feat(affinity-flow): 实现回复后关系追踪系统集成

- 在relationship_tracker.py中添加数据库支持的回复后关系追踪功能
- 新增UserRelationships数据库模型存储用户关系数据
- 集成全局关系追踪器到planner和interest_scoring系统
- 优化兴趣度评分系统的关系分获取逻辑，优先使用数据库存储的关系分
- 在plan_executor中执行回复后关系追踪，分析用户反应并更新关系
- 添加LLM响应清理功能确保JSON解析稳定性
- 更新模型配置模板添加relationship_tracker模型配置

src/chat/affinity_flow/interest_scoring.py

@@ -3,6 +3,7 @@
 基于多维度评分机制，包括兴趣匹配度、用户关系分、提及度和时间因子
 现在使用embedding计算智能兴趣匹配
 """
+
 import traceback
 from typing import Dict, List, Any
 
@@ -30,18 +31,20 @@ class InterestScoringSystem:
         }
 
         # 评分阈值
-        self.reply_threshold = 0.56    # 默认回复阈值
+        self.reply_threshold = 0.62  # 默认回复阈值
         self.mention_threshold = 0.3  # 提及阈值
 
         # 连续不回复概率提升
         self.no_reply_count = 0
-        self.max_no_reply_count = 20
+        self.max_no_reply_count = 10
-        self.probability_boost_per_no_reply = 0.02  # 每次不回复增加5%概率
+        self.probability_boost_per_no_reply = 0.01  # 每次不回复增加5%概率
 
         # 用户关系数据
         self.user_relationships: Dict[str, float] = {}  # user_id -> relationship_score
 
-    async def calculate_interest_scores(self, messages: List[DatabaseMessages], bot_nickname: str) -> List[InterestScore]:
+    async def calculate_interest_scores(
+        self, messages: List[DatabaseMessages], bot_nickname: str
+    ) -> List[InterestScore]:
         """计算消息的兴趣度评分"""
         logger.info("🚀 开始计算消息兴趣度评分...")
         logger.info(f"📨 收到 {len(messages)} 条消息")
@@ -87,9 +90,9 @@ class InterestScoringSystem:
         # 4. 计算总分
         logger.debug("🧮 计算加权总分...")
         total_score = (
-            interest_match_score * self.score_weights["interest_match"] +
+            interest_match_score * self.score_weights["interest_match"]
-            relationship_score * self.score_weights["relationship"] +
+            + relationship_score * self.score_weights["relationship"]
-            mentioned_score * self.score_weights["mentioned"]
+            + mentioned_score * self.score_weights["mentioned"]
         )
 
         details = {
@@ -108,7 +111,7 @@ class InterestScoringSystem:
             interest_match_score=interest_match_score,
             relationship_score=relationship_score,
             mentioned_score=mentioned_score,
-            details=details
+            details=details,
         )
 
     async def _calculate_interest_match_score(self, content: str, keywords: List[str] = None) -> float:
@@ -150,7 +153,9 @@ class InterestScoringSystem:
                 # 返回匹配分数，考虑置信度和匹配标签数量
                 match_count_bonus = min(len(match_result.matched_tags) * 0.05, 0.3)  # 每多匹配一个标签+0.05，最高+0.3
                 final_score = match_result.overall_score * 1.15 * match_result.confidence + match_count_bonus
-                logger.debug(f"⚖️  最终分数计算: 总分({match_result.overall_score:.3f}) × 1.3 × 置信度({match_result.confidence:.3f}) + 标签数量奖励({match_count_bonus:.3f}) = {final_score:.3f}")
+                logger.debug(
+                    f"⚖️  最终分数计算: 总分({match_result.overall_score:.3f}) × 1.3 × 置信度({match_result.confidence:.3f}) + 标签数量奖励({match_count_bonus:.3f}) = {final_score:.3f}"
+                )
                 return final_score
             else:
                 logger.warning("⚠️ 智能兴趣匹配未返回结果")
@@ -171,6 +176,7 @@ class InterestScoringSystem:
         if message.key_words:
             try:
                 import orjson
+
                 keywords = orjson.loads(message.key_words)
                 if not isinstance(keywords, list):
                     keywords = []
@@ -181,6 +187,7 @@ class InterestScoringSystem:
         if not keywords and message.key_words_lite:
             try:
                 import orjson
+
                 keywords = orjson.loads(message.key_words_lite)
                 if not isinstance(keywords, list):
                     keywords = []
@@ -198,16 +205,18 @@ class InterestScoringSystem:
         import re
 
         # 清理文本
-        content = re.sub(r'[^\w\s\u4e00-\u9fff]', ' ', content)  # 保留中文、英文、数字
+        content = re.sub(r"[^\w\s\u4e00-\u9fff]", " ", content)  # 保留中文、英文、数字
         words = content.split()
 
         # 过滤和关键词提取
         keywords = []
         for word in words:
             word = word.strip()
-            if (len(word) >= 2 and  # 至少2个字符
+            if (
-                word.isalnum() and  # 字母数字
+                len(word) >= 2  # 至少2个字符
-                not word.isdigit()):  # 不是纯数字
+                and word.isalnum()  # 字母数字
+                and not word.isdigit()
+            ):  # 不是纯数字
                 keywords.append(word.lower())
 
         # 去重并限制数量
@@ -215,11 +224,37 @@ class InterestScoringSystem:
         return unique_keywords[:10]  # 返回前10个唯一关键词
 
     def _calculate_relationship_score(self, user_id: str) -> float:
-        """计算关系分"""
+        """计算关系分 - 从数据库获取关系分"""
+        # 优先使用内存中的关系分
         if user_id in self.user_relationships:
             relationship_value = self.user_relationships[user_id]
             return min(relationship_value, 1.0)
-        return 0.3  # 默认新用户的基础分
+
+        # 如果内存中没有，尝试从关系追踪器获取
+        if hasattr(self, "relationship_tracker") and self.relationship_tracker:
+            try:
+                relationship_score = self.relationship_tracker.get_user_relationship_score(user_id)
+                # 同时更新内存缓存
+                self.user_relationships[user_id] = relationship_score
+                return relationship_score
+            except Exception as e:
+                logger.warning(f"从关系追踪器获取关系分失败: {e}")
+        else:
+            # 尝试从全局关系追踪器获取
+            try:
+                from src.chat.affinity_flow.relationship_integration import get_relationship_tracker
+
+                global_tracker = get_relationship_tracker()
+                if global_tracker:
+                    relationship_score = global_tracker.get_user_relationship_score(user_id)
+                    # 同时更新内存缓存
+                    self.user_relationships[user_id] = relationship_score
+                    return relationship_score
+            except Exception as e:
+                logger.warning(f"从全局关系追踪器获取关系分失败: {e}")
+
+        # 默认新用户的基础分
+        return 0.3
 
     def _calculate_mentioned_score(self, msg: DatabaseMessages, bot_nickname: str) -> float:
         """计算提及分数"""
@@ -312,7 +347,6 @@ class InterestScoringSystem:
             "user_relationships": len(self.user_relationships),
         }
 
-
     def reset_stats(self):
         """重置统计信息"""
         self.no_reply_count = 0
@@ -345,7 +379,9 @@ class InterestScoringSystem:
         return {
             "use_smart_matching": self.use_smart_matching,
             "smart_system_initialized": bot_interest_manager.is_initialized,
-            "smart_system_stats": bot_interest_manager.get_interest_stats() if bot_interest_manager.is_initialized else None
+            "smart_system_stats": bot_interest_manager.get_interest_stats()
+            if bot_interest_manager.is_initialized
+            else None,
         }
 
 

src/chat/affinity_flow/relationship_integration.py

@@ -0,0 +1,68 @@
+"""
+回复后关系追踪集成初始化脚本
+
+此脚本用于设置回复后关系追踪系统的全局变量和初始化连接
+确保各组件能正确协同工作
+"""
+
+from src.chat.affinity_flow.relationship_tracker import UserRelationshipTracker
+from src.chat.affinity_flow.interest_scoring import interest_scoring_system
+from src.common.logger import get_logger
+
+logger = get_logger("relationship_integration")
+
+# 全局关系追踪器实例
+relationship_tracker = None
+
+
+def initialize_relationship_tracking():
+    """初始化关系追踪系统"""
+    global relationship_tracker
+
+    try:
+        logger.info("🚀 初始化回复后关系追踪系统...")
+
+        # 创建关系追踪器实例
+        relationship_tracker = UserRelationshipTracker(interest_scoring_system=interest_scoring_system)
+
+        # 设置兴趣度评分系统的关系追踪器引用
+        interest_scoring_system.relationship_tracker = relationship_tracker
+
+        logger.info("✅ 回复后关系追踪系统初始化完成")
+        logger.info("📋 系统功能:")
+        logger.info("   🔄 自动回复后关系追踪")
+        logger.info("   💾 数据库持久化存储")
+        logger.info("   🧠 LLM智能关系分析")
+        logger.info("   ⏰ 5分钟追踪间隔")
+        logger.info("   🎯 兴趣度评分集成")
+
+        return relationship_tracker
+
+    except Exception as e:
+        logger.error(f"❌ 关系追踪系统初始化失败: {e}")
+        logger.debug("错误详情:", exc_info=True)
+        return None
+
+
+def get_relationship_tracker():
+    """获取全局关系追踪器实例"""
+    global relationship_tracker
+    return relationship_tracker
+
+
+def setup_plan_executor_relationship_tracker(plan_executor):
+    """为PlanExecutor设置关系追踪器"""
+    global relationship_tracker
+
+    if relationship_tracker and plan_executor:
+        plan_executor.set_relationship_tracker(relationship_tracker)
+        logger.info("✅ PlanExecutor关系追踪器设置完成")
+        return True
+
+    logger.warning("⚠️ 无法设置PlanExecutor关系追踪器")
+    return False
+
+
+# 自动初始化
+if __name__ == "__main__":
+    initialize_relationship_tracking()

src/chat/affinity_flow/relationship_tracker.py

@@ -1,13 +1,19 @@
 """
 用户关系追踪器
 负责追踪用户交互历史，并通过LLM分析更新用户关系分
+支持数据库持久化存储和回复后自动关系更新
 """
+
 import time
 from typing import Dict, List, Optional
 
 from src.common.logger import get_logger
 from src.config.config import model_config
 from src.llm_models.utils_model import LLMRequest
+from src.common.database.sqlalchemy_database_api import get_db_session
+from src.common.database.sqlalchemy_models import UserRelationships, Messages
+from sqlalchemy import select, desc
+from src.common.data_models.database_data_model import DatabaseMessages
 
 logger = get_logger("relationship_tracker")
 
@@ -23,16 +29,25 @@ class UserRelationshipTracker:
         self.relationship_history: List[Dict] = []
         self.interest_scoring_system = interest_scoring_system
 
+        # 数据库访问 - 使用SQLAlchemy
+        pass
+
+        # 用户关系缓存 (user_id -> {"relationship_text": str, "relationship_score": float, "last_tracked": float})
+        self.user_relationship_cache: Dict[str, Dict] = {}
+        self.cache_expiry_hours = 1  # 缓存过期时间(小时)
+
         # 关系更新LLM
         try:
             self.relationship_llm = LLMRequest(
-                model_set=model_config.model_task_config.relationship_tracker,
+                model_set=model_config.model_task_config.relationship_tracker, request_type="relationship_tracker"
-                request_type="relationship_tracker"
             )
         except AttributeError:
             # 如果relationship_tracker配置不存在，尝试其他可用的模型配置
-            available_models = [attr for attr in dir(model_config.model_task_config)
+            available_models = [
-                             if not attr.startswith('_') and attr != 'model_dump']
+                attr
+                for attr in dir(model_config.model_task_config)
+                if not attr.startswith("_") and attr != "model_dump"
+            ]
 
             if available_models:
                 # 使用第一个可用的模型配置
@@ -40,14 +55,14 @@ class UserRelationshipTracker:
                 logger.warning(f"relationship_tracker model configuration not found, using fallback: {fallback_model}")
                 self.relationship_llm = LLMRequest(
                     model_set=getattr(model_config.model_task_config, fallback_model),
-                    request_type="relationship_tracker"
+                    request_type="relationship_tracker",
                 )
             else:
                 # 如果没有任何模型配置，创建一个简单的LLMRequest
                 logger.warning("No model configurations found, creating basic LLMRequest")
                 self.relationship_llm = LLMRequest(
                     model_set="gpt-3.5-turbo",  # 默认模型
-                    request_type="relationship_tracker"
+                    request_type="relationship_tracker",
                 )
 
     def set_interest_scoring_system(self, interest_scoring_system):
@@ -58,8 +73,9 @@ class UserRelationshipTracker:
         """添加用户交互记录"""
         if len(self.tracking_users) >= self.max_tracking_users:
             # 移除最旧的记录
-            oldest_user = min(self.tracking_users.keys(),
+            oldest_user = min(
-                            key=lambda k: self.tracking_users[k].get("reply_timestamp", 0))
+                self.tracking_users.keys(), key=lambda k: self.tracking_users[k].get("reply_timestamp", 0)
+            )
             del self.tracking_users[oldest_user]
 
         # 获取当前关系分
@@ -73,7 +89,7 @@ class UserRelationshipTracker:
             "user_message": user_message,
             "bot_reply": bot_reply,
             "reply_timestamp": reply_timestamp,
-            "current_relationship_score": current_relationship_score
+            "current_relationship_score": current_relationship_score,
         }
 
         logger.debug(f"添加用户交互追踪: {user_id}")
@@ -101,11 +117,11 @@ class UserRelationshipTracker:
             prompt = f"""
 分析以下用户交互，更新用户关系：
 
-用户ID: {interaction['user_id']}
+用户ID: {interaction["user_id"]}
-用户名: {interaction['user_name']}
+用户名: {interaction["user_name"]}
-用户消息: {interaction['user_message']}
+用户消息: {interaction["user_message"]}
-Bot回复: {interaction['bot_reply']}
+Bot回复: {interaction["bot_reply"]}
-当前关系分: {interaction['current_relationship_score']}
+当前关系分: {interaction["current_relationship_score"]}
 
 请以JSON格式返回更新结果：
 {{
@@ -118,22 +134,31 @@ Bot回复: {interaction['bot_reply']}
             llm_response, _ = await self.relationship_llm.generate_response_async(prompt=prompt)
             if llm_response:
                 import json
-                response_data = json.loads(llm_response)
+
+                try:
+                    # 清理LLM响应，移除可能的格式标记
+                    cleaned_response = self._clean_llm_json_response(llm_response)
+                    response_data = json.loads(cleaned_response)
                     new_score = max(0.0, min(1.0, float(response_data.get("new_relationship_score", 0.3))))
 
                     if self.interest_scoring_system:
                         self.interest_scoring_system.update_user_relationship(
-                        interaction['user_id'],
+                            interaction["user_id"], new_score - interaction["current_relationship_score"]
-                        new_score - interaction['current_relationship_score']
                         )
 
                     return {
-                    "user_id": interaction['user_id'],
+                        "user_id": interaction["user_id"],
                         "new_relationship_score": new_score,
                         "reasoning": response_data.get("reasoning", ""),
-                    "interaction_summary": response_data.get("interaction_summary", "")
+                        "interaction_summary": response_data.get("interaction_summary", ""),
                     }
 
+                except json.JSONDecodeError as e:
+                    logger.error(f"LLM响应JSON解析失败: {e}")
+                    logger.debug(f"LLM原始响应: {llm_response}")
+                except Exception as e:
+                    logger.error(f"处理关系更新数据失败: {e}")
+
         except Exception as e:
             logger.error(f"更新用户关系时出错: {e}")
 
@@ -164,10 +189,7 @@ Bot回复: {interaction['bot_reply']}
 
     def add_to_history(self, relationship_update: Dict):
         """添加到关系历史"""
-        self.relationship_history.append({
+        self.relationship_history.append({**relationship_update, "update_time": time.time()})
-            **relationship_update,
-            "update_time": time.time()
-        })
 
         # 限制历史记录数量
         if len(self.relationship_history) > 100:
@@ -198,16 +220,13 @@ Bot回复: {interaction['bot_reply']}
         if user_id in self.tracking_users:
             current_score = self.tracking_users[user_id]["current_relationship_score"]
             if self.interest_scoring_system:
-                self.interest_scoring_system.update_user_relationship(
+                self.interest_scoring_system.update_user_relationship(user_id, new_score - current_score)
-                    user_id,
-                    new_score - current_score
-                )
 
             update_info = {
                 "user_id": user_id,
                 "new_relationship_score": new_score,
                 "reasoning": reasoning or "手动更新",
-                "interaction_summary": "手动更新关系分"
+                "interaction_summary": "手动更新关系分",
             }
             self.add_to_history(update_info)
             logger.info(f"强制更新用户关系: {user_id} -> {new_score:.2f}")
@@ -224,5 +243,371 @@ Bot回复: {interaction['bot_reply']}
             "current_relationship_score": interaction["current_relationship_score"],
             "interaction_count": 1,  # 简化版本，每次追踪只记录一次交互
             "last_interaction": interaction["reply_timestamp"],
-            "recent_message": interaction["user_message"][:100] + "..." if len(interaction["user_message"]) > 100 else interaction["user_message"]
+            "recent_message": interaction["user_message"][:100] + "..."
+            if len(interaction["user_message"]) > 100
+            else interaction["user_message"],
         }
+
+    # ===== 数据库支持方法 =====
+
+    def get_user_relationship_score(self, user_id: str) -> float:
+        """获取用户关系分"""
+        # 先检查缓存
+        if user_id in self.user_relationship_cache:
+            cache_data = self.user_relationship_cache[user_id]
+            # 检查缓存是否过期
+            cache_time = cache_data.get("last_tracked", 0)
+            if time.time() - cache_time < self.cache_expiry_hours * 3600:
+                return cache_data.get("relationship_score", 0.3)
+
+        # 缓存过期或不存在，从数据库获取
+        relationship_data = self._get_user_relationship_from_db(user_id)
+        if relationship_data:
+            # 更新缓存
+            self.user_relationship_cache[user_id] = {
+                "relationship_text": relationship_data.get("relationship_text", ""),
+                "relationship_score": relationship_data.get("relationship_score", 0.3),
+                "last_tracked": time.time(),
+            }
+            return relationship_data.get("relationship_score", 0.3)
+
+        # 数据库中也没有，返回默认值
+        return 0.3
+
+    def _get_user_relationship_from_db(self, user_id: str) -> Optional[Dict]:
+        """从数据库获取用户关系数据"""
+        try:
+            with get_db_session() as session:
+                # 查询用户关系表
+                stmt = select(UserRelationships).where(UserRelationships.user_id == user_id)
+                result = session.execute(stmt).scalar_one_or_none()
+
+                if result:
+                    return {
+                        "relationship_text": result.relationship_text or "",
+                        "relationship_score": float(result.relationship_score)
+                        if result.relationship_score is not None
+                        else 0.3,
+                        "last_updated": result.last_updated,
+                    }
+        except Exception as e:
+            logger.error(f"从数据库获取用户关系失败: {e}")
+
+        return None
+
+    def _update_user_relationship_in_db(self, user_id: str, relationship_text: str, relationship_score: float):
+        """更新数据库中的用户关系"""
+        try:
+            current_time = time.time()
+
+            with get_db_session() as session:
+                # 检查是否已存在关系记录
+                existing = session.execute(
+                    select(UserRelationships).where(UserRelationships.user_id == user_id)
+                ).scalar_one_or_none()
+
+                if existing:
+                    # 更新现有记录
+                    existing.relationship_text = relationship_text
+                    existing.relationship_score = relationship_score
+                    existing.last_updated = current_time
+                    existing.user_name = existing.user_name or user_id  # 更新用户名如果为空
+                else:
+                    # 插入新记录
+                    new_relationship = UserRelationships(
+                        user_id=user_id,
+                        user_name=user_id,
+                        relationship_text=relationship_text,
+                        relationship_score=relationship_score,
+                        last_updated=current_time,
+                    )
+                    session.add(new_relationship)
+
+                session.commit()
+                logger.info(f"已更新数据库中用户关系: {user_id} -> 分数: {relationship_score:.3f}")
+
+        except Exception as e:
+            logger.error(f"更新数据库用户关系失败: {e}")
+
+    # ===== 回复后关系追踪方法 =====
+
+    async def track_reply_relationship(
+        self, user_id: str, user_name: str, bot_reply_content: str, reply_timestamp: float
+    ):
+        """回复后关系追踪 - 主要入口点"""
+        try:
+            logger.info(f"🔄 开始回复后关系追踪: {user_id}")
+
+            # 检查上次追踪时间
+            last_tracked_time = self._get_last_tracked_time(user_id)
+            time_diff = reply_timestamp - last_tracked_time
+
+            if time_diff < 5 * 60:  # 5分钟内不重复追踪
+                logger.debug(f"用户 {user_id} 距离上次追踪时间不足5分钟，跳过")
+                return
+
+            # 获取上次bot回复该用户的消息
+            last_bot_reply = await self._get_last_bot_reply_to_user(user_id)
+            if not last_bot_reply:
+                logger.debug(f"未找到上次回复用户 {user_id} 的记录")
+                return
+
+            # 获取用户后续的反应消息
+            user_reactions = await self._get_user_reactions_after_reply(user_id, last_bot_reply.time)
+
+            # 获取当前关系数据
+            current_relationship = self._get_user_relationship_from_db(user_id)
+            current_score = current_relationship.get("relationship_score", 0.3) if current_relationship else 0.3
+            current_text = current_relationship.get("relationship_text", "新用户") if current_relationship else "新用户"
+
+            # 使用LLM分析并更新关系
+            await self._analyze_and_update_relationship(
+                user_id, user_name, last_bot_reply, user_reactions, current_text, current_score, bot_reply_content
+            )
+
+        except Exception as e:
+            logger.error(f"回复后关系追踪失败: {e}")
+            logger.debug("错误详情:", exc_info=True)
+
+    def _get_last_tracked_time(self, user_id: str) -> float:
+        """获取上次追踪时间"""
+        # 先检查缓存
+        if user_id in self.user_relationship_cache:
+            return self.user_relationship_cache[user_id].get("last_tracked", 0)
+
+        # 从数据库获取
+        relationship_data = self._get_user_relationship_from_db(user_id)
+        if relationship_data:
+            return relationship_data.get("last_updated", 0)
+
+        return 0
+
+    async def _get_last_bot_reply_to_user(self, user_id: str) -> Optional[DatabaseMessages]:
+        """获取上次bot回复该用户的消息"""
+        try:
+            with get_db_session() as session:
+                # 查询bot回复给该用户的最新消息
+                stmt = (
+                    select(Messages)
+                    .where(Messages.user_id == user_id)
+                    .where(Messages.reply_to.isnot(None))
+                    .order_by(desc(Messages.time))
+                    .limit(1)
+                )
+
+                result = session.execute(stmt).scalar_one_or_none()
+                if result:
+                    # 将SQLAlchemy模型转换为DatabaseMessages对象
+                    return self._sqlalchemy_to_database_messages(result)
+
+        except Exception as e:
+            logger.error(f"获取上次回复消息失败: {e}")
+
+        return None
+
+    async def _get_user_reactions_after_reply(self, user_id: str, reply_time: float) -> List[DatabaseMessages]:
+        """获取用户在bot回复后的反应消息"""
+        try:
+            with get_db_session() as session:
+                # 查询用户在回复时间之后的5分钟内的消息
+                end_time = reply_time + 5 * 60  # 5分钟
+
+                stmt = (
+                    select(Messages)
+                    .where(Messages.user_id == user_id)
+                    .where(Messages.time > reply_time)
+                    .where(Messages.time <= end_time)
+                    .order_by(Messages.time)
+                )
+
+                results = session.execute(stmt).scalars().all()
+                if results:
+                    return [self._sqlalchemy_to_database_messages(result) for result in results]
+
+        except Exception as e:
+            logger.error(f"获取用户反应消息失败: {e}")
+
+        return []
+
+    def _sqlalchemy_to_database_messages(self, sqlalchemy_message) -> DatabaseMessages:
+        """将SQLAlchemy消息模型转换为DatabaseMessages对象"""
+        try:
+            return DatabaseMessages(
+                message_id=sqlalchemy_message.message_id or "",
+                time=float(sqlalchemy_message.time) if sqlalchemy_message.time is not None else 0.0,
+                chat_id=sqlalchemy_message.chat_id or "",
+                reply_to=sqlalchemy_message.reply_to,
+                processed_plain_text=sqlalchemy_message.processed_plain_text or "",
+                user_id=sqlalchemy_message.user_id or "",
+                user_nickname=sqlalchemy_message.user_nickname or "",
+                user_platform=sqlalchemy_message.user_platform or "",
+            )
+        except Exception as e:
+            logger.error(f"SQLAlchemy消息转换失败: {e}")
+            # 返回一个基本的消息对象
+            return DatabaseMessages(
+                message_id="",
+                time=0.0,
+                chat_id="",
+                processed_plain_text="",
+                user_id="",
+                user_nickname="",
+                user_platform="",
+            )
+
+    async def _analyze_and_update_relationship(
+        self,
+        user_id: str,
+        user_name: str,
+        last_bot_reply: DatabaseMessages,
+        user_reactions: List[DatabaseMessages],
+        current_text: str,
+        current_score: float,
+        current_reply: str,
+    ):
+        """使用LLM分析并更新用户关系"""
+        try:
+            # 构建分析提示
+            user_reactions_text = "\n".join([f"- {msg.processed_plain_text}" for msg in user_reactions])
+
+            prompt = f"""
+分析以下用户交互，更新用户关系印象和分数：
+
+用户信息:
+- 用户ID: {user_id}
+- 用户名: {user_name}
+
+上次Bot回复: {last_bot_reply.processed_plain_text}
+
+用户反应消息:
+{user_reactions_text}
+
+当前Bot回复: {current_reply}
+
+当前关系印象: {current_text}
+当前关系分数: {current_score:.3f}
+
+请根据用户的反应和对话内容，分析用户性格特点、与Bot的互动模式，然后更新关系印象和分数。
+
+分析要点:
+1. 用户的情绪态度(积极/消极/中性)
+2. 用户对Bot的兴趣程度
+3. 用户的交流风格(主动/被动/友好/正式等)
+4. 互动的质量和深度
+
+请以JSON格式返回更新结果:
+{{
+    "relationship_text": "更新的关系印象描述(50字以内)",
+    "relationship_score": 0.0~1.0的新分数,
+    "analysis_reasoning": "分析理由说明",
+    "interaction_quality": "high/medium/low"
+}}
+"""
+
+            # 调用LLM进行分析
+            llm_response, _ = await self.relationship_llm.generate_response_async(prompt=prompt)
+
+            if llm_response:
+                import json
+
+                try:
+                    # 清理LLM响应，移除可能的格式标记
+                    cleaned_response = self._clean_llm_json_response(llm_response)
+                    response_data = json.loads(cleaned_response)
+
+                    new_text = response_data.get("relationship_text", current_text)
+                    new_score = max(0.0, min(1.0, float(response_data.get("relationship_score", current_score))))
+                    reasoning = response_data.get("analysis_reasoning", "")
+                    quality = response_data.get("interaction_quality", "medium")
+
+                    # 更新数据库
+                    self._update_user_relationship_in_db(user_id, new_text, new_score)
+
+                    # 更新缓存
+                    self.user_relationship_cache[user_id] = {
+                        "relationship_text": new_text,
+                        "relationship_score": new_score,
+                        "last_tracked": time.time(),
+                    }
+
+                    # 如果有兴趣度评分系统，也更新内存中的关系分
+                    if self.interest_scoring_system:
+                        self.interest_scoring_system.update_user_relationship(user_id, new_score - current_score)
+
+                    # 记录分析历史
+                    analysis_record = {
+                        "user_id": user_id,
+                        "timestamp": time.time(),
+                        "old_score": current_score,
+                        "new_score": new_score,
+                        "old_text": current_text,
+                        "new_text": new_text,
+                        "reasoning": reasoning,
+                        "quality": quality,
+                        "user_reactions_count": len(user_reactions),
+                    }
+                    self.relationship_history.append(analysis_record)
+
+                    # 限制历史记录数量
+                    if len(self.relationship_history) > 100:
+                        self.relationship_history = self.relationship_history[-100:]
+
+                    logger.info(f"✅ 关系分析完成: {user_id}")
+                    logger.info(f"   📝 印象: '{current_text}' -> '{new_text}'")
+                    logger.info(f"   💝 分数: {current_score:.3f} -> {new_score:.3f}")
+                    logger.info(f"   🎯 质量: {quality}")
+
+                except json.JSONDecodeError as e:
+                    logger.error(f"LLM响应JSON解析失败: {e}")
+                    logger.debug(f"LLM原始响应: {llm_response}")
+            else:
+                logger.warning("LLM未返回有效响应")
+
+        except Exception as e:
+            logger.error(f"关系分析失败: {e}")
+            logger.debug("错误详情:", exc_info=True)
+
+    def _clean_llm_json_response(self, response: str) -> str:
+        """
+        清理LLM响应，移除可能的JSON格式标记
+
+        Args:
+            response: LLM原始响应
+
+        Returns:
+            清理后的JSON字符串
+        """
+        try:
+            import re
+
+            # 移除常见的JSON格式标记
+            cleaned = response.strip()
+
+            # 移除 ```json 或 ``` 等标记
+            cleaned = re.sub(r"^```(?:json)?\s*", "", cleaned, flags=re.MULTILINE | re.IGNORECASE)
+            cleaned = re.sub(r"\s*```$", "", cleaned, flags=re.MULTILINE)
+
+            # 移除可能的Markdown代码块标记
+            cleaned = re.sub(r"^`|`$", "", cleaned, flags=re.MULTILINE)
+
+            # 尝试找到JSON对象的开始和结束
+            json_start = cleaned.find("{")
+            json_end = cleaned.rfind("}")
+
+            if json_start != -1 and json_end != -1 and json_end > json_start:
+                # 提取JSON部分
+                cleaned = cleaned[json_start : json_end + 1]
+
+            # 移除多余的空白字符
+            cleaned = cleaned.strip()
+
+            logger.debug(f"LLM响应清理: 原始长度={len(response)}, 清理后长度={len(cleaned)}")
+            if cleaned != response:
+                logger.debug(f"清理前: {response[:200]}...")
+                logger.debug(f"清理后: {cleaned[:200]}...")
+
+            return cleaned
+
+        except Exception as e:
+            logger.warning(f"清理LLM响应失败: {e}")
+            return response  # 清理失败时返回原始响应

src/chat/planner_actions/plan_executor.py

@@ -2,6 +2,7 @@
 PlanExecutor: 接收 Plan 对象并执行其中的所有动作。
 集成用户关系追踪机制，自动记录交互并更新关系。
 """
+
 import asyncio
 import time
 from typing import Dict, List
@@ -94,7 +95,9 @@ class PlanExecutor:
         self.execution_stats["successful_executions"] += successful_count
         self.execution_stats["failed_executions"] += len(execution_results) - successful_count
 
-        logger.info(f"动作执行完成: 总数={len(plan.decided_actions)}, 成功={successful_count}, 失败={len(execution_results) - successful_count}")
+        logger.info(
+            f"动作执行完成: 总数={len(plan.decided_actions)}, 成功={successful_count}, 失败={len(execution_results) - successful_count}"
+        )
 
         return {
             "executed_count": len(plan.decided_actions),
@@ -123,7 +126,7 @@ class PlanExecutor:
         try:
             logger.info(f"执行回复动作: {action_info.action_type}, 原因: {action_info.reasoning}")
 
-            if action_info.action_message.get("user_id","") == str(global_config.bot.qq_account):
+            if action_info.action_message.get("user_id", "") == str(global_config.bot.qq_account):
                 logger.warning("尝试回复自己，跳过此动作以防止死循环。")
                 return {
                     "action_type": action_info.action_type,
@@ -143,8 +146,7 @@ class PlanExecutor:
 
             # 通过动作管理器执行回复
             reply_content = await self.action_manager.execute_action(
-                action_name=action_info.action_type,
+                action_name=action_info.action_type, **action_params
-                **action_params
             )
 
             success = True
@@ -185,13 +187,15 @@ class PlanExecutor:
             for i, result in enumerate(executed_results):
                 if isinstance(result, Exception):
                     logger.error(f"执行动作 {other_actions[i].action_type} 时发生异常: {result}")
-                    results.append({
+                    results.append(
+                        {
                             "action_type": other_actions[i].action_type,
                             "success": False,
                             "error_message": str(result),
                             "execution_time": 0,
                             "reasoning": other_actions[i].reasoning,
-                    })
+                        }
+                    )
                 else:
                     results.append(result)
 
@@ -215,10 +219,7 @@ class PlanExecutor:
             }
 
             # 通过动作管理器执行动作
-            await self.action_manager.execute_action(
+            await self.action_manager.execute_action(action_name=action_info.action_type, **action_params)
-                action_name=action_info.action_type,
-                **action_params
-            )
 
             success = True
             logger.info(f"其他动作执行成功: {action_info.action_type}")
@@ -239,30 +240,49 @@ class PlanExecutor:
         }
 
     async def _track_user_interaction(self, action_info: ActionPlannerInfo, plan: Plan, reply_content: str):
-        """追踪用户交互"""
+        """追踪用户交互 - 集成回复后关系追踪"""
         try:
             if not action_info.action_message:
                 return
 
-            # 获取用户信息
+            # 获取用户信息 - 处理对象和字典两种情况
+            if hasattr(action_info.action_message, "user_id"):
+                # 对象情况
                 user_id = action_info.action_message.user_id
-            user_name = action_info.action_message.user_nickname or user_id
+                user_name = getattr(action_info.action_message, "user_nickname", user_id) or user_id
-            user_message = action_info.action_message.content
+                user_message = getattr(action_info.action_message, "content", "")
+            else:
+                # 字典情况
+                user_id = action_info.action_message.get("user_id", "")
+                user_name = action_info.action_message.get("user_nickname", user_id) or user_id
+                user_message = action_info.action_message.get("content", "")
 
-            # 如果有设置关系追踪器，添加交互记录
+            if not user_id:
+                logger.debug("跳过追踪：缺少用户ID")
+                return
+
+            # 如果有设置关系追踪器，执行回复后关系追踪
             if self.relationship_tracker:
+                # 记录基础交互信息（保持向后兼容）
                 self.relationship_tracker.add_interaction(
                     user_id=user_id,
                     user_name=user_name,
                     user_message=user_message,
                     bot_reply=reply_content,
-                    reply_timestamp=time.time()
+                    reply_timestamp=time.time(),
                 )
 
-                logger.debug(f"已添加用户交互追踪: {user_id}")
+                # 执行新的回复后关系追踪
+                await self.relationship_tracker.track_reply_relationship(
+                    user_id=user_id, user_name=user_name, bot_reply_content=reply_content, reply_timestamp=time.time()
+                )
+
+                logger.debug(f"已执行用户交互追踪: {user_id}")
 
         except Exception as e:
             logger.error(f"追踪用户交互时出错: {e}")
+            logger.debug(f"action_message类型: {type(action_info.action_message)}")
+            logger.debug(f"action_message内容: {action_info.action_message}")
 
     def get_execution_stats(self) -> Dict[str, any]:
         """获取执行统计信息"""

src/chat/planner_actions/planner.py

@@ -2,6 +2,7 @@
 主规划器入口，负责协调 PlanGenerator, PlanFilter, 和 PlanExecutor。
 集成兴趣度评分系统和用户关系追踪机制，实现智能化的聊天决策。
 """
+
 from dataclasses import asdict
 from typing import Dict, List, Optional, Tuple
 
@@ -17,10 +18,11 @@ from src.config.config import global_config
 from src.plugin_system.base.component_types import ChatMode
 
 # 导入提示词模块以确保其被初始化
-from src.chat.planner_actions import planner_prompts #noqa
+from src.chat.planner_actions import planner_prompts  # noqa
 
 logger = get_logger("planner")
 
+
 class ActionPlanner:
     """
     增强版ActionPlanner，集成兴趣度评分和用户关系追踪机制。
@@ -49,7 +51,24 @@ class ActionPlanner:
         # 初始化兴趣度评分系统
         self.interest_scoring = InterestScoringSystem()
 
-        # 初始化用户关系追踪器
+        # 尝试获取全局关系追踪器，如果没有则创建新的
+        try:
+            from src.chat.affinity_flow.relationship_integration import get_relationship_tracker
+
+            global_relationship_tracker = get_relationship_tracker()
+            if global_relationship_tracker:
+                # 使用全局关系追踪器
+                self.relationship_tracker = global_relationship_tracker
+                # 设置兴趣度评分系统的关系追踪器引用
+                self.interest_scoring.relationship_tracker = self.relationship_tracker
+                logger.info("使用全局关系追踪器")
+            else:
+                # 创建新的关系追踪器
+                self.relationship_tracker = UserRelationshipTracker(self.interest_scoring)
+                logger.info("创建新的关系追踪器实例")
+        except Exception as e:
+            logger.warning(f"获取全局关系追踪器失败: {e}")
+            # 创建新的关系追踪器
             self.relationship_tracker = UserRelationshipTracker(self.interest_scoring)
 
         # 设置执行器的关系追踪器
@@ -64,7 +83,9 @@ class ActionPlanner:
             "other_actions_executed": 0,
         }
 
-    async def plan(self, mode: ChatMode = ChatMode.FOCUS, message_data: dict = None) -> Tuple[List[Dict], Optional[Dict]]:
+    async def plan(
+        self, mode: ChatMode = ChatMode.FOCUS, message_data: dict = None
+    ) -> Tuple[List[Dict], Optional[Dict]]:
         """
         执行完整的增强版规划流程。
 
@@ -86,13 +107,14 @@ class ActionPlanner:
 
             return await self._enhanced_plan_flow(mode, unread_messages or [])
 
-
         except Exception as e:
             logger.error(f"规划流程出错: {e}")
             self.planner_stats["failed_plans"] += 1
             return [], None
 
-    async def _enhanced_plan_flow(self, mode: ChatMode, unread_messages: List[Dict]) -> Tuple[List[Dict], Optional[Dict]]:
+    async def _enhanced_plan_flow(
+        self, mode: ChatMode, unread_messages: List[Dict]
+    ) -> Tuple[List[Dict], Optional[Dict]]:
         """执行增强版规划流程"""
         try:
             # 1. 生成初始 Plan
@@ -101,9 +123,7 @@ class ActionPlanner:
             # 2. 兴趣度评分 - 只对未读消息进行评分
             if unread_messages:
                 bot_nickname = global_config.bot.nickname
-                interest_scores = await self.interest_scoring.calculate_interest_scores(
+                interest_scores = await self.interest_scoring.calculate_interest_scores(unread_messages, bot_nickname)
-                    unread_messages, bot_nickname
-                )
 
                 # 3. 根据兴趣度调整可用动作
                 if interest_scores:
@@ -123,6 +143,7 @@ class ActionPlanner:
                 logger.info(f"📊 最低要求: 阈值({base_threshold:.3f}) × 0.8 = {threshold_requirement:.3f}")
                 # 直接返回 no_action
                 from src.common.data_models.info_data_model import ActionPlannerInfo
+
                 no_action = ActionPlannerInfo(
                     action_type="no_action",
                     reasoning=f"兴趣度评分 {score:.3f} 未达阈值80% {threshold_requirement:.3f}",
@@ -133,7 +154,7 @@ class ActionPlanner:
                 filtered_plan.decided_actions = [no_action]
             else:
                 # 4. 筛选 Plan
-                filtered_plan = await self.filter.filter(reply_not_available,initial_plan)
+                filtered_plan = await self.filter.filter(reply_not_available, initial_plan)
 
             # 检查filtered_plan是否有reply动作，以便记录reply action
             has_reply_action = False
@@ -186,14 +207,12 @@ class ActionPlanner:
     def _build_return_result(self, plan: Plan) -> Tuple[List[Dict], Optional[Dict]]:
         """构建返回结果"""
         final_actions = plan.decided_actions or []
-        final_target_message = next(
+        final_target_message = next((act.action_message for act in final_actions if act.action_message), None)
-            (act.action_message for act in final_actions if act.action_message), None
-        )
 
         final_actions_dict = [asdict(act) for act in final_actions]
 
         if final_target_message:
-            if hasattr(final_target_message, '__dataclass_fields__'):
+            if hasattr(final_target_message, "__dataclass_fields__"):
                 final_target_message_dict = asdict(final_target_message)
             else:
                 final_target_message_dict = final_target_message

src/chat/replyer/default_generator.py

@@ -713,11 +713,27 @@ class DefaultReplyer:
                         msg_time = time.strftime('%H:%M:%S', time.localtime(msg.time))
                         msg_content = msg.processed_plain_text
                         
+                        # 使用与已读历史消息相同的方法获取用户名
+                        from src.person_info.person_info import PersonInfoManager, get_person_info_manager
+                        
+                        # 获取用户信息
+                        user_info = getattr(msg, 'user_info', {})
+                        platform = getattr(user_info, 'platform', '') or getattr(msg, 'platform', '')
+                        user_id = getattr(user_info, 'user_id', '') or getattr(msg, 'user_id', '')
+                        
+                        # 获取用户名
+                        if platform and user_id:
+                            person_id = PersonInfoManager.get_person_id(platform, user_id)
+                            person_info_manager = get_person_info_manager()
+                            sender_name = person_info_manager.get_value_sync(person_id, "person_name") or "未知用户"
+                        else:
+                            sender_name = "未知用户"
+    
                         # 添加兴趣度信息
                         interest_score = interest_scores.get(msg_id, 0.0)
                         interest_text = f" [兴趣度: {interest_score:.3f}]" if interest_score > 0 else ""
     
-                        unread_lines.append(f"{msg_time}: {msg_content}{interest_text}")
+                        unread_lines.append(f"{msg_time} {sender_name}: {msg_content}{interest_text}")
 
                     unread_history_prompt_str = "\n".join(unread_lines)
                     unread_history_prompt = f"这是未读历史消息，包含兴趣度评分，请优先对兴趣值高的消息做出动作：\n{unread_history_prompt_str}"
@@ -796,11 +812,27 @@ class DefaultReplyer:
                 msg_time = time.strftime('%H:%M:%S', time.localtime(msg.get("time", time.time())))
                 msg_content = msg.get("processed_plain_text", "")
                 
+                # 使用与已读历史消息相同的方法获取用户名
+                from src.person_info.person_info import PersonInfoManager, get_person_info_manager
+                
+                # 获取用户信息
+                user_info = msg.get("user_info", {})
+                platform = user_info.get("platform") or msg.get("platform", "")
+                user_id = user_info.get("user_id") or msg.get("user_id", "")
+                
+                # 获取用户名
+                if platform and user_id:
+                    person_id = PersonInfoManager.get_person_id(platform, user_id)
+                    person_info_manager = get_person_info_manager()
+                    sender_name = person_info_manager.get_value_sync(person_id, "person_name") or "未知用户"
+                else:
+                    sender_name = "未知用户"
+
                 # 添加兴趣度信息
                 interest_score = interest_scores.get(msg_id, 0.0)
                 interest_text = f" [兴趣度: {interest_score:.3f}]" if interest_score > 0 else ""
 
-                unread_lines.append(f"{msg_time}: {msg_content}{interest_text}")
+                unread_lines.append(f"{msg_time} {sender_name}: {msg_content}{interest_text}")
 
             unread_history_prompt_str = "\n".join(unread_lines)
             unread_history_prompt = f"这是未读历史消息，包含兴趣度评分，请优先对兴趣值高的消息做出动作：\n{unread_history_prompt_str}"

src/common/database/sqlalchemy_database_api.py

@@ -30,6 +30,7 @@ from src.common.database.sqlalchemy_models import (
     Schedule,
     MaiZoneScheduleStatus,
     CacheEntries,
+    UserRelationships,
 )
 
 logger = get_logger("sqlalchemy_database_api")
@@ -53,6 +54,7 @@ MODEL_MAPPING = {
     "Schedule": Schedule,
     "MaiZoneScheduleStatus": MaiZoneScheduleStatus,
     "CacheEntries": CacheEntries,
+    "UserRelationships": UserRelationships,
 }
 
 

src/common/database/sqlalchemy_models.py

@@ -696,7 +696,7 @@ def get_db_session() -> Iterator[Session]:
             raise RuntimeError("Database session not initialized")
         session = SessionLocal()
         yield session
-        #session.commit()
+        # session.commit()
     except Exception:
         if session:
             session.rollback()
@@ -748,3 +748,23 @@ class UserPermissions(Base):
         Index("idx_user_permission", "platform", "user_id", "permission_node"),
         Index("idx_permission_granted", "permission_node", "granted"),
     )
+
+
+class UserRelationships(Base):
+    """用户关系模型 - 存储用户与bot的关系数据"""
+
+    __tablename__ = "user_relationships"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    user_id = Column(get_string_field(100), nullable=False, unique=True, index=True)  # 用户ID
+    user_name = Column(get_string_field(100), nullable=True)  # 用户名
+    relationship_text = Column(Text, nullable=True)  # 关系印象描述
+    relationship_score = Column(Float, nullable=False, default=0.3)  # 关系分数(0-1)
+    last_updated = Column(Float, nullable=False, default=time.time)  # 最后更新时间
+    created_at = Column(DateTime, default=datetime.datetime.utcnow, nullable=False)  # 创建时间
+
+    __table_args__ = (
+        Index("idx_user_relationship_id", "user_id"),
+        Index("idx_relationship_score", "relationship_score"),
+        Index("idx_relationship_updated", "last_updated"),
+    )

src/config/api_ada_configs.py

@@ -143,7 +143,7 @@ class ModelTaskConfig(ValidatedConfigBase):
     monthly_plan_generator: TaskConfig = Field(..., description="月层计划生成模型配置")
     emoji_vlm: TaskConfig = Field(..., description="表情包识别模型配置")
     anti_injection: TaskConfig = Field(..., description="反注入检测专用模型配置")
-
+    relationship_tracker: TaskConfig = Field(..., description="关系追踪模型配置")
     # 处理配置文件中命名不一致的问题
     utils_video: TaskConfig = Field(..., description="视频分析模型配置（兼容配置文件中的命名）")
 

src/main.py

@@ -8,7 +8,6 @@ from maim_message import MessageServer
 from src.common.remote import TelemetryHeartBeatTask
 from src.manager.async_task_manager import async_task_manager
 from src.chat.utils.statistic import OnlineTimeRecordTask, StatisticOutputTask
-from src.common.remote import TelemetryHeartBeatTask
 from src.chat.emoji_system.emoji_manager import get_emoji_manager
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.config.config import global_config
@@ -249,6 +248,14 @@ MoFox_Bot(第三方修改版)
         get_emoji_manager().initialize()
         logger.info("表情包管理器初始化成功")
 
+        # 初始化回复后关系追踪系统
+        from src.chat.affinity_flow.relationship_integration import initialize_relationship_tracking
+        relationship_tracker = initialize_relationship_tracking()
+        if relationship_tracker:
+            logger.info("回复后关系追踪系统初始化成功")
+        else:
+            logger.warning("回复后关系追踪系统初始化失败")
+
         # 启动情绪管理器
         await mood_manager.start()
         logger.info("情绪管理器初始化成功")

src/plugins/built_in/napcat_adapter_plugin/src/send_handler.py

@@ -296,7 +296,12 @@ class SendHandler:
             return reply_seg
 
         try:
-            # 尝试通过 message_id 获取消息详情
+            # 检查是否为缓冲消息ID（格式：buffered-{original_id}-{timestamp}）
+            if id.startswith('buffered-'):
+                # 从缓冲消息ID中提取原始消息ID
+                original_id = id.split('-')[1]
+                msg_info_response = await self.send_message_to_napcat("get_msg", {"message_id": int(original_id)})
+            else:
                 msg_info_response = await self.send_message_to_napcat("get_msg", {"message_id": int(id)})
 
             replied_user_id = None

template/model_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "1.3.4"
+version = "1.3.5"
 
 # 配置文件版本号迭代规则同bot_config.toml
 
@@ -195,6 +195,11 @@ model_list = ["siliconflow-deepseek-v3"]
 temperature = 0.7
 max_tokens = 1000
 
+[model_task_config.relationship_tracker] # 用户关系追踪模型
+model_list = ["siliconflow-deepseek-v3"]
+temperature = 0.7
+max_tokens = 1000
+
 #嵌入模型
 [model_task_config.embedding]
 model_list = ["bge-m3"]