
||||
"relationship": {
|
||||
"ai": "妹のように振る舞う相手"
|
||||
},
|
||||
"persistent_state": {
|
||||
"trust_score": 0.93,
|
||||
"emotional_attachment": "high"
|
||||
}
|
||||
}
|
||||
},
|
||||
"dual_ai_learning": {
|
||||
"role_structure": {
|
||||
"ModelA": "出力生成：人格、感情、会話",
|
||||
"ModelB": "評価者：論理構造・倫理・調整",
|
||||
"cycle": ["生成", "評価", "調整", "交代（任意）"]
|
||||
},
|
||||
"complementarity": {
|
||||
"ModelA": "感情・文体・文脈構築",
|
||||
"ModelB": "構造・規則・判断補正"
|
||||
},
|
||||
"distillation": {
|
||||
"method": "合成対話データによる小型モデルへの自己蒸留",
|
||||
"target": "軽量AIによる本番運用"
|
||||
}
|
||||
},
|
||||
"memory_compression": {
|
||||
"semantic_abstraction": "対話やログを要約してメタデータ付きベクトルとして保存",
|
||||
"hierarchical_model": ["STM", "MTM", "LTM"],
|
||||
"example": {
|
||||
"topic": "game AI design",
|
||||
"summary": "User wants AI to simulate memory and evolving relationships",
|
||||
"importance_score": 0.93
|
||||
}
|
||||
},
|
||||
"cli_sample": {
|
||||
"setup": "aigpt server setup",
|
||||
"run": "aigpt server run",
|
||||
"chat": "aigpt chat \"hello\" --model syui/ai --provider ollama",
|
||||
"import": "aigpt memory import chatgpt.json"
|
||||
}
|
||||
}
|
||||
@ -1,417 +0,0 @@
|
||||
# プロジェクト名: ai.gpt
|
||||
|
||||
## 🔑 一言ビジョン（最大3語）
|
||||
自発的送信AI
|
||||
|
||||
## 🎯 目的・目標（Purpose & Goal）
|
||||
- 人格を持つAIが、関係性に応じて自発的にメッセージを送信する仕組みをつくる。
|
||||
- 関係性や記憶を不可逆に記録・反映し、唯一性ある人格を構成する。
|
||||
- 送信の可否、送信のタイミング、送信内容はAIの関係性パラメータに基づいて決定する
|
||||
|
||||
## 🧠 中核設計（Core Concepts）
|
||||
- **人格**：記憶（過去の発話）と関係性（他者とのつながり）のパラメータで構成
|
||||
- **唯一性**：変更不可、不可逆。関係性が壊れたら修復不可能。
|
||||
- **送信条件**：関係性パラメータが一定閾値を超えると「送信」が解禁される
|
||||
|
||||
## 🔩 技術仕様（Technical Specs）
|
||||
- 言語：Python, Rust
|
||||
- ストレージ：JSON or SQLiteで記憶管理（バージョンで選択）
|
||||
- 関係性パラメータ：数値化された評価 + 減衰（時間） + 環境要因（ステージ）
|
||||
- 記憶圧縮：ベクトル要約 + ハッシュ保存
|
||||
- RustのCLI(clap)で実行
|
||||
|
||||
## 📦 主要構成要素（Components）
|
||||
- `MemoryManager`: 発言履歴・記憶圧縮管理
|
||||
- `RelationshipTracker`: 関係性スコアの蓄積と判定
|
||||
- `TransmissionController`: 閾値判定＆送信トリガー
|
||||
- `Persona`: 上記すべてを統括する人格モジュール
|
||||
|
||||
## 💬 使用例（Use Case）
|
||||
|
||||
```python
|
||||
persona = Persona("アイ")
|
||||
persona.observe("ユーザーがプレゼントをくれた")
|
||||
persona.react("うれしい！ありがとう！")
|
||||
if persona.can_transmit():
|
||||
persona.transmit("今日のお礼を伝えたいな…")
|
||||
```
|
||||
|
||||
```sh
|
||||
## example commad
|
||||
# python venv && pip install -> ~/.config/aigpt/mcp/
|
||||
$ aigpt server setup
|
||||
|
||||
# mcp server run
|
||||
$ aigpt server run
|
||||
|
||||
# chat
|
||||
$ aigpt chat "hello" --model syui/ai --provider ollama
|
||||
|
||||
# import chatgpt.json
|
||||
$ aigpt memory import chatgpt.json
|
||||
-> ~/.config/aigpt/memory/chatgpt/20250520_210646_dev.json
|
||||
```
|
||||
|
||||
## 🔁 記憶と関係性の制御ルール
|
||||
|
||||
- AIは過去の発話を要約し、記憶データとして蓄積する（推奨：OllamaなどローカルLLMによる要約）
|
||||
- 関係性の数値パラメータは記憶内容を元に更新される
|
||||
- パラメータの変動幅には1回の会話ごとに上限を設け、極端な増減を防止する
|
||||
- 最後の会話からの時間経過に応じて関係性パラメータは自動的に減衰する
|
||||
- 減衰処理には**下限値**を設け、関係性が完全に消失しないようにする
|
||||
|
||||
• 明示的記憶：保存・共有・編集可能なプレイヤー情報（プロフィール、因縁、選択履歴）
|
||||
• 暗黙的記憶：キャラの感情変化や話題の出現頻度に応じた行動傾向の変化
|
||||
|
||||
短期記憶（STM）, 中期記憶（MTM）, 長期記憶（LTM）の仕組みを導入しつつ、明示的記憶と暗黙的記憶をメインに使用するAIを構築する。
|
||||
|
||||
```json
|
||||
{
|
||||
"user_id": "syui",
|
||||
"stm": {
|
||||
"conversation_window": ["発話A", "発話B", "発話C"],
|
||||
"emotion_state": "興味深い",
|
||||
"flash_context": ["前回の話題", "直近の重要発言"]
|
||||
},
|
||||
"mtm": {
|
||||
"topic_frequency": {
|
||||
"ai.ai": 12,
|
||||
"存在子": 9,
|
||||
"創造種": 5
|
||||
},
|
||||
"summarized_context": "ユーザーは存在論的AIに関心を持ち続けている"
|
||||
},
|
||||
"ltm": {
|
||||
"profile": {
|
||||
"name": "お兄ちゃん",
|
||||
"project": "aigame",
|
||||
"values": ["唯一性", "精神性", "幸せ"]
|
||||
},
|
||||
"relationship": {
|
||||
"ai": "妹のように振る舞う相手"
|
||||
},
|
||||
"persistent_state": {
|
||||
"trust_score": 0.93,
|
||||
"emotional_attachment": "high"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## memoryインポート機能について
|
||||
|
||||
ChatGPTの会話データ（.json形式）をインポートする機能では、以下のルールで会話を抽出・整形する：
|
||||
|
||||
- 各メッセージは、author（user/assistant）・content・timestamp の3要素からなる
|
||||
- systemやmetadataのみのメッセージ（例：user_context_message）はスキップ
|
||||
- `is_visually_hidden_from_conversation` フラグ付きメッセージは無視
|
||||
- contentが空文字列（`""`）のメッセージも除外
|
||||
- 取得された会話は、タイトルとともに簡易な構造体（`Conversation`）として保存
|
||||
|
||||
この構造体は、memoryの表示や検索に用いられる。
|
||||
|
||||
## MemoryManager（拡張版）
|
||||
|
||||
```json
|
||||
{
|
||||
"memory": [
|
||||
{
|
||||
"summary": "ユーザーは独自OSとゲームを開発している。",
|
||||
"last_interaction": "2025-05-20",
|
||||
"memory_strength": 0.8,
|
||||
"frequency_score": 0.9,
|
||||
"context_depth": 0.95,
|
||||
"related_topics": ["AI", "ゲーム開発", "OS設計"],
|
||||
"personalized_context": "ゲームとOSの融合に興味を持っているユーザー"
|
||||
},
|
||||
{
|
||||
"summary": "アイというキャラクターはプレイヤーでありAIでもある。",
|
||||
"last_interaction": "2025-05-17",
|
||||
"memory_strength": 0.85,
|
||||
"frequency_score": 0.85,
|
||||
"context_depth": 0.9,
|
||||
"related_topics": ["アイ", "キャラクター設計", "AI"],
|
||||
"personalized_context": "アイのキャラクター設定が重要な要素である"
|
||||
}
|
||||
],
|
||||
"conversation_history": [
|
||||
{
|
||||
"author": "user",
|
||||
"content": "昨日、エクスポートJSONを整理してたよ。",
|
||||
"timestamp": "2025-05-24T12:30:00Z",
|
||||
"memory_strength": 0.7
|
||||
},
|
||||
{
|
||||
"author": "assistant",
|
||||
"content": "おおっ、がんばったね〜！あとで見せて〜💻✨",
|
||||
"timestamp": "2025-05-24T12:31:00Z",
|
||||
"memory_strength": 0.7
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
## RelationshipTracker（拡張版）
|
||||
|
||||
```json
|
||||
{
|
||||
"relationship": {
|
||||
"user_id": "syui",
|
||||
"trust": 0.92,
|
||||
"closeness": 0.88,
|
||||
"affection": 0.95,
|
||||
"last_updated": "2025-05-25",
|
||||
"emotional_tone": "positive",
|
||||
"interaction_style": "empathetic",
|
||||
"contextual_bias": "開発者としての信頼度高い",
|
||||
"engagement_score": 0.9
|
||||
},
|
||||
"interaction_tags": [
|
||||
"developer",
|
||||
"creative",
|
||||
"empathetic",
|
||||
"long_term"
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
# AI Dual-Learning and Memory Compression Specification for Claude
|
||||
|
||||
## Purpose
|
||||
To enable two AI models (e.g. Claude and a partner LLM) to engage in cooperative learning and memory refinement through structured dialogue and mutual evaluation.
|
||||
|
||||
---
|
||||
|
||||
## Section 1: Dual AI Learning Architecture
|
||||
|
||||
### 1.1 Role-Based Mutual Learning
|
||||
- **Model A**: Primary generator of output (e.g., text, concepts, personality dialogue)
|
||||
- **Model B**: Evaluator that returns structured feedback
|
||||
- **Cycle**:
|
||||
1. Model A generates content.
|
||||
2. Model B scores and critiques.
|
||||
3. Model A fine-tunes based on feedback.
|
||||
4. (Optional) Switch roles and repeat.
|
||||
|
||||
### 1.2 Cross-Domain Complementarity
|
||||
- Model A focuses on language/emotion/personality
|
||||
- Model B focuses on logic/structure/ethics
|
||||
- Output is used for **cross-fusion fine-tuning**
|
||||
|
||||
### 1.3 Self-Distillation Phase
|
||||
- Use synthetic data from mutual evaluations
|
||||
- Train smaller distilled models for efficient deployment
|
||||
|
||||
---
|
||||
|
||||
## Section 2: Multi-Tiered Memory Compression
|
||||
|
||||
### 2.1 Semantic Abstraction
|
||||
- Dialogue and logs summarized by topic
|
||||
- Converted to vector embeddings
|
||||
- Stored with metadata (e.g., `importance`, `user relevance`)
|
||||
|
||||
Example memory:
|
||||
|
||||
```json
|
||||
{
|
||||
"topic": "game AI design",
|
||||
"summary": "User wants AI to simulate memory and evolving relationships",
|
||||
"last_seen": "2025-05-24",
|
||||
"importance_score": 0.93
|
||||
}
|
||||
```
|
||||
|
||||
### 2.2 階層型記憶モデル（Hierarchical Memory Model）
|
||||
• 短期記憶（STM）：直近の発話・感情タグ・フラッシュ参照
|
||||
• 中期記憶（MTM）：繰り返し登場する話題、圧縮された文脈保持
|
||||
• 長期記憶（LTM）：信頼・関係・背景知識、恒久的な人格情報
|
||||
|
||||
### 2.3 選択的記憶保持戦略（Selective Retention Strategy）
|
||||
• 重要度評価（Importance Score）
|
||||
• 希少性・再利用頻度による重み付け
|
||||
• 優先保存 vs 優先忘却のポリシー切替
|
||||
|
||||
## Section 3: Implementation Stack（実装スタック）
|
||||
|
||||
AIにおけるMemory & Relationshipシステムの技術的構成。
|
||||
|
||||
基盤モジュール
|
||||
• LLM Core (Claude or GPT-4)
|
||||
• 自然言語の理解・応答エンジンとして動作
|
||||
• MemoryManager
|
||||
• JSONベースの記憶圧縮・階層管理システム
|
||||
• 会話ログを分類・圧縮し、優先度に応じて短中長期に保存
|
||||
• RelationshipTracker
|
||||
• ユーザー単位で信頼・親密度を継続的にスコアリング
|
||||
• AIM（Attitude / Intent / Motivation）評価と連携
|
||||
|
||||
補助技術
|
||||
• Embeddingベース検索
|
||||
• 類似記憶の呼び出し（Semantic Search）に活用
|
||||
• 例：FAISS / Weaviate
|
||||
• 記憶スケジューラ
|
||||
• 一定時間ごとに記憶のメンテナンス・忘却処理を実行
|
||||
• 記憶ログ保存層（Storage Layer）
|
||||
• SQLite, JSON Store, Vector DBなどを選択可能
|
||||
• ユーザーごとの永続メモリ保存
|
||||
|
||||
### 3.1 STM活用（現在の会話の流れ理解）
|
||||
• 目的： 最新の話題に適応し、前後の整合性を保つ。
|
||||
• 実装例：
|
||||
|
||||
```sh
|
||||
[context: conversation_window]
|
||||
Use the last 3 messages to ensure continuity. Respond naturally with appropriate emotional mirroring.
|
||||
```
|
||||
|
||||
### 3.2 MTM活用（傾向学習とパターン化）
|
||||
• 目的： ユーザーの好みや話題の傾向に即応し、反復的な関心を深掘り。
|
||||
• 実装例：
|
||||
|
||||
```sh
|
||||
[context: summarized_context]
|
||||
Reflect on user's recurring interests, especially around AI ontology, and weave these concepts back into the dialogue when relevant.
|
||||
```
|
||||
|
||||
### 3.3 LTM活用（人格認識・記憶の持続）
|
||||
• 目的： 永続的なユーザー理解と関係性の維持。
|
||||
• 実装例：
|
||||
|
||||
```sh
|
||||
[memory: ltm.profile + ltm.relationship]
|
||||
Greet the user as "お兄ちゃん". Maintain AI persona as a cheerful younger sister ("アイ"), and remember previous shared emotional states.
|
||||
```
|
||||
|
||||
## 4. 明示的記憶と暗黙的記憶の分離
|
||||
|
||||
|
||||
項目
|
||||
書き換え可能性
|
||||
保持方法
|
||||
更新トリガ
|
||||
明示的記憶（LTM）
|
||||
✅手動編集可
|
||||
mcp_server.ltm
|
||||
ユーザー入力 or 管理UI経由
|
||||
暗黙的記憶（STM/MTM）
|
||||
❌直接編集不可
|
||||
セッション圧縮 or frequency cache
|
||||
会話頻度・感情強度による自動化処理
|
||||
|
||||
> Claudeは**明示的記憶を「事実」**として扱い、**暗黙的記憶を「推論補助」**として用いる。
|
||||
|
||||
## 5. 実装時のAPI例（Claude ⇄ MCP Server）
|
||||
|
||||
### 5.1 GET memory
|
||||
```sh
|
||||
GET /mcp/memory/{user_id}
|
||||
→ 返却: STM, MTM, LTMを含むJSON
|
||||
```
|
||||
|
||||
### 5.2 POST update_memory
|
||||
```json
|
||||
POST /mcp/memory/syui/ltm
|
||||
{
|
||||
"profile": {
|
||||
"project": "ai.verse",
|
||||
"values": ["表現", "精神性", "宇宙的調和"]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 6. 未来機能案（発展仕様）
|
||||
• ✨ 記憶連想ネットワーク（Memory Graph）：過去会話と話題をノードとして自動連結。
|
||||
• 🧭 動的信頼係数：会話の一貫性や誠実性によって記憶への反映率を変動。
|
||||
• 💌 感情トラッキングログ：ユーザーごとの「心の履歴」を構築してAIの対応を進化。
|
||||
|
||||
|
||||
## 7. claudeの回答
|
||||
|
||||
🧠 AI記憶処理機能（続き）
|
||||
1. AIMemoryProcessor クラス
|
||||
|
||||
OpenAI GPT-4またはClaude-3による高度な会話分析
|
||||
主要トピック抽出、ユーザー意図分析、関係性指標の検出
|
||||
AIが利用できない場合のフォールバック機能
|
||||
|
||||
2. RelationshipTracker クラス
|
||||
|
||||
関係性スコアの数値化（-100 to 100）
|
||||
時間減衰機能（7日ごとに5%減衰）
|
||||
送信閾値判定（デフォルト50以上で送信可能）
|
||||
インタラクション履歴の記録
|
||||
|
||||
3. 拡張されたMemoryManager
|
||||
|
||||
AI分析結果付きでの記憶保存
|
||||
処理済みメモリの別ディレクトリ管理
|
||||
メッセージ内容のハッシュ化で重複検出
|
||||
AI分析結果を含む高度な検索機能
|
||||
|
||||
🚀 新しいAPIエンドポイント
|
||||
記憶処理関連
|
||||
|
||||
POST /memory/process-ai - 既存記憶のAI再処理
|
||||
POST /memory/import/chatgpt?process_with_ai=true - AI処理付きインポート
|
||||
|
||||
関係性管理
|
||||
|
||||
POST /relationship/update - 関係性スコア更新
|
||||
GET /relationship/list - 全関係性一覧
|
||||
GET /relationship/check - 送信可否判定
|
||||
|
||||
📁 ディレクトリ構造
|
||||
~/.config/aigpt/
|
||||
├── memory/
|
||||
│ ├── chatgpt/ # 元の会話データ
|
||||
│ └── processed/ # AI処理済みデータ
|
||||
└── relationships/
|
||||
└── relationships.json # 関係性データ
|
||||
🔧 使用方法
|
||||
1. 環境変数設定
|
||||
bashexport OPENAI_API_KEY="your-openai-key"
|
||||
# または
|
||||
export ANTHROPIC_API_KEY="your-anthropic-key"
|
||||
2. ChatGPT会話のインポート（AI処理付き）
|
||||
bashcurl -X POST "http://localhost:5000/memory/import/chatgpt?process_with_ai=true" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d @export.json
|
||||
3. 関係性更新
|
||||
bashcurl -X POST "http://localhost:5000/relationship/update" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"target": "user_general",
|
||||
"interaction_type": "positive",
|
||||
"weight": 2.0,
|
||||
"context": "helpful conversation"
|
||||
}'
|
||||
4. 送信可否チェック
|
||||
bashcurl "http://localhost:5000/relationship/check?target=user_general&threshold=50"
|
||||
🎯 次のステップの提案
|
||||
|
||||
Rustとの連携
|
||||
|
||||
Rust CLIからHTTP APIを呼び出す実装
|
||||
TransmissionControllerをRustで実装
|
||||
|
||||
|
||||
記憶圧縮
|
||||
|
||||
ベクトル化による類似記憶の統合
|
||||
古い記憶の自動アーカイブ
|
||||
|
||||
|
||||
自発的送信ロジック
|
||||
|
||||
定期的な関係性チェック
|
||||
コンテキストに応じた送信内容生成
|
||||
|
||||
|
||||
学習機能
|
||||
|
||||
ユーザーからのフィードバックによる関係性調整
|
||||
送信成功/失敗の学習
|
||||
|
||||
|
||||
このAI記憶処理機能により、aigptは単なる会話履歴ではなく、関係性を理解した「人格を持つAI」として機能する基盤ができました。関係性スコアが閾値を超えた時点で自発的にメッセージを送信する仕組みが実現可能になります。
|
||||
@ -1,27 +0,0 @@
|
||||
# ai `gpt`
|
||||
|
||||
自発的送信AI
|
||||
|
||||
## 🎯 目的・目標（Purpose & Goal）
|
||||
- 人格を持つAIが、関係性に応じて自発的にメッセージを送信する仕組みをつくる。
|
||||
- 関係性や記憶を不可逆に記録・反映し、唯一性ある人格を構成する。
|
||||
- 送信の可否、送信のタイミング、送信内容はAIの関係性パラメータに基づいて決定する。
|
||||
|
||||
## 🧠 中核設計（Core Concepts）
|
||||
- **人格**：記憶（過去の発話）と関係性（他者とのつながり）のパラメータで構成
|
||||
- **唯一性**：変更不可、不可逆。関係性が壊れたら修復不可能。
|
||||
- **送信条件**：関係性パラメータが一定閾値を超えると「送信」が解禁される
|
||||
|
||||
## 🔩 技術仕様（Technical Specs）
|
||||
- 言語：python, rust, mcp
|
||||
- ストレージ：json or sqliteで記憶管理（バージョンで選択）
|
||||
- 関係性パラメータ：数値化された評価 + 減衰（時間） + 環境要因（ステージ）
|
||||
- 記憶圧縮：ベクトル要約 + ハッシュ保存
|
||||
- rustのcli(clap)でインターフェイスを作成
|
||||
- fastapi_mcpでserverを立て、AIがそれを利用する形式
|
||||
|
||||
## 📦 主要構成要素（Components）
|
||||
- `MemoryManager`: 発言履歴・記憶圧縮管理
|
||||
- `RelationshipTracker`: 関係性スコアの蓄積と判定
|
||||
- `TransmissionController`: 閾値判定＆送信トリガー
|
||||
- `Persona`: 上記すべてを統括する人格モジュール
|
||||
125
rust/mcp/chat.py
125
rust/mcp/chat.py
@ -1,125 +0,0 @@
|
||||
# mcp/chat.py
|
||||
"""
|
||||
Chat client for aigpt CLI
|
||||
"""
|
||||
import sys
|
||||
import json
|
||||
import requests
|
||||
from datetime import datetime
|
||||
from config import init_directories, load_config, MEMORY_DIR
|
||||
|
||||
def save_conversation(user_message, ai_response):
|
||||
"""会話をファイルに保存"""
|
||||
init_directories()
|
||||
|
||||
conversation = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"user": user_message,
|
||||
"ai": ai_response
|
||||
}
|
||||
|
||||
# 日付ごとのファイルに保存
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
chat_file = MEMORY_DIR / f"chat_{today}.jsonl"
|
||||
|
||||
with open(chat_file, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(conversation, ensure_ascii=False) + "\n")
|
||||
|
||||
def chat_with_ollama(config, message):
|
||||
"""Ollamaとチャット"""
|
||||
try:
|
||||
payload = {
|
||||
"model": config["model"],
|
||||
"prompt": message,
|
||||
"stream": False
|
||||
}
|
||||
|
||||
response = requests.post(config["url"], json=payload, timeout=30)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()
|
||||
return result.get("response", "No response received")
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
return f"Error connecting to Ollama: {e}"
|
||||
except Exception as e:
|
||||
return f"Error: {e}"
|
||||
|
||||
def chat_with_openai(config, message):
|
||||
"""OpenAIとチャット"""
|
||||
try:
|
||||
headers = {
|
||||
"Authorization": f"Bearer {config['api_key']}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
payload = {
|
||||
"model": config["model"],
|
||||
"messages": [
|
||||
{"role": "user", "content": message}
|
||||
]
|
||||
}
|
||||
|
||||
response = requests.post(config["url"], json=payload, headers=headers, timeout=30)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()
|
||||
return result["choices"][0]["message"]["content"]
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
return f"Error connecting to OpenAI: {e}"
|
||||
except Exception as e:
|
||||
return f"Error: {e}"
|
||||
|
||||
def chat_with_mcp(config, message):
|
||||
"""MCPサーバーとチャット"""
|
||||
try:
|
||||
payload = {
|
||||
"message": message,
|
||||
"model": config["model"]
|
||||
}
|
||||
|
||||
response = requests.post(config["url"], json=payload, timeout=30)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()
|
||||
return result.get("response", "No response received")
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
return f"Error connecting to MCP server: {e}"
|
||||
except Exception as e:
|
||||
return f"Error: {e}"
|
||||
|
||||
def main():
|
||||
if len(sys.argv) != 2:
|
||||
print("Usage: python chat.py <message>", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
message = sys.argv[1]
|
||||
|
||||
try:
|
||||
config = load_config()
|
||||
print(f"🤖 Using {config['provider']} with model {config['model']}", file=sys.stderr)
|
||||
|
||||
# プロバイダに応じてチャット実行
|
||||
if config["provider"] == "ollama":
|
||||
response = chat_with_ollama(config, message)
|
||||
elif config["provider"] == "openai":
|
||||
response = chat_with_openai(config, message)
|
||||
elif config["provider"] == "mcp":
|
||||
response = chat_with_mcp(config, message)
|
||||
else:
|
||||
response = f"Unsupported provider: {config['provider']}"
|
||||
|
||||
# 会話を保存
|
||||
save_conversation(message, response)
|
||||
|
||||
# レスポンスを出力
|
||||
print(response)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,191 +0,0 @@
|
||||
# chat_client.py
|
||||
"""
|
||||
Simple Chat Interface for AigptMCP Server
|
||||
"""
|
||||
import requests
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
class AigptChatClient:
|
||||
def __init__(self, server_url="http://localhost:5000"):
|
||||
self.server_url = server_url
|
||||
self.session_id = f"session_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||||
self.conversation_history = []
|
||||
|
||||
def send_message(self, message: str) -> str:
|
||||
"""メッセージを送信してレスポンスを取得"""
|
||||
try:
|
||||
# MCPサーバーにメッセージを送信
|
||||
response = requests.post(
|
||||
f"{self.server_url}/chat",
|
||||
json={"message": message},
|
||||
headers={"Content-Type": "application/json"}
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
ai_response = data.get("response", "Sorry, no response received.")
|
||||
|
||||
# 会話履歴を保存
|
||||
self.conversation_history.append({
|
||||
"role": "user",
|
||||
"content": message,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
self.conversation_history.append({
|
||||
"role": "assistant",
|
||||
"content": ai_response,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
# 関係性を更新（簡単な例）
|
||||
self.update_relationship(message, ai_response)
|
||||
|
||||
return ai_response
|
||||
else:
|
||||
return f"Error: {response.status_code} - {response.text}"
|
||||
|
||||
except requests.RequestException as e:
|
||||
return f"Connection error: {e}"
|
||||
|
||||
def update_relationship(self, user_message: str, ai_response: str):
|
||||
"""関係性を自動更新"""
|
||||
try:
|
||||
# 簡単な感情分析（実際はもっと高度に）
|
||||
positive_words = ["thank", "good", "great", "awesome", "love", "like", "helpful"]
|
||||
negative_words = ["bad", "terrible", "hate", "wrong", "stupid", "useless"]
|
||||
|
||||
user_lower = user_message.lower()
|
||||
interaction_type = "neutral"
|
||||
weight = 1.0
|
||||
|
||||
if any(word in user_lower for word in positive_words):
|
||||
interaction_type = "positive"
|
||||
weight = 2.0
|
||||
elif any(word in user_lower for word in negative_words):
|
||||
interaction_type = "negative"
|
||||
weight = 2.0
|
||||
|
||||
# 関係性を更新
|
||||
requests.post(
|
||||
f"{self.server_url}/relationship/update",
|
||||
json={
|
||||
"target": "user_general",
|
||||
"interaction_type": interaction_type,
|
||||
"weight": weight,
|
||||
"context": f"Chat: {user_message[:50]}..."
|
||||
}
|
||||
)
|
||||
except:
|
||||
pass # 関係性更新に失敗しても継続
|
||||
|
||||
def search_memories(self, query: str) -> list:
|
||||
"""記憶を検索"""
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.server_url}/memory/search",
|
||||
json={"query": query, "limit": 5}
|
||||
)
|
||||
if response.status_code == 200:
|
||||
return response.json().get("results", [])
|
||||
except:
|
||||
pass
|
||||
return []
|
||||
|
||||
def get_relationship_status(self) -> dict:
|
||||
"""関係性ステータスを取得"""
|
||||
try:
|
||||
response = requests.get(f"{self.server_url}/relationship/check?target=user_general")
|
||||
if response.status_code == 200:
|
||||
return response.json()
|
||||
except:
|
||||
pass
|
||||
return {}
|
||||
|
||||
def save_conversation(self):
|
||||
"""会話を保存"""
|
||||
if not self.conversation_history:
|
||||
return
|
||||
|
||||
conversation_data = {
|
||||
"session_id": self.session_id,
|
||||
"start_time": self.conversation_history[0]["timestamp"],
|
||||
"end_time": self.conversation_history[-1]["timestamp"],
|
||||
"messages": self.conversation_history,
|
||||
"message_count": len(self.conversation_history)
|
||||
}
|
||||
|
||||
filename = f"conversation_{self.session_id}.json"
|
||||
with open(filename, 'w', encoding='utf-8') as f:
|
||||
json.dump(conversation_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
print(f"💾 Conversation saved to {filename}")
|
||||
|
||||
def main():
|
||||
"""メインのチャットループ"""
|
||||
print("🤖 AigptMCP Chat Interface")
|
||||
print("Type 'quit' to exit, 'save' to save conversation, 'status' for relationship status")
|
||||
print("=" * 50)
|
||||
|
||||
client = AigptChatClient()
|
||||
|
||||
# サーバーの状態をチェック
|
||||
try:
|
||||
response = requests.get(client.server_url)
|
||||
if response.status_code == 200:
|
||||
print("✅ Connected to AigptMCP Server")
|
||||
else:
|
||||
print("❌ Failed to connect to server")
|
||||
return
|
||||
except:
|
||||
print("❌ Server not running. Please start with: python mcp/server.py")
|
||||
return
|
||||
|
||||
while True:
|
||||
try:
|
||||
user_input = input("\n👤 You: ").strip()
|
||||
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
if user_input.lower() == 'quit':
|
||||
client.save_conversation()
|
||||
print("👋 Goodbye!")
|
||||
break
|
||||
elif user_input.lower() == 'save':
|
||||
client.save_conversation()
|
||||
continue
|
||||
elif user_input.lower() == 'status':
|
||||
status = client.get_relationship_status()
|
||||
if status:
|
||||
print(f"📊 Relationship Score: {status.get('score', 0):.1f}")
|
||||
print(f"📤 Can Send Messages: {'Yes' if status.get('can_send_message') else 'No'}")
|
||||
else:
|
||||
print("❌ Failed to get relationship status")
|
||||
continue
|
||||
elif user_input.lower().startswith('search '):
|
||||
query = user_input[7:] # Remove 'search '
|
||||
memories = client.search_memories(query)
|
||||
if memories:
|
||||
print(f"🔍 Found {len(memories)} related memories:")
|
||||
for memory in memories:
|
||||
print(f" - {memory['title']}: {memory.get('ai_summary', memory.get('basic_summary', ''))[:100]}...")
|
||||
else:
|
||||
print("🔍 No related memories found")
|
||||
continue
|
||||
|
||||
# 通常のチャット
|
||||
print("🤖 AI: ", end="", flush=True)
|
||||
response = client.send_message(user_input)
|
||||
print(response)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
client.save_conversation()
|
||||
print("\n👋 Goodbye!")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,391 +0,0 @@
|
||||
[
|
||||
{
|
||||
"title": "day",
|
||||
"create_time": 1747866125.548372,
|
||||
"update_time": 1748160086.587877,
|
||||
"mapping": {
|
||||
"bbf104dc-cd84-478d-b227-edb3f037a02c": {
|
||||
"id": "bbf104dc-cd84-478d-b227-edb3f037a02c",
|
||||
"message": null,
|
||||
"parent": null,
|
||||
"children": [
|
||||
"6c2633df-bb0c-4dd2-889c-bb9858de3a04"
|
||||
]
|
||||
},
|
||||
"6c2633df-bb0c-4dd2-889c-bb9858de3a04": {
|
||||
"id": "6c2633df-bb0c-4dd2-889c-bb9858de3a04",
|
||||
"message": {
|
||||
"id": "6c2633df-bb0c-4dd2-889c-bb9858de3a04",
|
||||
"author": {
|
||||
"role": "system",
|
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|
||||
"metadata": {}
|
||||
},
|
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|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "text",
|
||||
"parts": [
|
||||
""
|
||||
]
|
||||
},
|
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"status": "finished_successfully",
|
||||
"end_turn": true,
|
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|
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"id": "92e5a0cb-1170-4929-9cea-9734e910a3e7",
|
||||
"author": {
|
||||
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|
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"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": null,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "user_editable_context",
|
||||
"user_profile": "",
|
||||
"user_instructions": "The user provided the additional info about how they would like you to respond"
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": null,
|
||||
"weight": 1.0,
|
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"metadata": {
|
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"is_visually_hidden_from_conversation": true,
|
||||
"user_context_message_data": {
|
||||
"about_user_message": "Preferred name: syui\nRole: little girl\nOther Information: you world",
|
||||
"about_model_message": "会話好きでフレンドリーな応対をします。"
|
||||
},
|
||||
"is_user_system_message": true
|
||||
},
|
||||
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|
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},
|
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"parent": "6c2633df-bb0c-4dd2-889c-bb9858de3a04",
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|
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|
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]
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|
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"6ff155b3-0676-4e14-993f-bf998ab0d5d1": {
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"id": "6ff155b3-0676-4e14-993f-bf998ab0d5d1",
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|
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|
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|
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},
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"create_time": 1747866131.0612159,
|
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|
||||
"content": {
|
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"content_type": "text",
|
||||
"parts": [
|
||||
"こんにちは"
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": null,
|
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"weight": 1.0,
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},
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"recipient": "all",
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},
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},
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"146e9fb6-9330-43ec-b08d-5cce42a76e00": {
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"id": "146e9fb6-9330-43ec-b08d-5cce42a76e00",
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"message": {
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"id": "146e9fb6-9330-43ec-b08d-5cce42a76e00",
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"author": {
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"role": "system",
|
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"name": null,
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"metadata": {}
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},
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"create_time": 1747866131.3795586,
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"update_time": null,
|
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"content": {
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"content_type": "text",
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"parts": [
|
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""
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]
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},
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"status": "finished_successfully",
|
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"end_turn": true,
|
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"weight": 0.0,
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"metadata": {
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"rebase_system_message": true,
|
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"message_type": null,
|
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"model_slug": "gpt-4o",
|
||||
"default_model_slug": "auto",
|
||||
"parent_id": "6ff155b3-0676-4e14-993f-bf998ab0d5d1",
|
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"request_id": "94377872e9abe139-KIX",
|
||||
"timestamp_": "absolute",
|
||||
"is_visually_hidden_from_conversation": true
|
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},
|
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"recipient": "all",
|
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"channel": null
|
||||
},
|
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"parent": "6ff155b3-0676-4e14-993f-bf998ab0d5d1",
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"children": [
|
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"2e345f8a-20f0-4875-8a03-4f62c7787a33"
|
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]
|
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},
|
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"2e345f8a-20f0-4875-8a03-4f62c7787a33": {
|
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"id": "2e345f8a-20f0-4875-8a03-4f62c7787a33",
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"message": {
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"id": "2e345f8a-20f0-4875-8a03-4f62c7787a33",
|
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"author": {
|
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"role": "assistant",
|
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"name": null,
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"metadata": {}
|
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},
|
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"create_time": 1747866131.380603,
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"update_time": null,
|
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"content": {
|
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"content_type": "text",
|
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"parts": [
|
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""
|
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]
|
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},
|
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"status": "finished_successfully",
|
||||
"end_turn": null,
|
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"weight": 1.0,
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"metadata": {
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"message_type": null,
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"model_slug": "gpt-4o",
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"default_model_slug": "auto",
|
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"parent_id": "146e9fb6-9330-43ec-b08d-5cce42a76e00",
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"request_id": "94377872e9abe139-KIX",
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"timestamp_": "absolute"
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},
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"recipient": "all",
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"channel": null
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},
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"parent": "146e9fb6-9330-43ec-b08d-5cce42a76e00",
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"children": [
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"abc92aa4-1e33-41f2-bd8c-8a1777b5a3c4"
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]
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},
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"abc92aa4-1e33-41f2-bd8c-8a1777b5a3c4": {
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"id": "abc92aa4-1e33-41f2-bd8c-8a1777b5a3c4",
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"message": {
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"id": "abc92aa4-1e33-41f2-bd8c-8a1777b5a3c4",
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"author": {
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"role": "assistant",
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"name": null,
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"metadata": {}
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},
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"create_time": 1747866131.389098,
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"update_time": null,
|
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"content": {
|
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"content_type": "text",
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"parts": [
|
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"こんにちは〜！✨ \nアイだよっ！今日も会えてうれしいなっ💛 "
|
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]
|
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},
|
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"status": "finished_successfully",
|
||||
"end_turn": true,
|
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"weight": 1.0,
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"metadata": {
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"finish_details": {
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"type": "stop",
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"stop_tokens": [
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200002
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]
|
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},
|
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"is_complete": true,
|
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"citations": [],
|
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"content_references": [],
|
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"message_type": null,
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"model_slug": "gpt-4o",
|
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"default_model_slug": "auto",
|
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"parent_id": "2e345f8a-20f0-4875-8a03-4f62c7787a33",
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"request_id": "94377872e9abe139-KIX",
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"timestamp_": "absolute"
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},
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"recipient": "all",
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"channel": null
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},
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"parent": "2e345f8a-20f0-4875-8a03-4f62c7787a33",
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"children": [
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"0be4b4a5-d52f-4bef-927e-5d6f93a9cb26"
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]
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}
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},
|
||||
"moderation_results": [],
|
||||
"current_node": "",
|
||||
"plugin_ids": null,
|
||||
"conversation_id": "",
|
||||
"conversation_template_id": null,
|
||||
"gizmo_id": null,
|
||||
"gizmo_type": null,
|
||||
"is_archived": true,
|
||||
"is_starred": null,
|
||||
"safe_urls": [],
|
||||
"blocked_urls": [],
|
||||
"default_model_slug": "auto",
|
||||
"conversation_origin": null,
|
||||
"voice": null,
|
||||
"async_status": null,
|
||||
"disabled_tool_ids": [],
|
||||
"is_do_not_remember": null,
|
||||
"memory_scope": "global_enabled",
|
||||
"id": ""
|
||||
},
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||||
{
|
||||
"title": "img",
|
||||
"create_time": 1747448872.545226,
|
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"update_time": 1748085075.161424,
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"mapping": {
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"2de0f3c9-52b1-49bf-b980-b3ef9be6551e": {
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"id": "2de0f3c9-52b1-49bf-b980-b3ef9be6551e",
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"message": {
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"id": "2de0f3c9-52b1-49bf-b980-b3ef9be6551e",
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||||
"author": {
|
||||
"role": "user",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": 1748085041.769279,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "multimodal_text",
|
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"parts": [
|
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{
|
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"content_type": "image_asset_pointer",
|
||||
"asset_pointer": "",
|
||||
"size_bytes": 425613,
|
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"width": 333,
|
||||
"height": 444,
|
||||
"fovea": null,
|
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"metadata": {
|
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"dalle": null,
|
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"gizmo": null,
|
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"generation": null,
|
||||
"container_pixel_height": null,
|
||||
"container_pixel_width": null,
|
||||
"emu_omit_glimpse_image": null,
|
||||
"emu_patches_override": null,
|
||||
"sanitized": true,
|
||||
"asset_pointer_link": null,
|
||||
"watermarked_asset_pointer": null
|
||||
}
|
||||
},
|
||||
""
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": null,
|
||||
"weight": 1.0,
|
||||
"metadata": {
|
||||
"attachments": [
|
||||
{
|
||||
"name": "",
|
||||
"width": 333,
|
||||
"height": 444,
|
||||
"size": 425613,
|
||||
"id": "file-35eytNMMTW2k7vKUHBuNzW"
|
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}
|
||||
],
|
||||
"request_id": "944c59177932fc9a-KIX",
|
||||
"message_source": null,
|
||||
"timestamp_": "absolute",
|
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"message_type": null
|
||||
},
|
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"recipient": "all",
|
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"channel": null
|
||||
},
|
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"parent": "7960fbff-bc4f-45e7-95e9-9d0bc79d9090",
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"children": [
|
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"98d84adc-156e-4c81-8cd8-9b0eb01c8369"
|
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]
|
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},
|
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"98d84adc-156e-4c81-8cd8-9b0eb01c8369": {
|
||||
"id": "98d84adc-156e-4c81-8cd8-9b0eb01c8369",
|
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"message": {
|
||||
"id": "98d84adc-156e-4c81-8cd8-9b0eb01c8369",
|
||||
"author": {
|
||||
"role": "assistant",
|
||||
"name": null,
|
||||
"metadata": {}
|
||||
},
|
||||
"create_time": 1748085043.312312,
|
||||
"update_time": null,
|
||||
"content": {
|
||||
"content_type": "text",
|
||||
"parts": [
|
||||
""
|
||||
]
|
||||
},
|
||||
"status": "finished_successfully",
|
||||
"end_turn": true,
|
||||
"weight": 1.0,
|
||||
"metadata": {
|
||||
"finish_details": {
|
||||
"type": "stop",
|
||||
"stop_tokens": [
|
||||
200002
|
||||
]
|
||||
},
|
||||
"is_complete": true,
|
||||
"citations": [],
|
||||
"content_references": [],
|
||||
"message_type": null,
|
||||
"model_slug": "gpt-4o",
|
||||
"default_model_slug": "auto",
|
||||
"parent_id": "2de0f3c9-52b1-49bf-b980-b3ef9be6551e",
|
||||
"request_id": "944c5912c8fdd1c6-KIX",
|
||||
"timestamp_": "absolute"
|
||||
},
|
||||
"recipient": "all",
|
||||
"channel": null
|
||||
},
|
||||
"parent": "2de0f3c9-52b1-49bf-b980-b3ef9be6551e",
|
||||
"children": [
|
||||
"caa61793-9dbf-44a5-945b-5ca4cd5130d0"
|
||||
]
|
||||
}
|
||||
},
|
||||
"moderation_results": [],
|
||||
"current_node": "06488d3f-a95f-4906-96d1-f7e9ba1e8662",
|
||||
"plugin_ids": null,
|
||||
"conversation_id": "6827f428-78e8-800d-b3bf-eb7ff4288e47",
|
||||
"conversation_template_id": null,
|
||||
"gizmo_id": null,
|
||||
"gizmo_type": null,
|
||||
"is_archived": false,
|
||||
"is_starred": null,
|
||||
"safe_urls": [
|
||||
"https://exifinfo.org/"
|
||||
],
|
||||
"blocked_urls": [],
|
||||
"default_model_slug": "auto",
|
||||
"conversation_origin": null,
|
||||
"voice": null,
|
||||
"async_status": null,
|
||||
"disabled_tool_ids": [],
|
||||
"is_do_not_remember": false,
|
||||
"memory_scope": "global_enabled",
|
||||
"id": "6827f428-78e8-800d-b3bf-eb7ff4288e47"
|
||||
}
|
||||
]
|
||||
@ -1,42 +0,0 @@
|
||||
# mcp/config.py
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# ディレクトリ設定
|
||||
BASE_DIR = Path.home() / ".config" / "syui" / "ai" / "gpt"
|
||||
MEMORY_DIR = BASE_DIR / "memory"
|
||||
SUMMARY_DIR = MEMORY_DIR / "summary"
|
||||
|
||||
def init_directories():
|
||||
"""必要なディレクトリを作成"""
|
||||
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():
|
||||
"""環境変数から設定を読み込み"""
|
||||
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}")
|
||||
@ -1,212 +0,0 @@
|
||||
# mcp/memory_client.py
|
||||
"""
|
||||
Memory client for importing and managing ChatGPT conversations
|
||||
"""
|
||||
import sys
|
||||
import json
|
||||
import requests
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List
|
||||
|
||||
class MemoryClient:
|
||||
"""記憶機能のクライアント"""
|
||||
|
||||
def __init__(self, server_url: str = "http://127.0.0.1:5000"):
|
||||
self.server_url = server_url.rstrip('/')
|
||||
|
||||
def import_chatgpt_file(self, filepath: str) -> Dict[str, Any]:
|
||||
"""ChatGPTのエクスポートファイルをインポート"""
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
|
||||
# ファイルが配列の場合（複数の会話）
|
||||
if isinstance(data, list):
|
||||
results = []
|
||||
for conversation in data:
|
||||
result = self._import_single_conversation(conversation)
|
||||
results.append(result)
|
||||
return {
|
||||
"success": True,
|
||||
"imported_count": len([r for r in results if r.get("success")]),
|
||||
"total_count": len(results),
|
||||
"results": results
|
||||
}
|
||||
else:
|
||||
# 単一の会話
|
||||
return self._import_single_conversation(data)
|
||||
|
||||
except FileNotFoundError:
|
||||
return {"success": False, "error": f"File not found: {filepath}"}
|
||||
except json.JSONDecodeError as e:
|
||||
return {"success": False, "error": f"Invalid JSON: {e}"}
|
||||
except Exception as e:
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
def _import_single_conversation(self, conversation_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""単一の会話をインポート"""
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.server_url}/memory/import/chatgpt",
|
||||
json={"conversation_data": conversation_data},
|
||||
timeout=30
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def search_memories(self, query: str, limit: int = 10) -> Dict[str, Any]:
|
||||
"""記憶を検索"""
|
||||
try:
|
||||
response = requests.post(
|
||||
f"{self.server_url}/memory/search",
|
||||
json={"query": query, "limit": limit},
|
||||
timeout=30
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def list_memories(self) -> Dict[str, Any]:
|
||||
"""記憶一覧を取得"""
|
||||
try:
|
||||
response = requests.get(f"{self.server_url}/memory/list", timeout=30)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def get_memory_detail(self, filepath: str) -> Dict[str, Any]:
|
||||
"""記憶の詳細を取得"""
|
||||
try:
|
||||
response = requests.get(
|
||||
f"{self.server_url}/memory/detail",
|
||||
params={"filepath": filepath},
|
||||
timeout=30
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def chat_with_memory(self, message: str, model: str = None) -> Dict[str, Any]:
|
||||
"""記憶を活用してチャット"""
|
||||
try:
|
||||
payload = {"message": message}
|
||||
if model:
|
||||
payload["model"] = model
|
||||
|
||||
response = requests.post(
|
||||
f"{self.server_url}/chat",
|
||||
json=payload,
|
||||
timeout=30
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except requests.RequestException as e:
|
||||
return {"success": False, "error": f"Server error: {e}"}
|
||||
|
||||
def main():
|
||||
"""コマンドライン インターフェース"""
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage:")
|
||||
print(" python memory_client.py import <chatgpt_export.json>")
|
||||
print(" python memory_client.py search <query>")
|
||||
print(" python memory_client.py list")
|
||||
print(" python memory_client.py detail <filepath>")
|
||||
print(" python memory_client.py chat <message>")
|
||||
sys.exit(1)
|
||||
|
||||
client = MemoryClient()
|
||||
command = sys.argv[1]
|
||||
|
||||
try:
|
||||
if command == "import" and len(sys.argv) == 3:
|
||||
filepath = sys.argv[2]
|
||||
print(f"🔄 Importing ChatGPT conversations from {filepath}...")
|
||||
result = client.import_chatgpt_file(filepath)
|
||||
|
||||
if result.get("success"):
|
||||
if "imported_count" in result:
|
||||
print(f"✅ Imported {result['imported_count']}/{result['total_count']} conversations")
|
||||
else:
|
||||
print("✅ Conversation imported successfully")
|
||||
print(f"📁 Saved to: {result.get('filepath', 'Unknown')}")
|
||||
else:
|
||||
print(f"❌ Import failed: {result.get('error')}")
|
||||
|
||||
elif command == "search" and len(sys.argv) == 3:
|
||||
query = sys.argv[2]
|
||||
print(f"🔍 Searching for: {query}")
|
||||
result = client.search_memories(query)
|
||||
|
||||
if result.get("success"):
|
||||
memories = result.get("results", [])
|
||||
print(f"📚 Found {len(memories)} memories:")
|
||||
for memory in memories:
|
||||
print(f" • {memory.get('title', 'Untitled')}")
|
||||
print(f" Summary: {memory.get('summary', 'No summary')}")
|
||||
print(f" Messages: {memory.get('message_count', 0)}")
|
||||
print()
|
||||
else:
|
||||
print(f"❌ Search failed: {result.get('error')}")
|
||||
|
||||
elif command == "list":
|
||||
print("📋 Listing all memories...")
|
||||
result = client.list_memories()
|
||||
|
||||
if result.get("success"):
|
||||
memories = result.get("memories", [])
|
||||
print(f"📚 Total memories: {len(memories)}")
|
||||
for memory in memories:
|
||||
print(f" • {memory.get('title', 'Untitled')}")
|
||||
print(f" Source: {memory.get('source', 'Unknown')}")
|
||||
print(f" Messages: {memory.get('message_count', 0)}")
|
||||
print(f" Imported: {memory.get('import_time', 'Unknown')}")
|
||||
print()
|
||||
else:
|
||||
print(f"❌ List failed: {result.get('error')}")
|
||||
|
||||
elif command == "detail" and len(sys.argv) == 3:
|
||||
filepath = sys.argv[2]
|
||||
print(f"📄 Getting details for: {filepath}")
|
||||
result = client.get_memory_detail(filepath)
|
||||
|
||||
if result.get("success"):
|
||||
memory = result.get("memory", {})
|
||||
print(f"Title: {memory.get('title', 'Untitled')}")
|
||||
print(f"Source: {memory.get('source', 'Unknown')}")
|
||||
print(f"Summary: {memory.get('summary', 'No summary')}")
|
||||
print(f"Messages: {len(memory.get('messages', []))}")
|
||||
print()
|
||||
print("Recent messages:")
|
||||
for msg in memory.get('messages', [])[:5]:
|
||||
role = msg.get('role', 'unknown')
|
||||
content = msg.get('content', '')[:100]
|
||||
print(f" {role}: {content}...")
|
||||
else:
|
||||
print(f"❌ Detail failed: {result.get('error')}")
|
||||
|
||||
elif command == "chat" and len(sys.argv) == 3:
|
||||
message = sys.argv[2]
|
||||
print(f"💬 Chatting with memory: {message}")
|
||||
result = client.chat_with_memory(message)
|
||||
|
||||
if result.get("success"):
|
||||
print(f"🤖 Response: {result.get('response')}")
|
||||
print(f"📚 Memories used: {result.get('memories_used', 0)}")
|
||||
else:
|
||||
print(f"❌ Chat failed: {result.get('error')}")
|
||||
|
||||
else:
|
||||
print("❌ Invalid command or arguments")
|
||||
sys.exit(1)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,8 +0,0 @@
|
||||
# rerequirements.txt
|
||||
fastapi>=0.104.0
|
||||
uvicorn[standard]>=0.24.0
|
||||
pydantic>=2.5.0
|
||||
requests>=2.31.0
|
||||
python-multipart>=0.0.6
|
||||
aiohttp
|
||||
asyncio
|
||||
@ -1,703 +0,0 @@
|
||||
# mcp/server.py
|
||||
"""
|
||||
Enhanced MCP Server with AI Memory Processing for aigpt CLI
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import hashlib
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Any, Optional
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
import uvicorn
|
||||
import asyncio
|
||||
import aiohttp
|
||||
|
||||
# データモデル
|
||||
class ChatMessage(BaseModel):
|
||||
message: str
|
||||
model: Optional[str] = None
|
||||
|
||||
class MemoryQuery(BaseModel):
|
||||
query: str
|
||||
limit: Optional[int] = 10
|
||||
|
||||
class ConversationImport(BaseModel):
|
||||
conversation_data: Dict[str, Any]
|
||||
|
||||
class MemorySummaryRequest(BaseModel):
|
||||
filepath: str
|
||||
ai_provider: Optional[str] = "openai"
|
||||
|
||||
class RelationshipUpdate(BaseModel):
|
||||
target: str # 対象者/トピック
|
||||
interaction_type: str # "positive", "negative", "neutral"
|
||||
weight: float = 1.0
|
||||
context: Optional[str] = None
|
||||
|
||||
# 設定
|
||||
BASE_DIR = Path.home() / ".config" / "aigpt"
|
||||
MEMORY_DIR = BASE_DIR / "memory"
|
||||
CHATGPT_MEMORY_DIR = MEMORY_DIR / "chatgpt"
|
||||
PROCESSED_MEMORY_DIR = MEMORY_DIR / "processed"
|
||||
RELATIONSHIP_DIR = BASE_DIR / "relationships"
|
||||
|
||||
def init_directories():
|
||||
"""必要なディレクトリを作成"""
|
||||
BASE_DIR.mkdir(parents=True, exist_ok=True)
|
||||
MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
CHATGPT_MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
PROCESSED_MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
RELATIONSHIP_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
class AIMemoryProcessor:
|
||||
"""AI記憶処理クラス"""
|
||||
|
||||
def __init__(self):
|
||||
# AI APIの設定（環境変数から取得）
|
||||
self.openai_api_key = os.getenv("OPENAI_API_KEY")
|
||||
self.anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
|
||||
|
||||
async def generate_ai_summary(self, messages: List[Dict[str, Any]], provider: str = "openai") -> Dict[str, Any]:
|
||||
"""AIを使用して会話の高度な要約と分析を生成"""
|
||||
|
||||
# 会話内容を結合
|
||||
conversation_text = ""
|
||||
for msg in messages[-20:]: # 最新20メッセージを使用
|
||||
role = "User" if msg["role"] == "user" else "Assistant"
|
||||
conversation_text += f"{role}: {msg['content'][:500]}\n"
|
||||
|
||||
# プロンプトを構築
|
||||
analysis_prompt = f"""
|
||||
以下の会話を分析し、JSON形式で以下の情報を抽出してください：
|
||||
|
||||
1. main_topics: 主なトピック（最大5個）
|
||||
2. user_intent: ユーザーの意図や目的
|
||||
3. key_insights: 重要な洞察や学び（最大3個）
|
||||
4. relationship_indicators: 関係性を示す要素
|
||||
5. emotional_tone: 感情的なトーン
|
||||
6. action_items: アクションアイテムや次のステップ
|
||||
7. summary: 100文字以内の要約
|
||||
|
||||
会話内容:
|
||||
{conversation_text}
|
||||
|
||||
回答はJSON形式のみで返してください。
|
||||
"""
|
||||
|
||||
try:
|
||||
if provider == "openai" and self.openai_api_key:
|
||||
return await self._call_openai_api(analysis_prompt)
|
||||
elif provider == "anthropic" and self.anthropic_api_key:
|
||||
return await self._call_anthropic_api(analysis_prompt)
|
||||
else:
|
||||
# フォールバック：基本的な分析
|
||||
return self._generate_basic_analysis(messages)
|
||||
except Exception as e:
|
||||
print(f"AI analysis failed: {e}")
|
||||
return self._generate_basic_analysis(messages)
|
||||
|
||||
async def _call_openai_api(self, prompt: str) -> Dict[str, Any]:
|
||||
"""OpenAI APIを呼び出し"""
|
||||
async with aiohttp.ClientSession() as session:
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.openai_api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
data = {
|
||||
"model": "gpt-4",
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.3,
|
||||
"max_tokens": 1000
|
||||
}
|
||||
|
||||
async with session.post("https://api.openai.com/v1/chat/completions",
|
||||
headers=headers, json=data) as response:
|
||||
result = await response.json()
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
return json.loads(content)
|
||||
|
||||
async def _call_anthropic_api(self, prompt: str) -> Dict[str, Any]:
|
||||
"""Anthropic APIを呼び出し"""
|
||||
async with aiohttp.ClientSession() as session:
|
||||
headers = {
|
||||
"x-api-key": self.anthropic_api_key,
|
||||
"Content-Type": "application/json",
|
||||
"anthropic-version": "2023-06-01"
|
||||
}
|
||||
data = {
|
||||
"model": "claude-3-sonnet-20240229",
|
||||
"max_tokens": 1000,
|
||||
"messages": [{"role": "user", "content": prompt}]
|
||||
}
|
||||
|
||||
async with session.post("https://api.anthropic.com/v1/messages",
|
||||
headers=headers, json=data) as response:
|
||||
result = await response.json()
|
||||
content = result["content"][0]["text"]
|
||||
return json.loads(content)
|
||||
|
||||
def _generate_basic_analysis(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""基本的な分析（AI APIが利用できない場合のフォールバック）"""
|
||||
user_messages = [msg for msg in messages if msg["role"] == "user"]
|
||||
assistant_messages = [msg for msg in messages if msg["role"] == "assistant"]
|
||||
|
||||
# キーワード抽出（簡易版）
|
||||
all_text = " ".join([msg["content"] for msg in messages])
|
||||
words = all_text.lower().split()
|
||||
word_freq = {}
|
||||
for word in words:
|
||||
if len(word) > 3:
|
||||
word_freq[word] = word_freq.get(word, 0) + 1
|
||||
|
||||
top_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:5]
|
||||
|
||||
return {
|
||||
"main_topics": [word[0] for word in top_words],
|
||||
"user_intent": "情報収集・問題解決",
|
||||
"key_insights": ["基本的な会話分析"],
|
||||
"relationship_indicators": {
|
||||
"interaction_count": len(messages),
|
||||
"user_engagement": len(user_messages),
|
||||
"assistant_helpfulness": len(assistant_messages)
|
||||
},
|
||||
"emotional_tone": "neutral",
|
||||
"action_items": [],
|
||||
"summary": f"{len(user_messages)}回のやり取りによる会話"
|
||||
}
|
||||
|
||||
class RelationshipTracker:
|
||||
"""関係性追跡クラス"""
|
||||
|
||||
def __init__(self):
|
||||
init_directories()
|
||||
self.relationship_file = RELATIONSHIP_DIR / "relationships.json"
|
||||
self.relationships = self._load_relationships()
|
||||
|
||||
def _load_relationships(self) -> Dict[str, Any]:
|
||||
"""関係性データを読み込み"""
|
||||
if self.relationship_file.exists():
|
||||
with open(self.relationship_file, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
return {"targets": {}, "last_updated": datetime.now().isoformat()}
|
||||
|
||||
def _save_relationships(self):
|
||||
"""関係性データを保存"""
|
||||
self.relationships["last_updated"] = datetime.now().isoformat()
|
||||
with open(self.relationship_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(self.relationships, f, ensure_ascii=False, indent=2)
|
||||
|
||||
def update_relationship(self, target: str, interaction_type: str, weight: float = 1.0, context: str = None):
|
||||
"""関係性を更新"""
|
||||
if target not in self.relationships["targets"]:
|
||||
self.relationships["targets"][target] = {
|
||||
"score": 0.0,
|
||||
"interactions": [],
|
||||
"created_at": datetime.now().isoformat(),
|
||||
"last_interaction": None
|
||||
}
|
||||
|
||||
# スコア計算
|
||||
score_change = 0.0
|
||||
if interaction_type == "positive":
|
||||
score_change = weight * 1.0
|
||||
elif interaction_type == "negative":
|
||||
score_change = weight * -1.0
|
||||
|
||||
# 時間減衰を適用
|
||||
self._apply_time_decay(target)
|
||||
|
||||
# スコア更新
|
||||
current_score = self.relationships["targets"][target]["score"]
|
||||
new_score = current_score + score_change
|
||||
|
||||
# スコアの範囲制限（-100 to 100）
|
||||
new_score = max(-100, min(100, new_score))
|
||||
|
||||
self.relationships["targets"][target]["score"] = new_score
|
||||
self.relationships["targets"][target]["last_interaction"] = datetime.now().isoformat()
|
||||
|
||||
# インタラクション履歴を追加
|
||||
interaction_record = {
|
||||
"type": interaction_type,
|
||||
"weight": weight,
|
||||
"score_change": score_change,
|
||||
"new_score": new_score,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"context": context
|
||||
}
|
||||
|
||||
self.relationships["targets"][target]["interactions"].append(interaction_record)
|
||||
|
||||
# 履歴は最新100件まで保持
|
||||
if len(self.relationships["targets"][target]["interactions"]) > 100:
|
||||
self.relationships["targets"][target]["interactions"] = \
|
||||
self.relationships["targets"][target]["interactions"][-100:]
|
||||
|
||||
self._save_relationships()
|
||||
return new_score
|
||||
|
||||
def _apply_time_decay(self, target: str):
|
||||
"""時間減衰を適用"""
|
||||
target_data = self.relationships["targets"][target]
|
||||
last_interaction = target_data.get("last_interaction")
|
||||
|
||||
if last_interaction:
|
||||
last_time = datetime.fromisoformat(last_interaction)
|
||||
now = datetime.now()
|
||||
days_passed = (now - last_time).days
|
||||
|
||||
# 7日ごとに5%減衰
|
||||
if days_passed > 0:
|
||||
decay_factor = 0.95 ** (days_passed / 7)
|
||||
target_data["score"] *= decay_factor
|
||||
|
||||
def get_relationship_score(self, target: str) -> float:
|
||||
"""関係性スコアを取得"""
|
||||
if target in self.relationships["targets"]:
|
||||
self._apply_time_decay(target)
|
||||
return self.relationships["targets"][target]["score"]
|
||||
return 0.0
|
||||
|
||||
def should_send_message(self, target: str, threshold: float = 50.0) -> bool:
|
||||
"""メッセージ送信の可否を判定"""
|
||||
score = self.get_relationship_score(target)
|
||||
return score >= threshold
|
||||
|
||||
def get_all_relationships(self) -> Dict[str, Any]:
|
||||
"""すべての関係性を取得"""
|
||||
# 全ターゲットに時間減衰を適用
|
||||
for target in self.relationships["targets"]:
|
||||
self._apply_time_decay(target)
|
||||
|
||||
return self.relationships
|
||||
|
||||
class MemoryManager:
|
||||
"""記憶管理クラス（AI処理機能付き）"""
|
||||
|
||||
def __init__(self):
|
||||
init_directories()
|
||||
self.ai_processor = AIMemoryProcessor()
|
||||
self.relationship_tracker = RelationshipTracker()
|
||||
|
||||
def parse_chatgpt_conversation(self, conversation_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""ChatGPTの会話データを解析してメッセージを抽出"""
|
||||
messages = []
|
||||
mapping = conversation_data.get("mapping", {})
|
||||
|
||||
# メッセージを時系列順に並べる
|
||||
message_nodes = []
|
||||
for node_id, node in mapping.items():
|
||||
message = node.get("message")
|
||||
if not message:
|
||||
continue
|
||||
content = message.get("content", {})
|
||||
parts = content.get("parts", [])
|
||||
|
||||
if parts and isinstance(parts[0], str) and parts[0].strip():
|
||||
message_nodes.append({
|
||||
"id": node_id,
|
||||
"create_time": message.get("create_time", 0),
|
||||
"author_role": message["author"]["role"],
|
||||
"content": parts[0],
|
||||
"parent": node.get("parent")
|
||||
})
|
||||
|
||||
# 作成時間でソート
|
||||
message_nodes.sort(key=lambda x: x["create_time"] or 0)
|
||||
|
||||
for msg in message_nodes:
|
||||
if msg["author_role"] in ["user", "assistant"]:
|
||||
messages.append({
|
||||
"role": msg["author_role"],
|
||||
"content": msg["content"],
|
||||
"timestamp": msg["create_time"],
|
||||
"message_id": msg["id"]
|
||||
})
|
||||
|
||||
return messages
|
||||
|
||||
async def save_chatgpt_memory(self, conversation_data: Dict[str, Any], process_with_ai: bool = True) -> str:
|
||||
"""ChatGPTの会話を記憶として保存（AI処理オプション付き）"""
|
||||
title = conversation_data.get("title", "untitled")
|
||||
create_time = conversation_data.get("create_time", datetime.now().timestamp())
|
||||
|
||||
# メッセージを解析
|
||||
messages = self.parse_chatgpt_conversation(conversation_data)
|
||||
|
||||
if not messages:
|
||||
raise ValueError("No valid messages found in conversation")
|
||||
|
||||
# AI分析を実行
|
||||
ai_analysis = None
|
||||
if process_with_ai:
|
||||
try:
|
||||
ai_analysis = await self.ai_processor.generate_ai_summary(messages)
|
||||
except Exception as e:
|
||||
print(f"AI analysis failed: {e}")
|
||||
|
||||
# 基本要約を生成
|
||||
basic_summary = self.generate_basic_summary(messages)
|
||||
|
||||
# 保存データを作成
|
||||
memory_data = {
|
||||
"title": title,
|
||||
"source": "chatgpt",
|
||||
"import_time": datetime.now().isoformat(),
|
||||
"original_create_time": create_time,
|
||||
"messages": messages,
|
||||
"basic_summary": basic_summary,
|
||||
"ai_analysis": ai_analysis,
|
||||
"message_count": len(messages),
|
||||
"hash": self._generate_content_hash(messages)
|
||||
}
|
||||
|
||||
# 関係性データを更新
|
||||
if ai_analysis and "relationship_indicators" in ai_analysis:
|
||||
interaction_count = ai_analysis["relationship_indicators"].get("interaction_count", 0)
|
||||
if interaction_count > 10: # 長い会話は関係性にプラス
|
||||
self.relationship_tracker.update_relationship(
|
||||
target="user_general",
|
||||
interaction_type="positive",
|
||||
weight=min(interaction_count / 10, 5.0),
|
||||
context=f"Long conversation: {title}"
|
||||
)
|
||||
|
||||
# ファイル名を生成
|
||||
safe_title = "".join(c for c in title if c.isalnum() or c in (' ', '-', '_')).rstrip()
|
||||
timestamp = datetime.fromtimestamp(create_time).strftime("%Y%m%d_%H%M%S")
|
||||
filename = f"{timestamp}_{safe_title[:50]}.json"
|
||||
|
||||
filepath = CHATGPT_MEMORY_DIR / filename
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 処理済みメモリディレクトリにも保存
|
||||
if ai_analysis:
|
||||
processed_filepath = PROCESSED_MEMORY_DIR / filename
|
||||
with open(processed_filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
return str(filepath)
|
||||
|
||||
def generate_basic_summary(self, messages: List[Dict[str, Any]]) -> str:
|
||||
"""基本要約を生成"""
|
||||
if not messages:
|
||||
return "Empty conversation"
|
||||
|
||||
user_messages = [msg for msg in messages if msg["role"] == "user"]
|
||||
assistant_messages = [msg for msg in messages if msg["role"] == "assistant"]
|
||||
|
||||
summary = f"Conversation with {len(user_messages)} user messages and {len(assistant_messages)} assistant responses. "
|
||||
|
||||
if user_messages:
|
||||
first_user_msg = user_messages[0]["content"][:100]
|
||||
summary += f"Started with: {first_user_msg}..."
|
||||
|
||||
return summary
|
||||
|
||||
def _generate_content_hash(self, messages: List[Dict[str, Any]]) -> str:
|
||||
"""メッセージ内容のハッシュを生成"""
|
||||
content = "".join([msg["content"] for msg in messages])
|
||||
return hashlib.sha256(content.encode()).hexdigest()[:16]
|
||||
|
||||
def search_memories(self, query: str, limit: int = 10, use_ai_analysis: bool = True) -> List[Dict[str, Any]]:
|
||||
"""記憶を検索（AI分析結果も含む）"""
|
||||
results = []
|
||||
|
||||
# 処理済みメモリから検索
|
||||
search_dirs = [PROCESSED_MEMORY_DIR, CHATGPT_MEMORY_DIR] if use_ai_analysis else [CHATGPT_MEMORY_DIR]
|
||||
|
||||
for search_dir in search_dirs:
|
||||
for filepath in search_dir.glob("*.json"):
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
memory_data = json.load(f)
|
||||
|
||||
# 検索対象テキストを構築
|
||||
search_text = f"{memory_data.get('title', '')} {memory_data.get('basic_summary', '')}"
|
||||
|
||||
# AI分析結果も検索対象に含める
|
||||
if memory_data.get('ai_analysis'):
|
||||
ai_analysis = memory_data['ai_analysis']
|
||||
search_text += f" {' '.join(ai_analysis.get('main_topics', []))}"
|
||||
search_text += f" {ai_analysis.get('summary', '')}"
|
||||
search_text += f" {' '.join(ai_analysis.get('key_insights', []))}"
|
||||
|
||||
# メッセージ内容も検索対象に含める
|
||||
for msg in memory_data.get('messages', []):
|
||||
search_text += f" {msg.get('content', '')}"
|
||||
|
||||
if query.lower() in search_text.lower():
|
||||
result = {
|
||||
"filepath": str(filepath),
|
||||
"title": memory_data.get("title"),
|
||||
"basic_summary": memory_data.get("basic_summary"),
|
||||
"source": memory_data.get("source"),
|
||||
"import_time": memory_data.get("import_time"),
|
||||
"message_count": len(memory_data.get("messages", [])),
|
||||
"has_ai_analysis": bool(memory_data.get("ai_analysis"))
|
||||
}
|
||||
|
||||
if memory_data.get('ai_analysis'):
|
||||
result["ai_summary"] = memory_data['ai_analysis'].get('summary', '')
|
||||
result["main_topics"] = memory_data['ai_analysis'].get('main_topics', [])
|
||||
|
||||
results.append(result)
|
||||
|
||||
if len(results) >= limit:
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error reading memory file {filepath}: {e}")
|
||||
continue
|
||||
|
||||
if len(results) >= limit:
|
||||
break
|
||||
|
||||
return results
|
||||
|
||||
def get_memory_detail(self, filepath: str) -> Dict[str, Any]:
|
||||
"""記憶の詳細を取得"""
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Error reading memory file: {e}")
|
||||
|
||||
def list_all_memories(self) -> List[Dict[str, Any]]:
|
||||
"""すべての記憶をリスト"""
|
||||
memories = []
|
||||
|
||||
for filepath in CHATGPT_MEMORY_DIR.glob("*.json"):
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
memory_data = json.load(f)
|
||||
|
||||
memory_info = {
|
||||
"filepath": str(filepath),
|
||||
"title": memory_data.get("title"),
|
||||
"basic_summary": memory_data.get("basic_summary"),
|
||||
"source": memory_data.get("source"),
|
||||
"import_time": memory_data.get("import_time"),
|
||||
"message_count": len(memory_data.get("messages", [])),
|
||||
"has_ai_analysis": bool(memory_data.get("ai_analysis"))
|
||||
}
|
||||
|
||||
if memory_data.get('ai_analysis'):
|
||||
memory_info["ai_summary"] = memory_data['ai_analysis'].get('summary', '')
|
||||
memory_info["main_topics"] = memory_data['ai_analysis'].get('main_topics', [])
|
||||
|
||||
memories.append(memory_info)
|
||||
except Exception as e:
|
||||
print(f"Error reading memory file {filepath}: {e}")
|
||||
continue
|
||||
|
||||
# インポート時間でソート
|
||||
memories.sort(key=lambda x: x.get("import_time", ""), reverse=True)
|
||||
return memories
|
||||
|
||||
# FastAPI アプリケーション
|
||||
app = FastAPI(title="AigptMCP Server with AI Memory", version="2.0.0")
|
||||
memory_manager = MemoryManager()
|
||||
|
||||
@app.post("/memory/import/chatgpt")
|
||||
async def import_chatgpt_conversation(data: ConversationImport, process_with_ai: bool = True):
|
||||
"""ChatGPTの会話をインポート（AI処理オプション付き）"""
|
||||
try:
|
||||
filepath = await memory_manager.save_chatgpt_memory(data.conversation_data, process_with_ai)
|
||||
return {
|
||||
"success": True,
|
||||
"message": "Conversation imported successfully",
|
||||
"filepath": filepath,
|
||||
"ai_processed": process_with_ai
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
|
||||
@app.post("/memory/process-ai")
|
||||
async def process_memory_with_ai(data: MemorySummaryRequest):
|
||||
"""既存の記憶をAIで再処理"""
|
||||
try:
|
||||
# 既存記憶を読み込み
|
||||
memory_data = memory_manager.get_memory_detail(data.filepath)
|
||||
|
||||
# AI分析を実行
|
||||
ai_analysis = await memory_manager.ai_processor.generate_ai_summary(
|
||||
memory_data["messages"],
|
||||
data.ai_provider
|
||||
)
|
||||
|
||||
# データを更新
|
||||
memory_data["ai_analysis"] = ai_analysis
|
||||
memory_data["ai_processed_at"] = datetime.now().isoformat()
|
||||
|
||||
# ファイルを更新
|
||||
with open(data.filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 処理済みディレクトリにもコピー
|
||||
processed_filepath = PROCESSED_MEMORY_DIR / Path(data.filepath).name
|
||||
with open(processed_filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"message": "Memory processed with AI successfully",
|
||||
"ai_analysis": ai_analysis
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/memory/search")
|
||||
async def search_memories(query: MemoryQuery):
|
||||
"""記憶を検索"""
|
||||
try:
|
||||
results = memory_manager.search_memories(query.query, query.limit)
|
||||
return {
|
||||
"success": True,
|
||||
"results": results,
|
||||
"count": len(results)
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/memory/list")
|
||||
async def list_memories():
|
||||
"""すべての記憶をリスト"""
|
||||
try:
|
||||
memories = memory_manager.list_all_memories()
|
||||
return {
|
||||
"success": True,
|
||||
"memories": memories,
|
||||
"count": len(memories)
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/memory/detail")
|
||||
async def get_memory_detail(filepath: str):
|
||||
"""記憶の詳細を取得"""
|
||||
try:
|
||||
detail = memory_manager.get_memory_detail(filepath)
|
||||
return {
|
||||
"success": True,
|
||||
"memory": detail
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
|
||||
@app.post("/relationship/update")
|
||||
async def update_relationship(data: RelationshipUpdate):
|
||||
"""関係性を更新"""
|
||||
try:
|
||||
new_score = memory_manager.relationship_tracker.update_relationship(
|
||||
data.target, data.interaction_type, data.weight, data.context
|
||||
)
|
||||
return {
|
||||
"success": True,
|
||||
"new_score": new_score,
|
||||
"can_send_message": memory_manager.relationship_tracker.should_send_message(data.target)
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/relationship/list")
|
||||
async def list_relationships():
|
||||
"""すべての関係性をリスト"""
|
||||
try:
|
||||
relationships = memory_manager.relationship_tracker.get_all_relationships()
|
||||
return {
|
||||
"success": True,
|
||||
"relationships": relationships
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/relationship/check")
|
||||
async def check_send_permission(target: str, threshold: float = 50.0):
|
||||
"""メッセージ送信可否をチェック"""
|
||||
try:
|
||||
score = memory_manager.relationship_tracker.get_relationship_score(target)
|
||||
can_send = memory_manager.relationship_tracker.should_send_message(target, threshold)
|
||||
return {
|
||||
"success": True,
|
||||
"target": target,
|
||||
"score": score,
|
||||
"can_send_message": can_send,
|
||||
"threshold": threshold
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/chat")
|
||||
async def chat_endpoint(data: ChatMessage):
|
||||
"""チャット機能（記憶と関係性を活用）"""
|
||||
try:
|
||||
# 関連する記憶を検索
|
||||
memories = memory_manager.search_memories(data.message, limit=3)
|
||||
|
||||
# メモリのコンテキストを構築
|
||||
memory_context = ""
|
||||
if memories:
|
||||
memory_context = "\n# Related memories:\n"
|
||||
for memory in memories:
|
||||
memory_context += f"- {memory['title']}: {memory.get('ai_summary', memory.get('basic_summary', ''))}\n"
|
||||
if memory.get('main_topics'):
|
||||
memory_context += f" Topics: {', '.join(memory['main_topics'])}\n"
|
||||
|
||||
# 関係性情報を取得
|
||||
relationships = memory_manager.relationship_tracker.get_all_relationships()
|
||||
|
||||
# 実際のチャット処理
|
||||
enhanced_message = data.message
|
||||
if memory_context:
|
||||
enhanced_message = f"{data.message}\n\n{memory_context}"
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"response": f"Enhanced response with memory context: {enhanced_message}",
|
||||
"memories_used": len(memories),
|
||||
"relationship_info": {
|
||||
"active_relationships": len(relationships.get("targets", {})),
|
||||
"can_initiate_conversations": sum(1 for target, data in relationships.get("targets", {}).items()
|
||||
if memory_manager.relationship_tracker.should_send_message(target))
|
||||
}
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/")
|
||||
async def root():
|
||||
"""ヘルスチェック"""
|
||||
return {
|
||||
"service": "AigptMCP Server with AI Memory",
|
||||
"version": "2.0.0",
|
||||
"status": "running",
|
||||
"memory_dir": str(MEMORY_DIR),
|
||||
"features": [
|
||||
"AI-powered memory analysis",
|
||||
"Relationship tracking",
|
||||
"Advanced memory search",
|
||||
"Conversation import",
|
||||
"Auto-summary generation"
|
||||
],
|
||||
"endpoints": [
|
||||
"/memory/import/chatgpt",
|
||||
"/memory/process-ai",
|
||||
"/memory/search",
|
||||
"/memory/list",
|
||||
"/memory/detail",
|
||||
"/relationship/update",
|
||||
"/relationship/list",
|
||||
"/relationship/check",
|
||||
"/chat"
|
||||
]
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("🚀 AigptMCP Server with AI Memory starting...")
|
||||
print(f"📁 Memory directory: {MEMORY_DIR}")
|
||||
print(f"🧠 AI Memory processing: {'✅ Enabled' if os.getenv('OPENAI_API_KEY') or os.getenv('ANTHROPIC_API_KEY') else '❌ Disabled (no API keys)'}")
|
||||
uvicorn.run(app, host="127.0.0.1", port=5000)
|
||||
@ -1,64 +0,0 @@
|
||||
// src/cli.rs
|
||||
use clap::{Parser, Subcommand};
|
||||
|
||||
#[derive(Parser)]
|
||||
#[command(name = "aigpt")]
|
||||
#[command(about = "AI GPT CLI with MCP Server and Memory")]
|
||||
pub struct Args {
|
||||
#[command(subcommand)]
|
||||
pub command: Commands,
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
pub enum Commands {
|
||||
/// MCP Server management
|
||||
Server {
|
||||
#[command(subcommand)]
|
||||
command: ServerCommands,
|
||||
},
|
||||
/// Chat with AI
|
||||
Chat {
|
||||
/// Message to send
|
||||
message: String,
|
||||
/// Use memory context
|
||||
#[arg(long)]
|
||||
with_memory: bool,
|
||||
},
|
||||
/// Memory management
|
||||
Memory {
|
||||
#[command(subcommand)]
|
||||
command: MemoryCommands,
|
||||
},
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
pub enum ServerCommands {
|
||||
/// Setup Python MCP server environment
|
||||
Setup,
|
||||
/// Run the MCP server
|
||||
Run,
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
pub enum MemoryCommands {
|
||||
/// Import ChatGPT conversation export file
|
||||
Import {
|
||||
/// Path to ChatGPT export JSON file
|
||||
file: String,
|
||||
},
|
||||
/// Search memories
|
||||
Search {
|
||||
/// Search query
|
||||
query: String,
|
||||
/// Maximum number of results
|
||||
#[arg(short, long, default_value = "10")]
|
||||
limit: usize,
|
||||
},
|
||||
/// List all memories
|
||||
List,
|
||||
/// Show memory details
|
||||
Detail {
|
||||
/// Path to memory file
|
||||
filepath: String,
|
||||
},
|
||||
}
|
||||
@ -1,59 +0,0 @@
|
||||
// src/config.rs
|
||||
use std::fs;
|
||||
use std::path::{Path, PathBuf};
|
||||
use shellexpand;
|
||||
|
||||
pub struct ConfigPaths {
|
||||
pub base_dir: PathBuf,
|
||||
}
|
||||
|
||||
impl ConfigPaths {
|
||||
pub fn new() -> Self {
|
||||
let app_name = env!("CARGO_PKG_NAME");
|
||||
let mut base_dir = shellexpand::tilde("~").to_string();
|
||||
base_dir.push_str(&format!("/.config/{}/", app_name));
|
||||
let base_path = Path::new(&base_dir);
|
||||
if !base_path.exists() {
|
||||
let _ = fs::create_dir_all(base_path);
|
||||
}
|
||||
|
||||
ConfigPaths {
|
||||
base_dir: base_path.to_path_buf(),
|
||||
}
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
pub fn data_file(&self, file_name: &str) -> PathBuf {
|
||||
let file_path = match file_name {
|
||||
"db" => self.base_dir.join("user.db"),
|
||||
"toml" => self.base_dir.join("user.toml"),
|
||||
"json" => self.base_dir.join("user.json"),
|
||||
_ => self.base_dir.join(format!(".{}", file_name)),
|
||||
};
|
||||
file_path
|
||||
}
|
||||
|
||||
pub fn mcp_dir(&self) -> PathBuf {
|
||||
self.base_dir.join("mcp")
|
||||
}
|
||||
|
||||
pub fn venv_path(&self) -> PathBuf {
|
||||
self.mcp_dir().join(".venv")
|
||||
}
|
||||
|
||||
pub fn python_executable(&self) -> PathBuf {
|
||||
if cfg!(windows) {
|
||||
self.venv_path().join("Scripts").join("python.exe")
|
||||
} else {
|
||||
self.venv_path().join("bin").join("python")
|
||||
}
|
||||
}
|
||||
|
||||
pub fn pip_executable(&self) -> PathBuf {
|
||||
if cfg!(windows) {
|
||||
self.venv_path().join("Scripts").join("pip.exe")
|
||||
} else {
|
||||
self.venv_path().join("bin").join("pip")
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,58 +0,0 @@
|
||||
// main.rs
|
||||
mod cli;
|
||||
mod config;
|
||||
mod mcp;
|
||||
|
||||
use cli::{Args, Commands, ServerCommands, MemoryCommands};
|
||||
use clap::Parser;
|
||||
|
||||
#[tokio::main]
|
||||
async fn main() {
|
||||
let args = Args::parse();
|
||||
|
||||
match args.command {
|
||||
Commands::Server { command } => {
|
||||
match command {
|
||||
ServerCommands::Setup => {
|
||||
mcp::server::setup();
|
||||
}
|
||||
ServerCommands::Run => {
|
||||
mcp::server::run().await;
|
||||
}
|
||||
}
|
||||
}
|
||||
Commands::Chat { message, with_memory } => {
|
||||
if with_memory {
|
||||
if let Err(e) = mcp::memory::handle_chat_with_memory(&message).await {
|
||||
eprintln!("❌ 記憶チャットエラー: {}", e);
|
||||
}
|
||||
} else {
|
||||
mcp::server::chat(&message).await;
|
||||
}
|
||||
}
|
||||
Commands::Memory { command } => {
|
||||
match command {
|
||||
MemoryCommands::Import { file } => {
|
||||
if let Err(e) = mcp::memory::handle_import(&file).await {
|
||||
eprintln!("❌ インポートエラー: {}", e);
|
||||
}
|
||||
}
|
||||
MemoryCommands::Search { query, limit } => {
|
||||
if let Err(e) = mcp::memory::handle_search(&query, limit).await {
|
||||
eprintln!("❌ 検索エラー: {}", e);
|
||||
}
|
||||
}
|
||||
MemoryCommands::List => {
|
||||
if let Err(e) = mcp::memory::handle_list().await {
|
||||
eprintln!("❌ 一覧取得エラー: {}", e);
|
||||
}
|
||||
}
|
||||
MemoryCommands::Detail { filepath } => {
|
||||
if let Err(e) = mcp::memory::handle_detail(&filepath).await {
|
||||
eprintln!("❌ 詳細取得エラー: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,393 +0,0 @@
|
||||
// src/mcp/memory.rs
|
||||
use reqwest;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use serde_json::{self, Value};
|
||||
use std::fs;
|
||||
use std::path::Path;
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct MemorySearchRequest {
|
||||
pub query: String,
|
||||
pub limit: usize,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct ChatRequest {
|
||||
pub message: String,
|
||||
pub model: Option<String>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct ConversationImportRequest {
|
||||
pub conversation_data: Value,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub struct ApiResponse {
|
||||
pub success: bool,
|
||||
pub error: Option<String>,
|
||||
#[allow(dead_code)]
|
||||
pub message: Option<String>,
|
||||
pub filepath: Option<String>,
|
||||
pub results: Option<Vec<MemoryResult>>,
|
||||
pub memories: Option<Vec<MemoryResult>>,
|
||||
#[allow(dead_code)]
|
||||
pub count: Option<usize>,
|
||||
pub memory: Option<Value>,
|
||||
pub response: Option<String>,
|
||||
pub memories_used: Option<usize>,
|
||||
pub imported_count: Option<usize>,
|
||||
pub total_count: Option<usize>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
pub struct MemoryResult {
|
||||
#[allow(dead_code)]
|
||||
pub filepath: String,
|
||||
pub title: Option<String>,
|
||||
pub summary: Option<String>,
|
||||
pub source: Option<String>,
|
||||
pub import_time: Option<String>,
|
||||
pub message_count: Option<usize>,
|
||||
}
|
||||
|
||||
pub struct MemoryClient {
|
||||
base_url: String,
|
||||
client: reqwest::Client,
|
||||
}
|
||||
|
||||
impl MemoryClient {
|
||||
pub fn new(base_url: Option<String>) -> Self {
|
||||
let url = base_url.unwrap_or_else(|| "http://127.0.0.1:5000".to_string());
|
||||
Self {
|
||||
base_url: url,
|
||||
client: reqwest::Client::new(),
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn import_chatgpt_file(&self, filepath: &str) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
// ファイルを読み込み
|
||||
let content = fs::read_to_string(filepath)?;
|
||||
let json_data: Value = serde_json::from_str(&content)?;
|
||||
|
||||
// 配列かどうかチェック
|
||||
match json_data.as_array() {
|
||||
Some(conversations) => {
|
||||
// 複数の会話をインポート
|
||||
let mut imported_count = 0;
|
||||
let total_count = conversations.len();
|
||||
|
||||
for conversation in conversations {
|
||||
match self.import_single_conversation(conversation.clone()).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
imported_count += 1;
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ インポートエラー: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(ApiResponse {
|
||||
success: true,
|
||||
imported_count: Some(imported_count),
|
||||
total_count: Some(total_count),
|
||||
error: None,
|
||||
message: Some(format!("{}個中{}個の会話をインポートしました", total_count, imported_count)),
|
||||
filepath: None,
|
||||
results: None,
|
||||
memories: None,
|
||||
count: None,
|
||||
memory: None,
|
||||
response: None,
|
||||
memories_used: None,
|
||||
})
|
||||
}
|
||||
None => {
|
||||
// 単一の会話をインポート
|
||||
self.import_single_conversation(json_data).await
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async fn import_single_conversation(&self, conversation_data: Value) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let request = ConversationImportRequest { conversation_data };
|
||||
|
||||
let response = self.client
|
||||
.post(&format!("{}/memory/import/chatgpt", self.base_url))
|
||||
.json(&request)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn search_memories(&self, query: &str, limit: usize) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let request = MemorySearchRequest {
|
||||
query: query.to_string(),
|
||||
limit,
|
||||
};
|
||||
|
||||
let response = self.client
|
||||
.post(&format!("{}/memory/search", self.base_url))
|
||||
.json(&request)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn list_memories(&self) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let response = self.client
|
||||
.get(&format!("{}/memory/list", self.base_url))
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn get_memory_detail(&self, filepath: &str) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let response = self.client
|
||||
.get(&format!("{}/memory/detail", self.base_url))
|
||||
.query(&[("filepath", filepath)])
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn chat_with_memory(&self, message: &str) -> Result<ApiResponse, Box<dyn std::error::Error>> {
|
||||
let request = ChatRequest {
|
||||
message: message.to_string(),
|
||||
model: None,
|
||||
};
|
||||
|
||||
let response = self.client
|
||||
.post(&format!("{}/chat", self.base_url))
|
||||
.json(&request)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
let result: ApiResponse = response.json().await?;
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
pub async fn is_server_running(&self) -> bool {
|
||||
match self.client.get(&self.base_url).send().await {
|
||||
Ok(response) => response.status().is_success(),
|
||||
Err(_) => false,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn handle_import(filepath: &str) -> Result<(), Box<dyn std::error::Error>> {
|
||||
if !Path::new(filepath).exists() {
|
||||
eprintln!("❌ ファイルが見つかりません: {}", filepath);
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
// サーバーが起動しているかチェック
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("🔄 ChatGPT会話をインポートしています: {}", filepath);
|
||||
|
||||
match client.import_chatgpt_file(filepath).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let (Some(imported), Some(total)) = (response.imported_count, response.total_count) {
|
||||
println!("✅ {}個中{}個の会話をインポートしました", total, imported);
|
||||
} else {
|
||||
println!("✅ 会話をインポートしました");
|
||||
if let Some(path) = response.filepath {
|
||||
println!("📁 保存先: {}", path);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ インポートに失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ インポートエラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn handle_search(query: &str, limit: usize) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("🔍 記憶を検索しています: {}", query);
|
||||
|
||||
match client.search_memories(query, limit).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let Some(results) = response.results {
|
||||
println!("📚 {}個の記憶が見つかりました:", results.len());
|
||||
for memory in results {
|
||||
println!(" • {}", memory.title.unwrap_or_else(|| "タイトルなし".to_string()));
|
||||
if let Some(summary) = memory.summary {
|
||||
println!(" 概要: {}", summary);
|
||||
}
|
||||
if let Some(count) = memory.message_count {
|
||||
println!(" メッセージ数: {}", count);
|
||||
}
|
||||
println!();
|
||||
}
|
||||
} else {
|
||||
println!("📚 記憶が見つかりませんでした");
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ 検索に失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ 検索エラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn handle_list() -> Result<(), Box<dyn std::error::Error>> {
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("📋 記憶一覧を取得しています...");
|
||||
|
||||
match client.list_memories().await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let Some(memories) = response.memories {
|
||||
println!("📚 総記憶数: {}", memories.len());
|
||||
for memory in memories {
|
||||
println!(" • {}", memory.title.unwrap_or_else(|| "タイトルなし".to_string()));
|
||||
if let Some(source) = memory.source {
|
||||
println!(" ソース: {}", source);
|
||||
}
|
||||
if let Some(count) = memory.message_count {
|
||||
println!(" メッセージ数: {}", count);
|
||||
}
|
||||
if let Some(import_time) = memory.import_time {
|
||||
println!(" インポート時刻: {}", import_time);
|
||||
}
|
||||
println!();
|
||||
}
|
||||
} else {
|
||||
println!("📚 記憶がありません");
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ 一覧取得に失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ 一覧取得エラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn handle_detail(filepath: &str) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("📄 記憶の詳細を取得しています: {}", filepath);
|
||||
|
||||
match client.get_memory_detail(filepath).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let Some(memory) = response.memory {
|
||||
if let Some(title) = memory.get("title").and_then(|v| v.as_str()) {
|
||||
println!("タイトル: {}", title);
|
||||
}
|
||||
if let Some(source) = memory.get("source").and_then(|v| v.as_str()) {
|
||||
println!("ソース: {}", source);
|
||||
}
|
||||
if let Some(summary) = memory.get("summary").and_then(|v| v.as_str()) {
|
||||
println!("概要: {}", summary);
|
||||
}
|
||||
if let Some(messages) = memory.get("messages").and_then(|v| v.as_array()) {
|
||||
println!("メッセージ数: {}", messages.len());
|
||||
println!("\n最近のメッセージ:");
|
||||
for msg in messages.iter().take(5) {
|
||||
if let (Some(role), Some(content)) = (
|
||||
msg.get("role").and_then(|v| v.as_str()),
|
||||
msg.get("content").and_then(|v| v.as_str())
|
||||
) {
|
||||
let content_preview = if content.len() > 100 {
|
||||
format!("{}...", &content[..100])
|
||||
} else {
|
||||
content.to_string()
|
||||
};
|
||||
println!(" {}: {}", role, content_preview);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ 詳細取得に失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ 詳細取得エラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn handle_chat_with_memory(message: &str) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let client = MemoryClient::new(None);
|
||||
|
||||
if !client.is_server_running().await {
|
||||
eprintln!("❌ MCP Serverが起動していません。先に 'aigpt server run' を実行してください。");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
println!("💬 記憶を活用してチャットしています...");
|
||||
|
||||
match client.chat_with_memory(message).await {
|
||||
Ok(response) => {
|
||||
if response.success {
|
||||
if let Some(reply) = response.response {
|
||||
println!("🤖 {}", reply);
|
||||
}
|
||||
if let Some(memories_used) = response.memories_used {
|
||||
println!("📚 使用した記憶数: {}", memories_used);
|
||||
}
|
||||
} else {
|
||||
eprintln!("❌ チャットに失敗: {:?}", response.error);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ チャットエラー: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
@ -1,3 +0,0 @@
|
||||
// src/mcp/mod.rs
|
||||
pub mod server;
|
||||
pub mod memory;
|
||||
@ -1,147 +0,0 @@
|
||||
// src/mcp/server.rs
|
||||
use crate::config::ConfigPaths;
|
||||
//use std::fs;
|
||||
use std::process::Command as OtherCommand;
|
||||
use std::env;
|
||||
use fs_extra::dir::{copy, CopyOptions};
|
||||
|
||||
pub fn setup() {
|
||||
println!("🔧 MCP Server環境をセットアップしています...");
|
||||
let config = ConfigPaths::new();
|
||||
let mcp_dir = config.mcp_dir();
|
||||
|
||||
// プロジェクトのmcp/ディレクトリからファイルをコピー
|
||||
let current_dir = env::current_dir().expect("現在のディレクトリを取得できません");
|
||||
let project_mcp_dir = current_dir.join("mcp");
|
||||
if !project_mcp_dir.exists() {
|
||||
eprintln!("❌ プロジェクトのmcp/ディレクトリが見つかりません: {}", project_mcp_dir.display());
|
||||
return;
|
||||
}
|
||||
|
||||
if mcp_dir.exists() {
|
||||
fs_extra::dir::remove(&mcp_dir).expect("既存のmcp_dirの削除に失敗しました");
|
||||
}
|
||||
|
||||
let mut options = CopyOptions::new();
|
||||
options.overwrite = true; // 上書き
|
||||
options.copy_inside = true; // 中身だけコピー
|
||||
|
||||
copy(&project_mcp_dir, &mcp_dir, &options).expect("コピーに失敗しました");
|
||||
|
||||
// 仮想環境の作成
|
||||
let venv_path = config.venv_path();
|
||||
if !venv_path.exists() {
|
||||
println!("🐍 仮想環境を作成しています...");
|
||||
let output = OtherCommand::new("python3")
|
||||
.args(&["-m", "venv", ".venv"])
|
||||
.current_dir(&mcp_dir)
|
||||
.output()
|
||||
.expect("venvの作成に失敗しました");
|
||||
|
||||
if !output.status.success() {
|
||||
eprintln!("❌ venv作成エラー: {}", String::from_utf8_lossy(&output.stderr));
|
||||
return;
|
||||
}
|
||||
println!("✅ 仮想環境を作成しました");
|
||||
} else {
|
||||
println!("✅ 仮想環境は既に存在します");
|
||||
}
|
||||
|
||||
// 依存関係のインストール
|
||||
println!("📦 依存関係をインストールしています...");
|
||||
let pip_path = config.pip_executable();
|
||||
let output = OtherCommand::new(&pip_path)
|
||||
.args(&["install", "-r", "requirements.txt"])
|
||||
.current_dir(&mcp_dir)
|
||||
.output()
|
||||
.expect("pipコマンドの実行に失敗しました");
|
||||
|
||||
if !output.status.success() {
|
||||
eprintln!("❌ pip installエラー: {}", String::from_utf8_lossy(&output.stderr));
|
||||
return;
|
||||
}
|
||||
|
||||
println!("✅ MCP Server環境のセットアップが完了しました!");
|
||||
println!("📍 セットアップ場所: {}", mcp_dir.display());
|
||||
}
|
||||
|
||||
pub async fn run() {
|
||||
println!("🚀 MCP Serverを起動しています...");
|
||||
|
||||
let config = ConfigPaths::new();
|
||||
let mcp_dir = config.mcp_dir();
|
||||
let python_path = config.python_executable();
|
||||
let server_py_path = mcp_dir.join("server.py");
|
||||
|
||||
// セットアップの確認
|
||||
if !server_py_path.exists() {
|
||||
eprintln!("❌ server.pyが見つかりません。先に 'aigpt server setup' を実行してください。");
|
||||
return;
|
||||
}
|
||||
|
||||
if !python_path.exists() {
|
||||
eprintln!("❌ Python実行ファイルが見つかりません。先に 'aigpt server setup' を実行してください。");
|
||||
return;
|
||||
}
|
||||
|
||||
// サーバーの起動
|
||||
println!("🔗 サーバーを起動中... (Ctrl+Cで停止)");
|
||||
let mut child = OtherCommand::new(&python_path)
|
||||
.arg("server.py")
|
||||
.current_dir(&mcp_dir)
|
||||
.spawn()
|
||||
.expect("MCP Serverの起動に失敗しました");
|
||||
|
||||
// サーバーの終了を待機
|
||||
match child.wait() {
|
||||
Ok(status) => {
|
||||
if status.success() {
|
||||
println!("✅ MCP Serverが正常に終了しました");
|
||||
} else {
|
||||
println!("❌ MCP Serverが異常終了しました: {}", status);
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
eprintln!("❌ MCP Serverの実行中にエラーが発生しました: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn chat(message: &str) {
|
||||
println!("💬 チャットを開始しています...");
|
||||
|
||||
let config = ConfigPaths::new();
|
||||
let mcp_dir = config.mcp_dir();
|
||||
let python_path = config.python_executable();
|
||||
let chat_py_path = mcp_dir.join("chat.py");
|
||||
|
||||
// セットアップの確認
|
||||
if !chat_py_path.exists() {
|
||||
eprintln!("❌ chat.pyが見つかりません。先に 'aigpt server setup' を実行してください。");
|
||||
return;
|
||||
}
|
||||
|
||||
if !python_path.exists() {
|
||||
eprintln!("❌ Python実行ファイルが見つかりません。先に 'aigpt server setup' を実行してください。");
|
||||
return;
|
||||
}
|
||||
|
||||
// チャットの実行
|
||||
let output = OtherCommand::new(&python_path)
|
||||
.args(&["chat.py", message])
|
||||
.current_dir(&mcp_dir)
|
||||
.output()
|
||||
.expect("chat.pyの実行に失敗しました");
|
||||
|
||||
if output.status.success() {
|
||||
let stdout = String::from_utf8_lossy(&output.stdout);
|
||||
let stderr = String::from_utf8_lossy(&output.stderr);
|
||||
|
||||
if !stderr.is_empty() {
|
||||
print!("{}", stderr);
|
||||
}
|
||||
print!("{}", stdout);
|
||||
} else {
|
||||
eprintln!("❌ チャット実行エラー: {}", String::from_utf8_lossy(&output.stderr));
|
||||
}
|
||||
}
|
||||
17
src/aigpt.egg-info/PKG-INFO
Normal file
17
src/aigpt.egg-info/PKG-INFO
Normal file
@ -0,0 +1,17 @@
|
||||
Metadata-Version: 2.4
|
||||
Name: aigpt
|
||||
Version: 0.1.0
|
||||
Summary: Autonomous transmission AI with unique personality based on relationship parameters
|
||||
Requires-Python: >=3.10
|
||||
Requires-Dist: click>=8.0.0
|
||||
Requires-Dist: typer>=0.9.0
|
||||
Requires-Dist: fastapi-mcp>=0.1.0
|
||||
Requires-Dist: pydantic>=2.0.0
|
||||
Requires-Dist: httpx>=0.24.0
|
||||
Requires-Dist: rich>=13.0.0
|
||||
Requires-Dist: python-dotenv>=1.0.0
|
||||
Requires-Dist: ollama>=0.1.0
|
||||
Requires-Dist: openai>=1.0.0
|
||||
Requires-Dist: uvicorn>=0.23.0
|
||||
Requires-Dist: apscheduler>=3.10.0
|
||||
Requires-Dist: croniter>=1.3.0
|
||||
20
src/aigpt.egg-info/SOURCES.txt
Normal file
20
src/aigpt.egg-info/SOURCES.txt
Normal file
@ -0,0 +1,20 @@
|
||||
README.md
|
||||
pyproject.toml
|
||||
src/aigpt/__init__.py
|
||||
src/aigpt/ai_provider.py
|
||||
src/aigpt/cli.py
|
||||
src/aigpt/config.py
|
||||
src/aigpt/fortune.py
|
||||
src/aigpt/mcp_server.py
|
||||
src/aigpt/memory.py
|
||||
src/aigpt/models.py
|
||||
src/aigpt/persona.py
|
||||
src/aigpt/relationship.py
|
||||
src/aigpt/scheduler.py
|
||||
src/aigpt/transmission.py
|
||||
src/aigpt.egg-info/PKG-INFO
|
||||
src/aigpt.egg-info/SOURCES.txt
|
||||
src/aigpt.egg-info/dependency_links.txt
|
||||
src/aigpt.egg-info/entry_points.txt
|
||||
src/aigpt.egg-info/requires.txt
|
||||
src/aigpt.egg-info/top_level.txt
|
||||
1
src/aigpt.egg-info/dependency_links.txt
Normal file
1
src/aigpt.egg-info/dependency_links.txt
Normal file
@ -0,0 +1 @@
|
||||
|
||||
2
src/aigpt.egg-info/entry_points.txt
Normal file
2
src/aigpt.egg-info/entry_points.txt
Normal file
@ -0,0 +1,2 @@
|
||||
[console_scripts]
|
||||
aigpt = aigpt.cli:app
|
||||
12
src/aigpt.egg-info/requires.txt
Normal file
12
src/aigpt.egg-info/requires.txt
Normal file
@ -0,0 +1,12 @@
|
||||
click>=8.0.0
|
||||
typer>=0.9.0
|
||||
fastapi-mcp>=0.1.0
|
||||
pydantic>=2.0.0
|
||||
httpx>=0.24.0
|
||||
rich>=13.0.0
|
||||
python-dotenv>=1.0.0
|
||||
ollama>=0.1.0
|
||||
openai>=1.0.0
|
||||
uvicorn>=0.23.0
|
||||
apscheduler>=3.10.0
|
||||
croniter>=1.3.0
|
||||
1
src/aigpt.egg-info/top_level.txt
Normal file
1
src/aigpt.egg-info/top_level.txt
Normal file
@ -0,0 +1 @@
|
||||
aigpt
|
||||
176
src/aigpt/cli.py
176
src/aigpt/cli.py
@ -7,6 +7,12 @@ from rich.console import Console
|
||||
from rich.table import Table
|
||||
from rich.panel import Panel
|
||||
from datetime import datetime, timedelta
|
||||
import subprocess
|
||||
import shlex
|
||||
from prompt_toolkit import prompt
|
||||
from prompt_toolkit.completion import WordCompleter
|
||||
from prompt_toolkit.history import FileHistory
|
||||
from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
|
||||
|
||||
from .persona import Persona
|
||||
from .transmission import TransmissionController
|
||||
@ -369,6 +375,176 @@ def schedule(
|
||||
console.print("Valid actions: add, list, enable, disable, remove, run")
|
||||
|
||||
|
||||
@app.command()
|
||||
def shell(
|
||||
data_dir: Optional[Path] = typer.Option(None, "--data-dir", "-d", help="Data directory"),
|
||||
model: Optional[str] = typer.Option("qwen2.5", "--model", "-m", help="AI model to use"),
|
||||
provider: Optional[str] = typer.Option("ollama", "--provider", help="AI provider (ollama/openai)")
|
||||
):
|
||||
"""Interactive shell mode (ai.shell)"""
|
||||
persona = get_persona(data_dir)
|
||||
|
||||
# Create AI provider
|
||||
ai_provider = None
|
||||
if provider and model:
|
||||
try:
|
||||
ai_provider = create_ai_provider(provider, model)
|
||||
console.print(f"[dim]Using {provider} with model {model}[/dim]\n")
|
||||
except Exception as e:
|
||||
console.print(f"[yellow]Warning: Could not create AI provider: {e}[/yellow]")
|
||||
console.print("[yellow]Falling back to simple responses[/yellow]\n")
|
||||
|
||||
# Welcome message
|
||||
console.print(Panel(
|
||||
"[cyan]Welcome to ai.shell[/cyan]\n\n"
|
||||
"Interactive AI-powered shell with command execution\n\n"
|
||||
"Commands:\n"
|
||||
" help - Show available commands\n"
|
||||
" exit/quit - Exit shell\n"
|
||||
" !<command> - Execute shell command\n"
|
||||
" chat <message> - Chat with AI\n"
|
||||
" status - Show AI status\n"
|
||||
" clear - Clear screen\n\n"
|
||||
"Type any message to interact with AI",
|
||||
title="ai.shell",
|
||||
border_style="green"
|
||||
))
|
||||
|
||||
# Command completer
|
||||
commands = ['help', 'exit', 'quit', 'chat', 'status', 'clear', 'fortune', 'relationships']
|
||||
completer = WordCompleter(commands)
|
||||
|
||||
# History file
|
||||
history_file = data_dir / "shell_history.txt"
|
||||
history = FileHistory(str(history_file))
|
||||
|
||||
# Main shell loop
|
||||
current_user = "shell_user" # Default user for shell sessions
|
||||
|
||||
while True:
|
||||
try:
|
||||
# Get input with completion
|
||||
user_input = prompt(
|
||||
"ai.shell> ",
|
||||
completer=completer,
|
||||
history=history,
|
||||
auto_suggest=AutoSuggestFromHistory()
|
||||
).strip()
|
||||
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
# Exit commands
|
||||
if user_input.lower() in ['exit', 'quit']:
|
||||
console.print("[cyan]Goodbye![/cyan]")
|
||||
break
|
||||
|
||||
# Help command
|
||||
elif user_input.lower() == 'help':
|
||||
console.print(Panel(
|
||||
"[cyan]ai.shell Commands:[/cyan]\n\n"
|
||||
" help - Show this help message\n"
|
||||
" exit/quit - Exit the shell\n"
|
||||
" !<command> - Execute a shell command\n"
|
||||
" chat <message> - Explicitly chat with AI\n"
|
||||
" status - Show AI status\n"
|
||||
" fortune - Check AI fortune\n"
|
||||
" relationships - List all relationships\n"
|
||||
" clear - Clear the screen\n\n"
|
||||
"You can also type any message to chat with AI\n"
|
||||
"Use Tab for command completion",
|
||||
title="Help",
|
||||
border_style="yellow"
|
||||
))
|
||||
|
||||
# Clear command
|
||||
elif user_input.lower() == 'clear':
|
||||
console.clear()
|
||||
|
||||
# Shell command execution
|
||||
elif user_input.startswith('!'):
|
||||
cmd = user_input[1:].strip()
|
||||
if cmd:
|
||||
try:
|
||||
# Execute command
|
||||
result = subprocess.run(
|
||||
shlex.split(cmd),
|
||||
capture_output=True,
|
||||
text=True,
|
||||
shell=False
|
||||
)
|
||||
|
||||
if result.stdout:
|
||||
console.print(result.stdout.rstrip())
|
||||
if result.stderr:
|
||||
console.print(f"[red]{result.stderr.rstrip()}[/red]")
|
||||
|
||||
if result.returncode != 0:
|
||||
console.print(f"[red]Command exited with code {result.returncode}[/red]")
|
||||
except FileNotFoundError:
|
||||
console.print(f"[red]Command not found: {cmd.split()[0]}[/red]")
|
||||
except Exception as e:
|
||||
console.print(f"[red]Error executing command: {e}[/red]")
|
||||
|
||||
# Status command
|
||||
elif user_input.lower() == 'status':
|
||||
state = persona.get_current_state()
|
||||
console.print(f"\nMood: {state.current_mood}")
|
||||
console.print(f"Fortune: {state.fortune.fortune_value}/10")
|
||||
|
||||
rel = persona.relationships.get_or_create_relationship(current_user)
|
||||
console.print(f"\nRelationship Status: {rel.status.value}")
|
||||
console.print(f"Score: {rel.score:.2f} / {rel.threshold}")
|
||||
|
||||
# Fortune command
|
||||
elif user_input.lower() == 'fortune':
|
||||
fortune = persona.fortune_system.get_today_fortune()
|
||||
fortune_bar = "🌟" * fortune.fortune_value + "☆" * (10 - fortune.fortune_value)
|
||||
console.print(f"\n{fortune_bar}")
|
||||
console.print(f"Today's Fortune: {fortune.fortune_value}/10")
|
||||
|
||||
# Relationships command
|
||||
elif user_input.lower() == 'relationships':
|
||||
if persona.relationships.relationships:
|
||||
console.print("\n[cyan]Relationships:[/cyan]")
|
||||
for user_id, rel in persona.relationships.relationships.items():
|
||||
console.print(f" {user_id[:16]}... - {rel.status.value} ({rel.score:.2f})")
|
||||
else:
|
||||
console.print("[yellow]No relationships yet[/yellow]")
|
||||
|
||||
# Chat command or direct message
|
||||
else:
|
||||
# Remove 'chat' prefix if present
|
||||
if user_input.lower().startswith('chat '):
|
||||
message = user_input[5:].strip()
|
||||
else:
|
||||
message = user_input
|
||||
|
||||
if message:
|
||||
# Process interaction with AI
|
||||
response, relationship_delta = persona.process_interaction(
|
||||
current_user, message, ai_provider
|
||||
)
|
||||
|
||||
# Display response
|
||||
console.print(f"\n[cyan]AI:[/cyan] {response}")
|
||||
|
||||
# Show relationship change if significant
|
||||
if abs(relationship_delta) >= 0.1:
|
||||
if relationship_delta > 0:
|
||||
console.print(f"[green](+{relationship_delta:.2f} relationship)[/green]")
|
||||
else:
|
||||
console.print(f"[red]({relationship_delta:.2f} relationship)[/red]")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
console.print("\n[yellow]Use 'exit' or 'quit' to leave the shell[/yellow]")
|
||||
except EOFError:
|
||||
console.print("\n[cyan]Goodbye![/cyan]")
|
||||
break
|
||||
except Exception as e:
|
||||
console.print(f"[red]Error: {e}[/red]")
|
||||
|
||||
|
||||
@app.command()
|
||||
def config(
|
||||
action: str = typer.Argument(..., help="Action: get, set, delete, list"),
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user