fix

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。

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)