diff --git a/claude.md b/claude.md
index 31bd5aa..5e0be4b 100644
--- a/claude.md
+++ b/claude.md
@@ -14,10 +14,11 @@
- **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
+- 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
@@ -326,3 +327,94 @@ POST /mcp/memory/syui/ltm
• ✨ 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。
diff --git a/mcp/server.py b/mcp/server.py
index 2e25c73..e8a5e45 100644
--- a/mcp/server.py
+++ b/mcp/server.py
@@ -1,15 +1,18 @@
# mcp/server.py
"""
-Enhanced MCP Server with Memory for aigpt CLI
+Enhanced MCP Server with AI Memory Processing for aigpt CLI
"""
import json
import os
-from datetime import datetime
+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):
@@ -23,22 +26,260 @@ class MemoryQuery(BaseModel):
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 MemoryManager:
- """記憶管理クラス"""
+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"""
@@ -54,7 +295,6 @@ class MemoryManager:
content = message.get("content", {})
parts = content.get("parts", [])
- # partsが存在し、最初の要素が文字列で空でない場合のみ
if parts and isinstance(parts[0], str) and parts[0].strip():
message_nodes.append({
"id": node_id,
@@ -63,18 +303,6 @@ class MemoryManager:
"content": parts[0],
"parent": node.get("parent")
})
- else:
- print(f"⚠️ Skipped non-text or empty message in node {node_id}")
- #if message and message.get("content", {}).get("parts"):
- # parts = message["content"]["parts"]
- # if parts and parts[0].strip(): # 空でないメッセージのみ
- # 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)
@@ -90,8 +318,8 @@ class MemoryManager:
return messages
- def save_chatgpt_memory(self, conversation_data: Dict[str, Any]) -> str:
- """ChatGPTの会話を記憶として保存"""
+ 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())
@@ -101,6 +329,17 @@ class MemoryManager:
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,
@@ -108,10 +347,24 @@ class MemoryManager:
"import_time": datetime.now().isoformat(),
"original_create_time": create_time,
"messages": messages,
- "summary": self.generate_summary(messages)
+ "basic_summary": basic_summary,
+ "ai_analysis": ai_analysis,
+ "message_count": len(messages),
+ "hash": self._generate_content_hash(messages)
}
- # ファイル名を生成（タイトルをサニタイズ）
+ # 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"
@@ -120,14 +373,19 @@ class MemoryManager:
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_summary(self, messages: List[Dict[str, Any]]) -> str:
- """会話の要約を生成"""
+ def generate_basic_summary(self, messages: List[Dict[str, Any]]) -> str:
+ """基本要約を生成"""
if not messages:
return "Empty conversation"
- # 簡単な要約を生成（実際のAIによる要約は後で実装可能）
user_messages = [msg for msg in messages if msg["role"] == "user"]
assistant_messages = [msg for msg in messages if msg["role"] == "assistant"]
@@ -139,37 +397,64 @@ class MemoryManager:
return summary
- def search_memories(self, query: str, limit: int = 10) -> List[Dict[str, Any]]:
- """記憶を検索"""
+ 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 = []
- # ChatGPTの記憶を検索
- for filepath in CHATGPT_MEMORY_DIR.glob("*.json"):
- try:
- with open(filepath, 'r', encoding='utf-8') as f:
- memory_data = json.load(f)
-
- # 簡単なキーワード検索
- search_text = f"{memory_data.get('title', '')} {memory_data.get('summary', '')}"
- for msg in memory_data.get('messages', []):
- search_text += f" {msg.get('content', '')}"
-
- if query.lower() in search_text.lower():
- results.append({
- "filepath": str(filepath),
- "title": memory_data.get("title"),
- "summary": memory_data.get("summary"),
- "source": memory_data.get("source"),
- "import_time": memory_data.get("import_time"),
- "message_count": len(memory_data.get("messages", []))
- })
+ # 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)
- if len(results) >= limit:
- break
+ # 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"))
+ }
- except Exception as e:
- print(f"Error reading memory file {filepath}: {e}")
- continue
+ 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
@@ -190,14 +475,21 @@ class MemoryManager:
with open(filepath, 'r', encoding='utf-8') as f:
memory_data = json.load(f)
- memories.append({
+ memory_info = {
"filepath": str(filepath),
"title": memory_data.get("title"),
- "summary": memory_data.get("summary"),
+ "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", []))
- })
+ "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
@@ -207,22 +499,57 @@ class MemoryManager:
return memories
# FastAPI アプリケーション
-app = FastAPI(title="AigptMCP Server with Memory", version="1.0.0")
+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):
- """ChatGPTの会話をインポート"""
+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 = memory_manager.save_chatgpt_memory(data.conversation_data)
+ filepath = await memory_manager.save_chatgpt_memory(data.conversation_data, process_with_ai)
return {
"success": True,
"message": "Conversation imported successfully",
- "filepath": filepath
+ "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"""
@@ -261,9 +588,52 @@ async def get_memory_detail(filepath: str):
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（記憶を活用）"""
+ """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)
@@ -273,9 +643,14 @@ async def chat_endpoint(data: ChatMessage):
if memories:
memory_context = "\n# Related memories:\n"
for memory in memories:
- memory_context += f"- {memory['title']}: {memory['summary']}\n"
+ 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"
- # 実際のチャット処理（他のプロバイダーに転送）
+ # 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}"
@@ -283,7 +658,12 @@ async def chat_endpoint(data: ChatMessage):
return {
"success": True,
"response": f"Enhanced response with memory context: {enhanced_message}",
- "memories_used": len(memories)
+ "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))
@@ -292,19 +672,32 @@ async def chat_endpoint(data: ChatMessage):
async def root():
"""ヘルスチェック"""
return {
- "service": "AigptMCP Server with Memory",
+ "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 Memory starting...")
+ 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)