feat(relationship): 重构关系信息提取系统并集成聊天流印象

- 在 RelationshipFetcher 中添加 build_chat_stream_impression 方法，支持聊天流印象信息构建
- 扩展数据库模型，为 ChatStreams 表添加聊天流印象相关字段（stream_impression_text、stream_chat_style、stream_topic_keywords、stream_interest_score）
- 为 UserRelationships 表添加用户别名和偏好关键词字段（user_aliases、preference_keywords）
- 在 DefaultReplyer、Prompt 和 S4U PromptBuilder 中集成用户关系信息和聊天流印象的组合输出
- 重构工具系统，为 BaseTool 添加 chat_stream 参数支持上下文感知
- 移除旧的 ChatterRelationshipTracker 及相关关系追踪逻辑，统一使用评分API
- 在 AffinityChatterPlugin 中添加 UserProfileTool 和 ChatStreamImpressionTool 支持
- 优化计划执行器，移除关系追踪相关代码并改进错误处理

BREAKING CHANGE: 移除了 ChatterRelationshipTracker 类及相关的关系追踪功能，现在统一使用 scoring_api 进行关系管理。BaseTool 构造函数现在需要 chat_stream 参数。

integration_test_relationship_tools.py

@@ -0,0 +1,303 @@
+"""
+关系追踪工具集成测试脚本
+
+注意：此脚本需要在完整的应用环境中运行
+建议通过 bot.py 启动后在交互式环境中测试
+"""
+
+import asyncio
+
+
+async def test_user_profile_tool():
+    """测试用户画像工具"""
+    print("\n" + "=" * 80)
+    print("测试 UserProfileTool")
+    print("=" * 80)
+    
+    from src.plugins.built_in.affinity_flow_chatter.user_profile_tool import UserProfileTool
+    from src.common.database.sqlalchemy_database_api import db_query
+    from src.common.database.sqlalchemy_models import UserRelationships
+    
+    tool = UserProfileTool()
+    print(f"✅ 工具名称: {tool.name}")
+    print(f"   工具描述: {tool.description}")
+    
+    # 执行工具
+    test_user_id = "integration_test_user_001"
+    result = await tool.execute({
+        "target_user_id": test_user_id,
+        "user_aliases": "测试小明,TestMing,小明君",
+        "impression_description": "这是一个集成测试用户，性格开朗活泼，喜欢技术讨论，对AI和编程特别感兴趣。经常提出有深度的问题。",
+        "preference_keywords": "AI,Python,深度学习,游戏开发,科幻小说",
+        "affection_score": 0.85
+    })
+    
+    print(f"\n✅ 工具执行结果:")
+    print(f"   类型: {result.get('type')}")
+    print(f"   内容: {result.get('content')}")
+    
+    # 验证数据库
+    db_data = await db_query(
+        UserRelationships,
+        filters={"user_id": test_user_id},
+        limit=1
+    )
+    
+    if db_data:
+        data = db_data[0]
+        print(f"\n✅ 数据库验证:")
+        print(f"   user_id: {data.get('user_id')}")
+        print(f"   user_aliases: {data.get('user_aliases')}")
+        print(f"   relationship_text: {data.get('relationship_text', '')[:80]}...")
+        print(f"   preference_keywords: {data.get('preference_keywords')}")
+        print(f"   relationship_score: {data.get('relationship_score')}")
+        return True
+    else:
+        print(f"\n❌ 数据库中未找到数据")
+        return False
+
+
+async def test_chat_stream_impression_tool():
+    """测试聊天流印象工具"""
+    print("\n" + "=" * 80)
+    print("测试 ChatStreamImpressionTool")
+    print("=" * 80)
+    
+    from src.plugins.built_in.affinity_flow_chatter.chat_stream_impression_tool import ChatStreamImpressionTool
+    from src.common.database.sqlalchemy_database_api import db_query
+    from src.common.database.sqlalchemy_models import ChatStreams, get_db_session
+    
+    # 准备测试数据：先创建一条 ChatStreams 记录
+    test_stream_id = "integration_test_stream_001"
+    print(f"🔧 准备测试数据：创建聊天流记录 {test_stream_id}")
+    
+    import time
+    current_time = time.time()
+    
+    async with get_db_session() as session:
+        new_stream = ChatStreams(
+            stream_id=test_stream_id,
+            create_time=current_time,
+            last_active_time=current_time,
+            platform="QQ",
+            user_platform="QQ",
+            user_id="test_user_123",
+            user_nickname="测试用户",
+            group_name="测试技术交流群",
+            group_platform="QQ",
+            group_id="test_group_456",
+            stream_impression_text="",  # 初始为空
+            stream_chat_style="",
+            stream_topic_keywords="",
+            stream_interest_score=0.5
+        )
+        session.add(new_stream)
+        await session.commit()
+        print(f"✅ 测试聊天流记录已创建")
+    
+    tool = ChatStreamImpressionTool()
+    print(f"✅ 工具名称: {tool.name}")
+    print(f"   工具描述: {tool.description}")
+    
+    # 执行工具
+    result = await tool.execute({
+        "stream_id": test_stream_id,
+        "impression_description": "这是一个技术交流群，成员主要是程序员和AI爱好者。大家经常分享最新的技术文章，讨论编程问题，氛围友好且专业。",
+        "chat_style": "专业技术交流,活跃讨论,互帮互助,知识分享",
+        "topic_keywords": "Python开发,机器学习,AI应用,Web后端,数据分析,开源项目",
+        "interest_score": 0.90
+    })
+    
+    print(f"\n✅ 工具执行结果:")
+    print(f"   类型: {result.get('type')}")
+    print(f"   内容: {result.get('content')}")
+    
+    # 验证数据库
+    db_data = await db_query(
+        ChatStreams,
+        filters={"stream_id": test_stream_id},
+        limit=1
+    )
+    
+    if db_data:
+        data = db_data[0]
+        print(f"\n✅ 数据库验证:")
+        print(f"   stream_id: {data.get('stream_id')}")
+        print(f"   stream_impression_text: {data.get('stream_impression_text', '')[:80]}...")
+        print(f"   stream_chat_style: {data.get('stream_chat_style')}")
+        print(f"   stream_topic_keywords: {data.get('stream_topic_keywords')}")
+        print(f"   stream_interest_score: {data.get('stream_interest_score')}")
+        return True
+    else:
+        print(f"\n❌ 数据库中未找到数据")
+        return False
+
+
+async def test_relationship_info_build():
+    """测试关系信息构建"""
+    print("\n" + "=" * 80)
+    print("测试关系信息构建（提示词集成）")
+    print("=" * 80)
+    
+    from src.person_info.relationship_fetcher import relationship_fetcher_manager
+    
+    test_stream_id = "integration_test_stream_001"
+    test_person_id = "test_person_999"  # 使用一个可能不存在的ID来测试
+    
+    fetcher = relationship_fetcher_manager.get_fetcher(test_stream_id)
+    print(f"✅ RelationshipFetcher 已创建")
+    
+    # 测试聊天流印象构建
+    print(f"\n🔍 构建聊天流印象...")
+    stream_info = await fetcher.build_chat_stream_impression(test_stream_id)
+    
+    if stream_info:
+        print(f"✅ 聊天流印象构建成功")
+        print(f"\n{'=' * 80}")
+        print(stream_info)
+        print(f"{'=' * 80}")
+    else:
+        print(f"⚠️  聊天流印象为空（可能测试数据不存在）")
+    
+    return True
+
+
+async def cleanup_test_data():
+    """清理测试数据"""
+    print("\n" + "=" * 80)
+    print("清理测试数据")
+    print("=" * 80)
+    
+    from src.common.database.sqlalchemy_database_api import db_query
+    from src.common.database.sqlalchemy_models import UserRelationships, ChatStreams
+    
+    try:
+        # 清理用户数据
+        await db_query(
+            UserRelationships,
+            query_type="delete",
+            filters={"user_id": "integration_test_user_001"}
+        )
+        print("✅ 用户测试数据已清理")
+        
+        # 清理聊天流数据
+        await db_query(
+            ChatStreams,
+            query_type="delete",
+            filters={"stream_id": "integration_test_stream_001"}
+        )
+        print("✅ 聊天流测试数据已清理")
+        
+        return True
+    except Exception as e:
+        print(f"⚠️  清理失败: {e}")
+        return False
+
+
+async def run_all_tests():
+    """运行所有测试"""
+    print("\n" + "=" * 80)
+    print("关系追踪工具集成测试")
+    print("=" * 80)
+    
+    results = {}
+    
+    # 测试1
+    try:
+        results["UserProfileTool"] = await test_user_profile_tool()
+    except Exception as e:
+        print(f"\n❌ UserProfileTool 测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+        results["UserProfileTool"] = False
+    
+    # 测试2
+    try:
+        results["ChatStreamImpressionTool"] = await test_chat_stream_impression_tool()
+    except Exception as e:
+        print(f"\n❌ ChatStreamImpressionTool 测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+        results["ChatStreamImpressionTool"] = False
+    
+    # 测试3
+    try:
+        results["RelationshipFetcher"] = await test_relationship_info_build()
+    except Exception as e:
+        print(f"\n❌ RelationshipFetcher 测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+        results["RelationshipFetcher"] = False
+    
+    # 清理
+    try:
+        await cleanup_test_data()
+    except Exception as e:
+        print(f"\n⚠️  清理测试数据失败: {e}")
+    
+    # 总结
+    print("\n" + "=" * 80)
+    print("测试总结")
+    print("=" * 80)
+    
+    passed = sum(1 for r in results.values() if r)
+    total = len(results)
+    
+    for test_name, result in results.items():
+        status = "✅ 通过" if result else "❌ 失败"
+        print(f"{status} - {test_name}")
+    
+    print(f"\n总计: {passed}/{total} 测试通过")
+    
+    if passed == total:
+        print("\n🎉 所有测试通过！")
+    else:
+        print(f"\n⚠️  {total - passed} 个测试失败")
+    
+    return passed == total
+
+
+# 使用说明
+print("""
+============================================================================
+关系追踪工具集成测试脚本
+============================================================================
+
+此脚本需要在完整的应用环境中运行。
+
+使用方法1: 在 bot.py 中添加测试调用
+-----------------------------------
+在 bot.py 的 main() 函数中添加:
+
+    # 测试关系追踪工具
+    from tests.integration_test_relationship_tools import run_all_tests
+    await run_all_tests()
+
+使用方法2: 在 Python REPL 中运行
+-----------------------------------
+启动 bot.py 后，在 Python 调试控制台中执行:
+
+    import asyncio
+    from tests.integration_test_relationship_tools import run_all_tests
+    asyncio.create_task(run_all_tests())
+
+使用方法3: 直接在此文件底部运行
+-----------------------------------
+取消注释下面的代码，然后确保已启动应用环境
+============================================================================
+""")
+
+
+# 如果需要直接运行（需要应用环境已启动）
+if __name__ == "__main__":
+    print("\n⚠️  警告: 直接运行此脚本可能会失败，因为缺少应用环境")
+    print("建议在 bot.py 启动后的环境中运行\n")
+    
+    try:
+        asyncio.run(run_all_tests())
+    except Exception as e:
+        print(f"\n❌ 测试失败: {e}")
+        print("\n建议:")
+        print("1. 确保已启动 bot.py")
+        print("2. 在 Python 调试控制台中运行测试")
+        print("3. 或在 bot.py 中添加测试调用")

src/chat/replyer/default_generator.py

@@ -1882,42 +1882,64 @@ class DefaultReplyer:
             logger.warning(f"未找到用户 {sender} 的ID，跳过信息提取")
             return f"你完全不认识{sender}，不理解ta的相关信息。"
 
-        # 使用统一评分API获取关系信息
+        # 使用 RelationshipFetcher 获取完整关系信息（包含新字段）
         try:
-            from src.plugin_system.apis.scoring_api import scoring_api
+            from src.person_info.relationship_fetcher import relationship_fetcher_manager
 
-            # 获取用户信息以获取真实的user_id
+            # 获取 chat_id
-            user_info = await person_info_manager.get_values(person_id, ["user_id", "platform"])
+            chat_id = self.chat_stream.stream_id
-            user_id = user_info.get("user_id", "unknown")
 
-            # 从统一API获取关系数据
+            # 获取 RelationshipFetcher 实例
-            relationship_data = await scoring_api.get_user_relationship_data(user_id)
+            relationship_fetcher = relationship_fetcher_manager.get_fetcher(chat_id)
-            if relationship_data:
-                relationship_text = relationship_data.get("relationship_text", "")
-                relationship_score = relationship_data.get("relationship_score", 0.3)
 
-                # 构建丰富的关系信息描述
+            # 构建用户关系信息（包含别名、偏好关键词等新字段）
-                if relationship_text:
+            user_relation_info = await relationship_fetcher.build_relation_info(person_id, points_num=5)
-                    # 转换关系分数为描述性文本
-                    if relationship_score >= 0.8:
-                        relationship_level = "非常亲密的朋友"
-                    elif relationship_score >= 0.6:
-                        relationship_level = "好朋友"
-                    elif relationship_score >= 0.4:
-                        relationship_level = "普通朋友"
-                    elif relationship_score >= 0.2:
-                        relationship_level = "认识的人"
-                    else:
-                        relationship_level = "陌生人"
 
-                    return f"你与{sender}的关系：{relationship_level}（关系分：{relationship_score:.2f}/1.0）。{relationship_text}"
+            # 构建聊天流印象信息
-                else:
+            stream_impression = await relationship_fetcher.build_chat_stream_impression(chat_id)
-                    return f"你与{sender}是初次见面，关系分：{relationship_score:.2f}/1.0。"
+
+            # 组合两部分信息
+            if user_relation_info and stream_impression:
+                return "\n\n".join([user_relation_info, stream_impression])
+            elif user_relation_info:
+                return user_relation_info
+            elif stream_impression:
+                return stream_impression
             else:
                 return f"你完全不认识{sender}，这是第一次互动。"
 
         except Exception as e:
             logger.error(f"获取关系信息失败: {e}")
+            # 降级到基本信息
+            try:
+                from src.plugin_system.apis.scoring_api import scoring_api
+
+                user_info = await person_info_manager.get_values(person_id, ["user_id", "platform"])
+                user_id = user_info.get("user_id", "unknown")
+
+                relationship_data = await scoring_api.get_user_relationship_data(user_id)
+                if relationship_data:
+                    relationship_text = relationship_data.get("relationship_text", "")
+                    relationship_score = relationship_data.get("relationship_score", 0.3)
+
+                    if relationship_text:
+                        if relationship_score >= 0.8:
+                            relationship_level = "非常亲密的朋友"
+                        elif relationship_score >= 0.6:
+                            relationship_level = "好朋友"
+                        elif relationship_score >= 0.4:
+                            relationship_level = "普通朋友"
+                        elif relationship_score >= 0.2:
+                            relationship_level = "认识的人"
+                        else:
+                            relationship_level = "陌生人"
+
+                        return f"你与{sender}的关系：{relationship_level}（关系分：{relationship_score:.2f}/1.0）。{relationship_text}"
+                    else:
+                        return f"你与{sender}是初次见面，关系分：{relationship_score:.2f}/1.0。"
+            except Exception:
+                pass
+
             return f"你与{sender}是普通朋友关系。"
 
     async def _store_chat_memory_async(self, reply_to: str, reply_message: dict[str, Any] | None = None):

src/chat/utils/prompt.py

@@ -1109,8 +1109,18 @@ class Prompt:
             logger.warning(f"未找到用户 {sender} 的ID，跳过信息提取")
             return f"你完全不认识{sender}，不理解ta的相关信息。"
 
-        # 使用关系提取器构建关系信息
+        # 使用关系提取器构建用户关系信息和聊天流印象
-        return await relationship_fetcher.build_relation_info(person_id, points_num=5)
+        user_relation_info = await relationship_fetcher.build_relation_info(person_id, points_num=5)
+        stream_impression = await relationship_fetcher.build_chat_stream_impression(chat_id)
+        
+        # 组合两部分信息
+        info_parts = []
+        if user_relation_info:
+            info_parts.append(user_relation_info)
+        if stream_impression:
+            info_parts.append(stream_impression)
+        
+        return "\n\n".join(info_parts) if info_parts else ""
 
     def _get_default_result_for_task(self, task_name: str) -> dict[str, Any]:
         """为超时或失败的异步构建任务提供一个安全的默认返回值.

src/common/database/sqlalchemy_models.py

@@ -140,6 +140,11 @@ class ChatStreams(Base):
     consecutive_no_reply: Mapped[int | None] = mapped_column(Integer, nullable=True, default=0)
     # 消息打断系统字段
     interruption_count: Mapped[int | None] = mapped_column(Integer, nullable=True, default=0)
+    # 聊天流印象字段
+    stream_impression_text: Mapped[str | None] = mapped_column(Text, nullable=True)  # 对聊天流的主观印象描述
+    stream_chat_style: Mapped[str | None] = mapped_column(Text, nullable=True)  # 聊天流的总体风格
+    stream_topic_keywords: Mapped[str | None] = mapped_column(Text, nullable=True)  # 话题关键词，逗号分隔
+    stream_interest_score: Mapped[float | None] = mapped_column(Float, nullable=True, default=0.5)  # 对聊天流的兴趣程度(0-1)
 
     __table_args__ = (
         Index("idx_chatstreams_stream_id", "stream_id"),
@@ -877,7 +882,9 @@ class UserRelationships(Base):
     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     user_id: Mapped[str] = mapped_column(get_string_field(100), nullable=False, unique=True, index=True)
     user_name: Mapped[str | None] = mapped_column(get_string_field(100), nullable=True)
+    user_aliases: Mapped[str | None] = mapped_column(Text, nullable=True)  # 用户别名，逗号分隔
     relationship_text: Mapped[str | None] = mapped_column(Text, nullable=True)
+    preference_keywords: Mapped[str | None] = mapped_column(Text, nullable=True)  # 用户偏好关键词，逗号分隔
     relationship_score: Mapped[float] = mapped_column(Float, nullable=False, default=0.3)  # 关系分数(0-1)
     last_updated: Mapped[float] = mapped_column(Float, nullable=False, default=time.time)
     created_at: Mapped[datetime.datetime] = mapped_column(DateTime, default=datetime.datetime.utcnow, nullable=False)

src/main.py

@@ -432,20 +432,6 @@ 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
-        try:
-            from src.plugins.built_in.affinity_flow_chatter.interest_scoring import chatter_interest_scoring_system
-            from src.plugins.built_in.affinity_flow_chatter.relationship_tracker import ChatterRelationshipTracker
-
-            relationship_tracker = ChatterRelationshipTracker(interest_scoring_system=chatter_interest_scoring_system)
-            chatter_interest_scoring_system.relationship_tracker = relationship_tracker
-            logger.info("回复后关系追踪系统初始化成功")
-        except Exception as e:
-            logger.error(f"回复后关系追踪系统初始化失败: {e}")
-            relationship_tracker = None
-        """
-
         # 启动情绪管理器
         await mood_manager.start()
         logger.info("情绪管理器初始化成功")

src/mais4u/mais4u_chat/s4u_prompt.py

@@ -166,13 +166,25 @@ class PromptBuilder:
                 person_id = PersonInfoManager.get_person_id(person[0], person[1])
                 person_ids.append(person_id)
 
-            # 使用 RelationshipFetcher 的 build_relation_info 方法，设置 points_num=3 保持与原来相同的行为
+            # 构建用户关系信息和聊天流印象信息
-            relation_info_list = await asyncio.gather(
+            user_relation_tasks = [relationship_fetcher.build_relation_info(person_id, points_num=3) for person_id in person_ids]
-                *[relationship_fetcher.build_relation_info(person_id, points_num=3) for person_id in person_ids]
+            stream_impression_task = relationship_fetcher.build_chat_stream_impression(chat_stream.stream_id)
-            )
+            
-            if relation_info := "".join(relation_info_list):
+            # 并行获取所有信息
+            results = await asyncio.gather(*user_relation_tasks, stream_impression_task)
+            relation_info_list = results[:-1]  # 用户关系信息
+            stream_impression = results[-1]     # 聊天流印象
+            
+            # 组合用户关系信息和聊天流印象
+            combined_info_parts = []
+            if user_relation_info := "".join(relation_info_list):
+                combined_info_parts.append(user_relation_info)
+            if stream_impression:
+                combined_info_parts.append(stream_impression)
+            
+            if combined_info := "\n\n".join(combined_info_parts):
                 relation_prompt = await global_prompt_manager.format_prompt(
-                    "relation_prompt", relation_info=relation_info
+                    "relation_prompt", relation_info=combined_info
                 )
         return relation_prompt
 

src/person_info/relationship_fetcher.py

@@ -177,25 +177,44 @@ class RelationshipFetcher:
         if points_text:
             relation_parts.append(f"你记得关于{person_name}的一些事情：\n{points_text}")
 
-        # 5. 从UserRelationships表获取额外关系信息
+        # 5. 从UserRelationships表获取完整关系信息（新系统）
         try:
             from src.common.database.sqlalchemy_database_api import db_query
             from src.common.database.sqlalchemy_models import UserRelationships
 
             # 查询用户关系数据
+            user_id = str(person_info_manager.get_value(person_id, "user_id"))
             relationships = await db_query(
                 UserRelationships,
-                filters=[UserRelationships.user_id == str(person_info_manager.get_value(person_id, "user_id"))],
+                filters={"user_id": user_id},
                 limit=1,
             )
 
             if relationships:
                 rel_data = relationships[0]
+                
+                # 5.1 用户别名
+                if rel_data.user_aliases:
+                    aliases_list = [alias.strip() for alias in rel_data.user_aliases.split(",") if alias.strip()]
+                    if aliases_list:
+                        aliases_str = "、".join(aliases_list)
+                        relation_parts.append(f"{person_name}的别名有：{aliases_str}")
+                
+                # 5.2 关系印象文本（主观认知）
                 if rel_data.relationship_text:
-                    relation_parts.append(f"关系记录：{rel_data.relationship_text}")
+                    relation_parts.append(f"你对{person_name}的整体认知：{rel_data.relationship_text}")
-                if rel_data.relationship_score:
+                
+                # 5.3 用户偏好关键词
+                if rel_data.preference_keywords:
+                    keywords_list = [kw.strip() for kw in rel_data.preference_keywords.split(",") if kw.strip()]
+                    if keywords_list:
+                        keywords_str = "、".join(keywords_list)
+                        relation_parts.append(f"{person_name}的偏好和兴趣：{keywords_str}")
+                
+                # 5.4 关系亲密程度（好感分数）
+                if rel_data.relationship_score is not None:
                     score_desc = self._get_relationship_score_description(rel_data.relationship_score)
-                    relation_parts.append(f"关系亲密程度：{score_desc}")
+                    relation_parts.append(f"你们的关系程度：{score_desc}（{rel_data.relationship_score:.2f}）")
 
         except Exception as e:
             logger.debug(f"查询UserRelationships表失败: {e}")
@@ -210,6 +229,84 @@ class RelationshipFetcher:
 
         return relation_info
 
+    async def build_chat_stream_impression(self, stream_id: str) -> str:
+        """构建聊天流的印象信息
+        
+        Args:
+            stream_id: 聊天流ID
+            
+        Returns:
+            str: 格式化后的聊天流印象字符串
+        """
+        try:
+            from src.common.database.sqlalchemy_database_api import db_query
+            from src.common.database.sqlalchemy_models import ChatStreams
+
+            # 查询聊天流数据
+            streams = await db_query(
+                ChatStreams,
+                filters={"stream_id": stream_id},
+                limit=1,
+            )
+
+            if not streams:
+                return ""
+
+            stream_data = streams[0]
+            impression_parts = []
+
+            # 1. 聊天环境基本信息
+            if stream_data.group_name:
+                impression_parts.append(f"这是一个名为「{stream_data.group_name}」的群聊")
+            else:
+                impression_parts.append("这是一个私聊对话")
+
+            # 2. 聊天流的主观印象
+            if stream_data.stream_impression_text:
+                impression_parts.append(f"你对这个聊天环境的印象：{stream_data.stream_impression_text}")
+
+            # 3. 聊天风格
+            if stream_data.stream_chat_style:
+                impression_parts.append(f"这里的聊天风格：{stream_data.stream_chat_style}")
+
+            # 4. 常见话题
+            if stream_data.stream_topic_keywords:
+                topics_list = [topic.strip() for topic in stream_data.stream_topic_keywords.split(",") if topic.strip()]
+                if topics_list:
+                    topics_str = "、".join(topics_list)
+                    impression_parts.append(f"这里常讨论的话题：{topics_str}")
+
+            # 5. 兴趣程度
+            if stream_data.stream_interest_score is not None:
+                interest_desc = self._get_interest_score_description(stream_data.stream_interest_score)
+                impression_parts.append(f"你对这个聊天环境的兴趣程度：{interest_desc}（{stream_data.stream_interest_score:.2f}）")
+
+            # 构建最终的印象信息字符串
+            if impression_parts:
+                impression_info = "关于当前的聊天环境：\n" + "\n".join(
+                    [f"• {part}" for part in impression_parts]
+                )
+                return impression_info
+            else:
+                return ""
+
+        except Exception as e:
+            logger.debug(f"查询ChatStreams表失败: {e}")
+            return ""
+
+    def _get_interest_score_description(self, score: float) -> str:
+        """根据兴趣分数返回描述性文字"""
+        if score >= 0.8:
+            return "非常感兴趣，很喜欢这里的氛围"
+        elif score >= 0.6:
+            return "比较感兴趣，愿意积极参与"
+        elif score >= 0.4:
+            return "一般兴趣，会适度参与"
+        elif score >= 0.2:
+            return "兴趣不大，较少主动参与"
+        else:
+            return "不太感兴趣，很少参与"
+
     def _get_attitude_description(self, attitude: int) -> str:
         """根据态度分数返回描述性文字"""
         if attitude >= 80:

src/plugin_system/apis/tool_api.py

@@ -7,8 +7,16 @@ from src.plugin_system.base.component_types import ComponentType
 logger = get_logger("tool_api")
 
 
-def get_tool_instance(tool_name: str) -> BaseTool | None:
+def get_tool_instance(tool_name: str, chat_stream: Any = None) -> BaseTool | None:
-    """获取公开工具实例"""
+    """获取公开工具实例
+    
+    Args:
+        tool_name: 工具名称
+        chat_stream: 聊天流对象，用于提供上下文信息
+        
+    Returns:
+        BaseTool: 工具实例，如果工具不存在则返回None
+    """
     from src.plugin_system.core import component_registry
 
     # 获取插件配置
@@ -19,7 +27,7 @@ def get_tool_instance(tool_name: str) -> BaseTool | None:
         plugin_config = None
 
     tool_class: type[BaseTool] = component_registry.get_component_class(tool_name, ComponentType.TOOL)  # type: ignore
-    return tool_class(plugin_config) if tool_class else None
+    return tool_class(plugin_config, chat_stream) if tool_class else None
 
 
 def get_llm_available_tool_definitions() -> list[dict[str, Any]]:

src/plugin_system/base/base_tool.py

@@ -47,8 +47,9 @@ class BaseTool(ABC):
     sub_tools: list[tuple[str, str, list[tuple[str, ToolParamType, str, bool, list[str] | None]]]] = []
     """子工具列表，格式为[(子工具名, 子工具描述, 子工具参数)]。仅在二步工具中使用"""
 
-    def __init__(self, plugin_config: dict | None = None):
+    def __init__(self, plugin_config: dict | None = None, chat_stream: Any = None):
         self.plugin_config = plugin_config or {}  # 直接存储插件配置字典
+        self.chat_stream = chat_stream  # 存储聊天流信息，可用于获取上下文
 
     @classmethod
     def get_tool_definition(cls) -> dict[str, Any]:

src/plugin_system/core/tool_use.py

@@ -226,7 +226,7 @@ class ToolExecutor:
         """执行单个工具调用，并处理缓存"""
 
         function_args = tool_call.args or {}
-        tool_instance = tool_instance or get_tool_instance(tool_call.func_name)
+        tool_instance = tool_instance or get_tool_instance(tool_call.func_name, self.chat_stream)
 
         # 如果工具不存在或未启用缓存，则直接执行
         if not tool_instance or not tool_instance.enable_cache:
@@ -320,7 +320,7 @@ class ToolExecutor:
                 parts = function_name.split("_", 1)
                 if len(parts) == 2:
                     base_tool_name, sub_tool_name = parts
-                    base_tool_instance = get_tool_instance(base_tool_name)
+                    base_tool_instance = get_tool_instance(base_tool_name, self.chat_stream)
 
                     if base_tool_instance and base_tool_instance.is_two_step_tool:
                         logger.info(f"{self.log_prefix}执行二步工具第二步: {base_tool_name}.{sub_tool_name}")
@@ -340,7 +340,7 @@ class ToolExecutor:
                             }
 
             # 获取对应工具实例
-            tool_instance = tool_instance or get_tool_instance(function_name)
+            tool_instance = tool_instance or get_tool_instance(function_name, self.chat_stream)
             if not tool_instance:
                 logger.warning(f"未知工具名称: {function_name}")
                 return None

src/plugins/built_in/affinity_flow_chatter/affinity_interest_calculator.py

@@ -209,13 +209,13 @@ class AffinityInterestCalculator(BaseInterestCalculator):
             relationship_value = self.user_relationships[user_id]
             return min(relationship_value, 1.0)
 
-        # 如果内存中没有，尝试从关系追踪器获取
+        # 如果内存中没有，尝试从统一的评分API获取
         try:
-            from .relationship_tracker import ChatterRelationshipTracker
+            from src.plugin_system.apis.scoring_api import scoring_api
 
-            global_tracker = ChatterRelationshipTracker()
+            relationship_data = await scoring_api.get_user_relationship_data(user_id)
-            if global_tracker:
+            if relationship_data:
-                relationship_score = await global_tracker.get_user_relationship_score(user_id)
+                relationship_score = relationship_data.get("relationship_score", global_config.affinity_flow.base_relationship_score)
                 # 同时更新内存缓存
                 self.user_relationships[user_id] = relationship_score
                 return relationship_score

src/plugins/built_in/affinity_flow_chatter/chat_stream_impression_tool.py

@@ -0,0 +1,363 @@
+"""
+聊天流印象更新工具
+
+通过LLM二步调用机制更新对聊天流（如QQ群）的整体印象，包括主观描述、聊天风格、话题关键词和兴趣分数
+"""
+
+import json
+import time
+from typing import Any
+
+from sqlalchemy import select
+
+from src.common.database.sqlalchemy_database_api import get_db_session
+from src.common.database.sqlalchemy_models import ChatStreams
+from src.common.logger import get_logger
+from src.config.config import model_config
+from src.llm_models.utils_model import LLMRequest
+from src.plugin_system import BaseTool, ToolParamType
+
+logger = get_logger("chat_stream_impression_tool")
+
+
+class ChatStreamImpressionTool(BaseTool):
+    """聊天流印象更新工具
+    
+    使用二步调用机制：
+    1. LLM决定是否调用工具并传入初步参数（stream_id会自动传入）
+    2. 工具内部调用LLM，结合现有数据和传入参数，决定最终更新内容
+    """
+
+    name = "update_chat_stream_impression"
+    description = "当你通过观察聊天记录对当前聊天环境（群聊或私聊）产生了整体印象或认识时使用此工具，更新对这个聊天流的看法。包括：环境氛围、聊天风格、常见话题、你的兴趣程度。调用时机：当你发现这个聊天环境有明显的氛围特点（如很活跃、很专业、很闲聊）、群成员经常讨论某类话题、或者你对这个环境的感受发生变化时。注意：这是对整个聊天环境的印象，而非对单个用户。"
+    parameters = [
+        ("impression_description", ToolParamType.STRING, "你对这个聊天环境的整体感受和印象，例如'这是个技术氛围浓厚的群'、'大家都很友好热情'。当你通过聊天记录感受到环境特点时填写（可选）", False, None),
+        ("chat_style", ToolParamType.STRING, "这个聊天环境的风格特征，如'活跃热闹,互帮互助'、'严肃专业,深度讨论'、'轻松闲聊,段子频出'等。当你发现聊天方式有明显特点时填写（可选）", False, None),
+        ("topic_keywords", ToolParamType.STRING, "这个聊天环境中经常出现的话题，如'编程,AI,技术分享'或'游戏,动漫,娱乐'。当你观察到群里反复讨论某些主题时填写，多个关键词用逗号分隔（可选）", False, None),
+        ("interest_score", ToolParamType.FLOAT, "你对这个聊天环境的兴趣和喜欢程度，0.0(无聊/不喜欢)到1.0(很有趣/很喜欢)。当你对这个环境的感觉发生变化时更新（可选）", False, None),
+    ]
+    available_for_llm = True
+    history_ttl = 5
+
+    def __init__(self, plugin_config: dict | None = None):
+        super().__init__(plugin_config)
+        
+        # 初始化用于二步调用的LLM
+        try:
+            self.impression_llm = LLMRequest(
+                model_set=model_config.model_task_config.relationship_tracker,
+                request_type="chat_stream_impression_update"
+            )
+        except AttributeError:
+            # 降级处理
+            available_models = [
+                attr for attr in dir(model_config.model_task_config)
+                if not attr.startswith("_") and attr != "model_dump"
+            ]
+            if available_models:
+                fallback_model = available_models[0]
+                logger.warning(f"relationship_tracker配置不存在，使用降级模型: {fallback_model}")
+                self.impression_llm = LLMRequest(
+                    model_set=getattr(model_config.model_task_config, fallback_model),
+                    request_type="chat_stream_impression_update"
+                )
+            else:
+                logger.error("无可用的模型配置")
+                self.impression_llm = None
+
+    async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
+        """执行聊天流印象更新
+        
+        Args:
+            function_args: 工具参数，stream_id会由系统自动注入
+            
+        Returns:
+            dict: 执行结果
+        """
+        try:
+            # stream_id应该由调用方（如工具执行器）自动注入
+            # 如果没有注入，尝试从上下文获取
+            stream_id = function_args.get("stream_id")
+            if not stream_id:
+                # 尝试从其他可能的来源获取
+                logger.warning("stream_id未自动注入，尝试从其他来源获取")
+                # 这里可以添加从上下文获取的逻辑
+                return {
+                    "type": "error",
+                    "id": "chat_stream_impression",
+                    "content": "错误：无法获取当前聊天流ID"
+                }
+            
+            # 从LLM传入的参数
+            new_impression = function_args.get("impression_description", "")
+            new_style = function_args.get("chat_style", "")
+            new_topics = function_args.get("topic_keywords", "")
+            new_score = function_args.get("interest_score")
+            
+            # 从数据库获取现有聊天流印象
+            existing_impression = await self._get_stream_impression(stream_id)
+            
+            # 如果LLM没有传入任何有效参数，返回提示
+            if not any([new_impression, new_style, new_topics, new_score is not None]):
+                return {
+                    "type": "info",
+                    "id": stream_id,
+                    "content": "提示：需要提供至少一项更新内容（印象描述、聊天风格、话题关键词或兴趣分数）"
+                }
+            
+            # 调用LLM进行二步决策
+            if self.impression_llm is None:
+                logger.error("LLM未正确初始化，无法执行二步调用")
+                return {
+                    "type": "error",
+                    "id": stream_id,
+                    "content": "系统错误：LLM未正确初始化"
+                }
+            
+            final_impression = await self._llm_decide_final_impression(
+                stream_id=stream_id,
+                existing_impression=existing_impression,
+                new_impression=new_impression,
+                new_style=new_style,
+                new_topics=new_topics,
+                new_score=new_score
+            )
+            
+            if not final_impression:
+                return {
+                    "type": "error",
+                    "id": stream_id,
+                    "content": "LLM决策失败，无法更新聊天流印象"
+                }
+            
+            # 更新数据库
+            await self._update_stream_impression_in_db(stream_id, final_impression)
+            
+            # 构建返回信息
+            updates = []
+            if final_impression.get("stream_impression_text"):
+                updates.append(f"印象: {final_impression['stream_impression_text'][:50]}...")
+            if final_impression.get("stream_chat_style"):
+                updates.append(f"风格: {final_impression['stream_chat_style']}")
+            if final_impression.get("stream_topic_keywords"):
+                updates.append(f"话题: {final_impression['stream_topic_keywords']}")
+            if final_impression.get("stream_interest_score") is not None:
+                updates.append(f"兴趣分: {final_impression['stream_interest_score']:.2f}")
+            
+            result_text = f"已更新聊天流 {stream_id} 的印象：\n" + "\n".join(updates)
+            logger.info(f"聊天流印象更新成功: {stream_id}")
+            
+            return {
+                "type": "chat_stream_impression_update",
+                "id": stream_id,
+                "content": result_text
+            }
+            
+        except Exception as e:
+            logger.error(f"聊天流印象更新失败: {e}", exc_info=True)
+            return {
+                "type": "error",
+                "id": function_args.get("stream_id", "unknown"),
+                "content": f"聊天流印象更新失败: {str(e)}"
+            }
+
+    async def _get_stream_impression(self, stream_id: str) -> dict[str, Any]:
+        """从数据库获取聊天流现有印象
+        
+        Args:
+            stream_id: 聊天流ID
+            
+        Returns:
+            dict: 聊天流印象数据
+        """
+        try:
+            async with get_db_session() as session:
+                stmt = select(ChatStreams).where(ChatStreams.stream_id == stream_id)
+                result = await session.execute(stmt)
+                stream = result.scalar_one_or_none()
+                
+                if stream:
+                    return {
+                        "stream_impression_text": stream.stream_impression_text or "",
+                        "stream_chat_style": stream.stream_chat_style or "",
+                        "stream_topic_keywords": stream.stream_topic_keywords or "",
+                        "stream_interest_score": float(stream.stream_interest_score) if stream.stream_interest_score is not None else 0.5,
+                        "group_name": stream.group_name or "私聊",
+                    }
+                else:
+                    # 聊天流不存在，返回默认值
+                    return {
+                        "stream_impression_text": "",
+                        "stream_chat_style": "",
+                        "stream_topic_keywords": "",
+                        "stream_interest_score": 0.5,
+                        "group_name": "未知",
+                    }
+        except Exception as e:
+            logger.error(f"获取聊天流印象失败: {e}")
+            return {
+                "stream_impression_text": "",
+                "stream_chat_style": "",
+                "stream_topic_keywords": "",
+                "stream_interest_score": 0.5,
+                "group_name": "未知",
+            }
+
+    async def _llm_decide_final_impression(
+        self,
+        stream_id: str,
+        existing_impression: dict[str, Any],
+        new_impression: str,
+        new_style: str,
+        new_topics: str,
+        new_score: float | None
+    ) -> dict[str, Any] | None:
+        """使用LLM决策最终的聊天流印象内容
+        
+        Args:
+            stream_id: 聊天流ID
+            existing_impression: 现有印象数据
+            new_impression: LLM传入的新印象
+            new_style: LLM传入的新风格
+            new_topics: LLM传入的新话题
+            new_score: LLM传入的新分数
+            
+        Returns:
+            dict: 最终决定的印象数据，如果失败返回None
+        """
+        try:
+            # 获取bot人设
+            from src.individuality.individuality import Individuality
+            individuality = Individuality()
+            bot_personality = await individuality.get_personality_block()
+            
+            prompt = f"""
+你现在是一个有着特定性格和身份的AI助手。你的人设是：{bot_personality}
+
+你正在更新对聊天流 {stream_id} 的整体印象。
+
+【当前聊天流信息】
+- 聊天环境: {existing_impression.get('group_name', '未知')}
+- 当前印象: {existing_impression.get('stream_impression_text', '暂无印象')}
+- 聊天风格: {existing_impression.get('stream_chat_style', '未知')}
+- 常见话题: {existing_impression.get('stream_topic_keywords', '未知')}
+- 当前兴趣分: {existing_impression.get('stream_interest_score', 0.5):.2f}
+
+【本次想要更新的内容】
+- 新的印象描述: {new_impression if new_impression else '不更新'}
+- 新的聊天风格: {new_style if new_style else '不更新'}
+- 新的话题关键词: {new_topics if new_topics else '不更新'}
+- 新的兴趣分数: {new_score if new_score is not None else '不更新'}
+
+请综合考虑现有信息和新信息，决定最终的聊天流印象内容。注意：
+1. 印象描述：如果提供了新印象，应该综合现有印象和新印象，形成对这个聊天环境的整体认知（100-200字）
+2. 聊天风格：如果提供了新风格，应该用简洁的词语概括，如"活跃轻松"、"严肃专业"、"幽默随性"等
+3. 话题关键词：如果提供了新话题，应该与现有话题合并（去重），保留最核心和频繁的话题
+4. 兴趣分数：如果提供了新分数，需要结合现有分数合理调整（0.0表示完全不感兴趣，1.0表示非常感兴趣）
+
+请以JSON格式返回最终决定：
+{{
+    "stream_impression_text": "最终的印象描述（100-200字），整体性的对这个聊天环境的认知",
+    "stream_chat_style": "最终的聊天风格，简洁概括",
+    "stream_topic_keywords": "最终的话题关键词，逗号分隔",
+    "stream_interest_score": 最终的兴趣分数（0.0-1.0）,
+    "reasoning": "你的决策理由"
+}}
+"""
+            
+            # 调用LLM
+            llm_response, _ = await self.impression_llm.generate_response_async(prompt=prompt)
+            
+            if not llm_response:
+                logger.warning("LLM未返回有效响应")
+                return None
+            
+            # 清理并解析响应
+            cleaned_response = self._clean_llm_json_response(llm_response)
+            response_data = json.loads(cleaned_response)
+            
+            # 提取最终决定的数据
+            final_impression = {
+                "stream_impression_text": response_data.get("stream_impression_text", existing_impression.get("stream_impression_text", "")),
+                "stream_chat_style": response_data.get("stream_chat_style", existing_impression.get("stream_chat_style", "")),
+                "stream_topic_keywords": response_data.get("stream_topic_keywords", existing_impression.get("stream_topic_keywords", "")),
+                "stream_interest_score": max(0.0, min(1.0, float(response_data.get("stream_interest_score", existing_impression.get("stream_interest_score", 0.5))))),
+            }
+            
+            logger.info(f"LLM决策完成: {stream_id}")
+            logger.debug(f"决策理由: {response_data.get('reasoning', '无')}")
+            
+            return final_impression
+            
+        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 if 'llm_response' in locals() else 'N/A'}")
+            return None
+        except Exception as e:
+            logger.error(f"LLM决策失败: {e}", exc_info=True)
+            return None
+
+    async def _update_stream_impression_in_db(self, stream_id: str, impression: dict[str, Any]):
+        """更新数据库中的聊天流印象
+        
+        Args:
+            stream_id: 聊天流ID
+            impression: 印象数据
+        """
+        try:
+            async with get_db_session() as session:
+                stmt = select(ChatStreams).where(ChatStreams.stream_id == stream_id)
+                result = await session.execute(stmt)
+                existing = result.scalar_one_or_none()
+                
+                if existing:
+                    # 更新现有记录
+                    existing.stream_impression_text = impression.get("stream_impression_text", "")
+                    existing.stream_chat_style = impression.get("stream_chat_style", "")
+                    existing.stream_topic_keywords = impression.get("stream_topic_keywords", "")
+                    existing.stream_interest_score = impression.get("stream_interest_score", 0.5)
+                    
+                    await session.commit()
+                    logger.info(f"聊天流印象已更新到数据库: {stream_id}")
+                else:
+                    error_msg = f"聊天流 {stream_id} 不存在于数据库中，无法更新印象"
+                    logger.error(error_msg)
+                    # 注意：通常聊天流应该在消息处理时就已创建，这里不创建新记录
+                    raise ValueError(error_msg)
+                    
+        except Exception as e:
+            logger.error(f"更新聊天流印象到数据库失败: {e}", exc_info=True)
+            raise
+
+    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:
+            str: 清理后的JSON字符串
+        """
+        try:
+            import re
+            
+            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)
+            
+            # 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:
+                cleaned = cleaned[json_start:json_end + 1]
+            
+            cleaned = cleaned.strip()
+            
+            return cleaned
+            
+        except Exception as e:
+            logger.warning(f"清理LLM响应失败: {e}")
+            return response

src/plugins/built_in/affinity_flow_chatter/plan_executor.py

@@ -45,13 +45,6 @@ class ChatterPlanExecutor:
             "execution_times": [],
         }
 
-        # 用户关系追踪引用
-        self.relationship_tracker = None
-
-    def set_relationship_tracker(self, relationship_tracker):
-        """设置关系追踪器"""
-        self.relationship_tracker = relationship_tracker
-
     async def execute(self, plan: Plan) -> dict[str, Any]:
         """
         遍历并执行Plan对象中`decided_actions`列表里的所有动作。
@@ -238,19 +231,11 @@ class ChatterPlanExecutor:
         except Exception as e:
             error_message = str(e)
             logger.error(f"执行回复动作失败: {action_info.action_type}, 错误: {error_message}")
-        # 记录用户关系追踪 - 使用后台异步执行，防止阻塞主流程
-        if success and action_info.action_message:
-            logger.debug(f"准备执行关系追踪: success={success}, action_message存在={bool(action_info.action_message)}")
-            logger.debug(f"关系追踪器状态: {self.relationship_tracker is not None}")
         
-            # 直接使用后台异步任务执行关系追踪，避免阻塞主回复流程
+        # 将机器人回复添加到已读消息中
-            import asyncio
+        if success and action_info.action_message:
-            asyncio.create_task(self._track_user_interaction(action_info, plan, reply_content))
-            logger.debug("关系追踪已启动为后台异步任务")
-        else:
-            logger.debug(f"跳过关系追踪: success={success}, action_message存在={bool(action_info.action_message)}")
-            # 将机器人回复添加到已读消息中
             await self._add_bot_reply_to_read_messages(action_info, plan, reply_content)
+        
         execution_time = time.time() - start_time
         self.execution_stats["execution_times"].append(execution_time)
 
@@ -356,81 +341,6 @@ class ChatterPlanExecutor:
             "reasoning": action_info.reasoning,
         }
 
-    async def _track_user_interaction(self, action_info: ActionPlannerInfo, plan: Plan, reply_content: str):
-        """追踪用户交互 - 集成回复后关系追踪"""
-        try:
-            logger.debug("🔍 开始执行用户交互追踪")
-
-            if not action_info.action_message:
-                logger.debug("❌ 跳过追踪：action_message为空")
-                return
-
-            # 获取用户信息 - 处理DatabaseMessages对象
-            if hasattr(action_info.action_message, "user_id"):
-                # DatabaseMessages对象情况
-                user_id = action_info.action_message.user_id
-                user_name = action_info.action_message.user_nickname or user_id
-                # 使用processed_plain_text作为消息内容，如果没有则使用display_message
-                user_message = (
-                    action_info.action_message.processed_plain_text
-                    or action_info.action_message.display_message
-                    or ""
-                )
-                logger.debug(f"📝 从DatabaseMessages获取用户信息: user_id={user_id}, user_name={user_name}")
-            else:
-                # 字典情况（向后兼容）- 适配扁平化消息字典结构
-                # 首先尝试从扁平化结构直接获取用户信息
-                user_id = action_info.action_message.get("user_id")
-                user_name = action_info.action_message.get("user_nickname") or user_id
-
-                # 如果扁平化结构中没有用户信息，再尝试从嵌套的user_info获取
-                if not user_id:
-                    user_info = action_info.action_message.get("user_info", {})
-                    user_id = user_info.get("user_id")
-                    user_name = user_info.get("user_nickname") or user_id
-                    logger.debug(f"📝 从嵌套user_info获取用户信息: user_id={user_id}, user_name={user_name}")
-                else:
-                    logger.debug(f"📝 从扁平化结构获取用户信息: user_id={user_id}, user_name={user_name}")
-
-                # 获取消息内容，优先使用processed_plain_text
-                user_message = (
-                    action_info.action_message.get("processed_plain_text", "")
-                    or action_info.action_message.get("display_message", "")
-                    or action_info.action_message.get("content", "")
-                )
-
-            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:
-                logger.debug(f"✅ 关系追踪器存在，开始为用户 {user_id} 执行追踪")
-
-                # 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(),
-                )
-                logger.debug(f"📊 已添加基础交互信息: {user_name}({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"🎯 已执行回复后关系追踪: {user_id}")
-
-            else:
-                logger.debug("❌ 关系追踪器不存在，跳过追踪")
-
-        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}")
-
     async def _add_bot_reply_to_read_messages(self, action_info: ActionPlannerInfo, plan: Plan, reply_content: str):
         """将机器人回复添加到已读消息中"""
         try:
@@ -491,7 +401,7 @@ class ChatterPlanExecutor:
                 # 群组信息（如果是群聊）
                 chat_info_group_id=chat_stream.group_info.group_id if chat_stream.group_info else None,
                 chat_info_group_name=chat_stream.group_info.group_name if chat_stream.group_info else None,
-                chat_info_group_platform=chat_stream.group_info.group_platform if chat_stream.group_info else None,
+                chat_info_group_platform=getattr(chat_stream.group_info, "platform", None) if chat_stream.group_info else None,
 
                 # 动作信息
                 actions=["bot_reply"],

src/plugins/built_in/affinity_flow_chatter/planner.py

@@ -51,16 +51,6 @@ class ChatterActionPlanner:
         self.generator = ChatterPlanGenerator(chat_id)
         self.executor = ChatterPlanExecutor(action_manager)
 
-        # 初始化关系追踪器
-        if global_config.affinity_flow.enable_relationship_tracking:
-            from .relationship_tracker import ChatterRelationshipTracker
-            self.relationship_tracker = ChatterRelationshipTracker()
-            self.executor.set_relationship_tracker(self.relationship_tracker)
-            logger.info(f"关系追踪器已初始化 (chat_id: {chat_id})")
-        else:
-            self.relationship_tracker = None
-            logger.info(f"关系系统已禁用，跳过关系追踪器初始化 (chat_id: {chat_id})")
-
         # 使用新的统一兴趣度管理系统
 
         # 规划器统计

src/plugins/built_in/affinity_flow_chatter/plugin.py

@@ -52,4 +52,20 @@ class AffinityChatterPlugin(BasePlugin):
         except Exception as e:
             logger.error(f"加载 AffinityInterestCalculator 时出错: {e}")
 
+        try:
+            # 延迟导入 UserProfileTool
+            from .user_profile_tool import UserProfileTool
+
+            components.append((UserProfileTool.get_tool_info(), UserProfileTool))
+        except Exception as e:
+            logger.error(f"加载 UserProfileTool 时出错: {e}")
+
+        try:
+            # 延迟导入 ChatStreamImpressionTool
+            from .chat_stream_impression_tool import ChatStreamImpressionTool
+
+            components.append((ChatStreamImpressionTool.get_tool_info(), ChatStreamImpressionTool))
+        except Exception as e:
+            logger.error(f"加载 ChatStreamImpressionTool 时出错: {e}")
+
         return components

src/plugins/built_in/affinity_flow_chatter/relationship_tracker.py

@@ -1,820 +0,0 @@
-"""
-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 random
-import time
-
-from sqlalchemy import desc, select
-
-from src.common.data_models.database_data_model import DatabaseMessages
-from src.common.database.sqlalchemy_database_api import get_db_session
-from src.common.database.sqlalchemy_models import Messages, UserRelationships
-from src.common.logger import get_logger
-from src.config.config import global_config, model_config
-from src.llm_models.utils_model import LLMRequest
-
-logger = get_logger("chatter_relationship_tracker")
-
-
-class ChatterRelationshipTracker:
-    """用户关系追踪器"""
-
-    def __init__(self, interest_scoring_system=None):
-        self.tracking_users: dict[str, dict] = {}  # user_id -> interaction_data
-        self.max_tracking_users = 3
-        self.update_interval_minutes = 30
-        self.last_update_time = time.time()
-        self.relationship_history: list[dict] = []
-
-        # 兼容性：保留参数但不直接使用，转而使用统一API
-        self.interest_scoring_system = None  # 废弃，不再使用
-
-        # 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, 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)
-                if not attr.startswith("_") and attr != "model_dump"
-            ]
-
-            if available_models:
-                # 使用第一个可用的模型配置
-                fallback_model = available_models[0]
-                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",
-                )
-            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",
-                )
-
-    def set_interest_scoring_system(self, interest_scoring_system):
-        """设置兴趣度评分系统引用（已废弃，使用统一API）"""
-        # 不再需要设置，直接使用统一API
-        logger.info("set_interest_scoring_system 已废弃，现在使用统一评分API")
-
-    def add_interaction(self, user_id: str, user_name: str, user_message: str, bot_reply: str, reply_timestamp: float):
-        """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(), key=lambda k: self.tracking_users[k].get("reply_timestamp", 0)
-            )
-            del self.tracking_users[oldest_user]
-
-        # 获取当前关系分 - 使用缓存数据
-        current_relationship_score = global_config.affinity_flow.base_relationship_score  # 默认值
-        if user_id in self.user_relationship_cache:
-            current_relationship_score = self.user_relationship_cache[user_id].get("relationship_score", current_relationship_score)
-
-        self.tracking_users[user_id] = {
-            "user_id": user_id,
-            "user_name": user_name,
-            "user_message": user_message,
-            "bot_reply": bot_reply,
-            "reply_timestamp": reply_timestamp,
-            "current_relationship_score": current_relationship_score,
-        }
-
-        logger.debug(f"添加用户交互追踪: {user_id}")
-
-    async def check_and_update_relationships(self) -> list[dict]:
-        """检查并更新用户关系"""
-        current_time = time.time()
-        if current_time - self.last_update_time < self.update_interval_minutes * 60:
-            return []
-
-        updates = []
-        for user_id, interaction in list(self.tracking_users.items()):
-            if current_time - interaction["reply_timestamp"] > 60 * 5:  # 5分钟
-                update = await self._update_user_relationship(interaction)
-                if update:
-                    updates.append(update)
-                    del self.tracking_users[user_id]
-
-        self.last_update_time = current_time
-        return updates
-
-    async def _update_user_relationship(self, interaction: dict) -> dict | None:
-        """更新单个用户的关系"""
-        try:
-            # 获取bot人设信息
-            from src.individuality.individuality import Individuality
-
-            individuality = Individuality()
-            bot_personality = await individuality.get_personality_block()
-
-            prompt = f"""
-你现在是一个有着特定性格和身份的AI助手。你的人设是：{bot_personality}
-
-请以你独特的性格视角，严格按现实逻辑分析以下用户交互，更新用户关系：
-
-用户ID: {interaction["user_id"]}
-用户名: {interaction["user_name"]}
-用户消息: {interaction["user_message"]}
-你的回复: {interaction["bot_reply"]}
-当前关系分: {interaction["current_relationship_score"]}
-
-【重要】关系分数档次定义：
-- 0.0-0.2：陌生人/初次认识 - 仅礼貌性交流
-- 0.2-0.4：普通网友 - 有基本互动但不熟悉
-- 0.4-0.6：熟悉网友 - 经常交流，有一定了解
-- 0.6-0.8：朋友 - 可以分享心情，互相关心
-- 0.8-1.0：好朋友/知己 - 深度信任，亲密无间
-
-【严格要求】：
-1. 加分必须符合现实关系发展逻辑 - 不能因为对方态度好就盲目加分到不符合当前关系档次的分数
-2. 关系提升需要足够的互动积累和时间验证
-3. 即使是朋友关系，单次互动加分通常不超过0.05-0.1
-4. 人物印象描述应该是泛化的、整体的理解，从你的视角对用户整体性格特质的描述：
-   - 描述用户的整体性格特点（如：温柔、幽默、理性、感性等）
-   - 用户给你的整体感觉和印象
-   - 你们关系的整体状态和氛围
-   - 避免描述具体事件或对话内容，而是基于这些事件形成的整体认知
-
-根据你的人设性格，思考：
-1. 从你的性格视角，这个用户给你什么样的整体印象？
-2. 用户的性格特质和行为模式是否符合你的喜好？
-3. 基于这次互动，你对用户的整体认知有什么变化？
-4. 这个用户在你心中的整体形象是怎样的？
-
-请以JSON格式返回更新结果：
-{{
-    "new_relationship_score": 0.0~1.0的数值（必须符合现实逻辑）,
-    "reasoning": "从你的性格角度说明更新理由，重点说明是否符合现实关系发展逻辑",
-    "interaction_summary": "基于你性格的用户整体印象描述，包含用户的整体性格特质、给你的整体感觉，避免具体事件描述"
-}}
-"""
-
-            # 调用LLM进行分析 - 添加超时保护
-            import asyncio
-            try:
-                llm_response, _ = await asyncio.wait_for(
-                    self.relationship_llm.generate_response_async(prompt=prompt),
-                    timeout=30.0  # 30秒超时
-                )
-            except asyncio.TimeoutError:
-                logger.warning(f"初次见面LLM调用超时: user_id={user_id}, 跳过此次追踪")
-                return
-            except Exception as e:
-                logger.error(f"初次见面LLM调用失败: user_id={user_id}, 错误: {e}")
-                return
-
-            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_score = max(
-                        0.0,
-                        min(
-                            1.0,
-                            float(
-                                response_data.get(
-                                    "new_relationship_score", global_config.affinity_flow.base_relationship_score
-                                )
-                            ),
-                        ),
-                    )
-
-                    # 使用统一API更新关系分
-                    from src.plugin_system.apis.scoring_api import scoring_api
-                    await scoring_api.update_user_relationship(
-                        interaction["user_id"], new_score
-                    )
-
-                    return {
-                        "user_id": interaction["user_id"],
-                        "new_relationship_score": new_score,
-                        "reasoning": response_data.get("reasoning", ""),
-                        "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}")
-
-        return None
-
-    def get_tracking_users(self) -> dict[str, dict]:
-        """获取正在追踪的用户"""
-        return self.tracking_users.copy()
-
-    def get_user_interaction(self, user_id: str) -> dict | None:
-        """获取特定用户的交互记录"""
-        return self.tracking_users.get(user_id)
-
-    def remove_user_tracking(self, user_id: str):
-        """移除用户追踪"""
-        if user_id in self.tracking_users:
-            del self.tracking_users[user_id]
-            logger.debug(f"移除用户追踪: {user_id}")
-
-    def clear_all_tracking(self):
-        """清空所有追踪"""
-        self.tracking_users.clear()
-        logger.info("清空所有用户追踪")
-
-    def get_relationship_history(self) -> list[dict]:
-        """获取关系历史记录"""
-        return self.relationship_history.copy()
-
-    def add_to_history(self, relationship_update: dict):
-        """添加到关系历史"""
-        self.relationship_history.append({**relationship_update, "update_time": time.time()})
-
-        # 限制历史记录数量
-        if len(self.relationship_history) > 100:
-            self.relationship_history = self.relationship_history[-100:]
-
-    def get_tracker_stats(self) -> dict:
-        """获取追踪器统计"""
-        return {
-            "tracking_users": len(self.tracking_users),
-            "max_tracking_users": self.max_tracking_users,
-            "update_interval_minutes": self.update_interval_minutes,
-            "relationship_history": len(self.relationship_history),
-            "last_update_time": self.last_update_time,
-        }
-
-    def update_config(self, max_tracking_users: int | None = None, update_interval_minutes: int | None = None):
-        """更新配置"""
-        if max_tracking_users is not None:
-            self.max_tracking_users = max_tracking_users
-            logger.info(f"更新最大追踪用户数: {max_tracking_users}")
-
-        if update_interval_minutes is not None:
-            self.update_interval_minutes = update_interval_minutes
-            logger.info(f"更新关系更新间隔: {update_interval_minutes} 分钟")
-
-    async def force_update_relationship(self, user_id: str, new_score: float, reasoning: str = ""):
-        """强制更新用户关系分"""
-        if user_id in self.tracking_users:
-            current_score = self.tracking_users[user_id]["current_relationship_score"]
-
-            # 使用统一API更新关系分
-            from src.plugin_system.apis.scoring_api import scoring_api
-            await scoring_api.update_user_relationship(user_id, new_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",
-            }
-            self.add_to_history(update_info)
-            logger.info(f"强制更新用户关系: {user_id} -> {new_score:.2f}")
-
-    def get_user_summary(self, user_id: str) -> dict:
-        """获取用户交互总结"""
-        if user_id not in self.tracking_users:
-            return {}
-
-        interaction = self.tracking_users[user_id]
-        return {
-            "user_id": user_id,
-            "user_name": interaction["user_name"],
-            "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"],
-        }
-
-    # ===== 数据库支持方法 =====
-
-    async 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", global_config.affinity_flow.base_relationship_score)
-
-        # 缓存过期或不存在，从数据库获取
-        relationship_data = await 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", global_config.affinity_flow.base_relationship_score
-                ),
-                "last_tracked": time.time(),
-            }
-            return relationship_data.get("relationship_score", global_config.affinity_flow.base_relationship_score)
-
-        # 数据库中也没有，返回默认值
-        return global_config.affinity_flow.base_relationship_score
-
-    async def _get_user_relationship_from_db(self, user_id: str) -> dict | None:
-        """从数据库获取用户关系数据"""
-        try:
-            async with get_db_session() as session:
-                # 查询用户关系表
-                stmt = select(UserRelationships).where(UserRelationships.user_id == user_id)
-                result = await session.execute(stmt)
-                relationship = result.scalar_one_or_none()
-
-                if relationship:
-                    return {
-                        "relationship_text": relationship.relationship_text or "",
-                        "relationship_score": float(relationship.relationship_score)
-                        if relationship.relationship_score is not None
-                        else 0.3,
-                        "last_updated": relationship.last_updated,
-                    }
-        except Exception as e:
-            logger.error(f"从数据库获取用户关系失败: {e}")
-
-        return None
-
-    async 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()
-
-            async with get_db_session() as session:
-                # 检查是否已存在关系记录
-                stmt = select(UserRelationships).where(UserRelationships.user_id == user_id)
-                result = await session.execute(stmt)
-                existing = result.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)
-
-                await 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:
-            # 首先检查是否启用关系追踪
-            if not global_config.affinity_flow.enable_relationship_tracking:
-                logger.debug(f"🚫 [RelationshipTracker] 关系追踪系统已禁用，跳过用户 {user_id}")
-                return
-
-            # 概率筛选 - 减少API调用压力
-            tracking_probability = global_config.affinity_flow.relationship_tracking_probability
-            if random.random() > tracking_probability:
-                logger.debug(
-                    f"🎲 [RelationshipTracker] 概率筛选未通过 ({tracking_probability:.2f})，跳过用户 {user_id} 的关系追踪"
-                )
-                return
-
-            logger.info(f"🔄 [RelationshipTracker] 开始回复后关系追踪: {user_id} (概率通过: {tracking_probability:.2f})")
-
-            # 检查上次追踪时间 - 使用配置的冷却时间
-            last_tracked_time = await self._get_last_tracked_time(user_id)
-            cooldown_hours = global_config.affinity_flow.relationship_tracking_cooldown_hours
-            cooldown_seconds = cooldown_hours * 3600
-            time_diff = reply_timestamp - last_tracked_time
-
-            # 使用配置的最小间隔时间
-            min_interval = global_config.affinity_flow.relationship_tracking_interval_min
-            required_interval = max(min_interval, cooldown_seconds)
-
-            if time_diff < required_interval:
-                logger.debug(
-                    f"⏱️ [RelationshipTracker] 用户 {user_id} 距离上次追踪时间不足 {required_interval/60:.1f} 分钟 "
-                    f"(实际: {time_diff/60:.1f} 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.info(f"👋 [RelationshipTracker] 未找到用户 {user_id} 的历史回复记录，启动'初次见面'逻辑")
-                await self._handle_first_interaction(user_id, user_name, bot_reply_content)
-                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)
-            logger.debug(f"💬 [RelationshipTracker] 找到用户 {user_id} 在上次回复后的 {len(user_reactions)} 条反应消息")
-
-            # 获取当前关系数据
-            current_relationship = await self._get_user_relationship_from_db(user_id)
-            current_score = (
-                current_relationship.get("relationship_score", global_config.affinity_flow.base_relationship_score)
-                if current_relationship
-                else global_config.affinity_flow.base_relationship_score
-            )
-            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
-            logger.debug(f"🧠 [RelationshipTracker] 开始为用户 {user_id} 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)
-
-    async 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 = await 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) -> DatabaseMessages | None:
-        """获取上次bot回复该用户的消息"""
-        try:
-            async 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 = await session.execute(stmt)
-                message = result.scalar_one_or_none()
-                if message:
-                    # 将SQLAlchemy模型转换为DatabaseMessages对象
-                    return self._sqlalchemy_to_database_messages(message)
-
-        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:
-            async 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)
-                )
-
-                result = await session.execute(stmt)
-                messages = result.scalars().all()
-                if messages:
-                    return [self._sqlalchemy_to_database_messages(message) for message in messages]
-
-        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])
-
-            # 获取bot人设信息
-            from src.individuality.individuality import Individuality
-
-            individuality = Individuality()
-            bot_personality = await individuality.get_personality_block()
-
-            prompt = f"""
-你现在是一个有着特定性格和身份的AI助手。你的人设是：{bot_personality}
-
-请以你独特的性格视角，严格按现实逻辑分析以下用户交互，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}
-
-你上次的回复: {last_bot_reply.processed_plain_text}
-
-用户反应消息:
-{user_reactions_text}
-
-你当前的回复: {current_reply}
-
-当前关系印象: {current_text}
-当前关系分数: {current_score:.3f}
-
-【重要】关系分数档次定义：
-- 0.0-0.2：陌生人/初次认识 - 仅礼貌性交流
-- 0.2-0.4：普通网友 - 有基本互动但不熟悉
-- 0.4-0.6：熟悉网友 - 经常交流，有一定了解
-- 0.6-0.8：朋友 - 可以分享心情，互相关心
-- 0.8-1.0：好朋友/知己 - 深度信任，亲密无间
-
-【严格要求】：
-1. 加分必须符合现实关系发展逻辑 - 不能因为用户反应好就盲目加分
-2. 关系提升需要足够的互动积累和时间验证，单次互动加分通常不超过0.05-0.1
-3. 必须考虑当前关系档次，不能跳跃式提升（比如从0.3直接到0.7）
-4. 人物印象描述应该是泛化的、整体的理解（100-200字），从你的视角对用户整体性格特质的描述：
-   - 描述用户的整体性格特点和行为模式（如：温柔体贴、幽默风趣、理性稳重等）
-   - 用户给你的整体感觉和印象氛围
-   - 你们关系的整体状态和发展阶段
-   - 基于所有互动形成的用户整体形象认知
-   - 避免提及具体事件或对话内容，而是总结形成的整体印象
-5. 在撰写人物印象时，请根据已有信息自然地融入用户的性别。如果性别不确定，请使用中性描述。
-
-性格视角深度分析:
-1. 从你的性格视角，基于这次互动，你对用户的整体印象有什么新的认识？
-2. 用户的整体性格特质和行为模式符合你的喜好吗？
-3. 从现实角度看，这次互动是否足以让关系提升到下一个档次？为什么？
-4. 基于你们的互动历史，用户在你心中的整体形象是怎样的？
-5. 这个用户给你带来的整体感受和情绪体验是怎样的？
-
-请以JSON格式返回更新结果:
-{{
-    "relationship_text": "泛化的用户整体印象描述(100-200字)，其中自然地体现用户的性别，包含用户的整体性格特质、给你的整体感觉和印象氛围，避免具体事件描述",
-    "relationship_score": 0.0~1.0的新分数（必须严格符合现实逻辑）,
-    "analysis_reasoning": "从你性格角度的深度分析，重点说明分数调整的现实合理性",
-    "interaction_quality": "high/medium/low"
-}}
-"""
-
-            # 调用LLM进行分析 - 添加超时保护
-            import asyncio
-            try:
-                llm_response, _ = await asyncio.wait_for(
-                    self.relationship_llm.generate_response_async(prompt=prompt),
-                    timeout=30.0  # 30秒超时
-                )
-            except asyncio.TimeoutError:
-                logger.warning(f"关系追踪LLM调用超时: user_id={user_id}, 跳过此次追踪")
-                return
-            except Exception as e:
-                logger.error(f"关系追踪LLM调用失败: user_id={user_id}, 错误: {e}")
-                return
-
-            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
-                    await 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(),
-                    }
-
-                    # 使用统一API更新关系分（内存缓存已通过数据库更新自动处理）
-                    # 数据库更新后，缓存会在下次访问时自动同步
-
-                    # 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)
-
-    async def _handle_first_interaction(self, user_id: str, user_name: str, bot_reply_content: str):
-        """处理与用户的初次交互"""
-        try:
-            # 初次交互也进行概率检查，但使用更高的通过率
-            first_interaction_probability = min(1.0, global_config.affinity_flow.relationship_tracking_probability * 1.5)
-            if random.random() > first_interaction_probability:
-                logger.debug(
-                    f"🎲 [RelationshipTracker] 初次交互概率筛选未通过 ({first_interaction_probability:.2f})，跳过用户 {user_id}"
-                )
-                return
-
-            logger.info(f"✨ [RelationshipTracker] 正在处理与用户 {user_id} 的初次交互 (概率通过: {first_interaction_probability:.2f})")
-
-            # 获取bot人设信息
-            from src.individuality.individuality import Individuality
-
-            individuality = Individuality()
-            bot_personality = await individuality.get_personality_block()
-
-            prompt = f"""
-你现在是：{bot_personality}
-
-你正在与一个新用户进行初次有效互动。请根据你对TA的第一印象，建立初始关系档案。
-
-用户信息:
-- 用户ID: {user_id}
-- 用户名: {user_name}
-
-你的首次回复: {bot_reply_content}
-
-【严格要求】：
-1. 建立一个初始关系分数，通常在0.2-0.4之间（普通网友）。
-2. 初始关系印象描述要简洁地记录你对用户的整体初步看法（50-100字）。请在描述中自然地融入你对用户性别的初步判断（例如“他似乎是...”或“感觉她...”），如果完全无法判断，则使用中性描述。
-   - 基于用户名和初次互动，用户给你的整体感觉
-   - 你感受到的用户整体性格特质倾向
-   - 你对与这个用户建立关系的整体期待和感觉
-   - 避免描述具体的事件细节，而是整体的直觉印象
-
-请以JSON格式返回结果:
-{{
-    "relationship_text": "简洁的用户整体初始印象描述(50-100字)，其中自然地体现对用户性别的初步判断",
-    "relationship_score": 0.2~0.4的新分数,
-    "analysis_reasoning": "从你性格角度说明建立此初始印象的理由"
-}}
-"""
-            # 调用LLM进行分析
-            llm_response, _ = await self.relationship_llm.generate_response_async(prompt=prompt)
-            if not llm_response:
-                logger.warning(f"初次交互分析时LLM未返回有效响应: {user_id}")
-                return
-
-            import json
-
-            cleaned_response = self._clean_llm_json_response(llm_response)
-            response_data = json.loads(cleaned_response)
-
-            new_text = response_data.get("relationship_text", "初次见面")
-            new_score = max(
-                0.0,
-                min(
-                    1.0,
-                    float(response_data.get("relationship_score", global_config.affinity_flow.base_relationship_score)),
-                ),
-            )
-
-            # 更新数据库和缓存
-            await self._update_user_relationship_in_db(user_id, new_text, new_score)
-            self.user_relationship_cache[user_id] = {
-                "relationship_text": new_text,
-                "relationship_score": new_score,
-                "last_tracked": time.time(),
-            }
-
-            logger.info(f"✅ [RelationshipTracker] 已成功为新用户 {user_id} 建立初始关系档案，分数为 {new_score:.3f}")
-
-        except Exception as e:
-            logger.error(f"处理初次交互失败: {user_id}, 错误: {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/plugins/built_in/affinity_flow_chatter/user_profile_tool.py

@@ -0,0 +1,370 @@
+"""
+用户画像更新工具
+
+通过LLM二步调用机制更新用户画像信息，包括别名、主观印象、偏好关键词和好感分数
+"""
+
+import orjson
+import time
+from typing import Any
+
+from sqlalchemy import select
+
+from src.common.database.sqlalchemy_database_api import get_db_session
+from src.common.database.sqlalchemy_models import UserRelationships
+from src.common.logger import get_logger
+from src.config.config import global_config, model_config
+from src.llm_models.utils_model import LLMRequest
+from src.plugin_system import BaseTool, ToolParamType
+
+logger = get_logger("user_profile_tool")
+
+
+class UserProfileTool(BaseTool):
+    """用户画像更新工具
+    
+    使用二步调用机制：
+    1. LLM决定是否调用工具并传入初步参数
+    2. 工具内部调用LLM，结合现有数据和传入参数，决定最终更新内容
+    """
+
+    name = "update_user_profile"
+    description = "当你通过聊天记录对某个用户产生了新的认识或印象时使用此工具，更新该用户的画像信息。包括：用户别名、你对TA的主观印象、TA的偏好兴趣、你对TA的好感程度。调用时机：当你发现用户透露了新的个人信息、展现了性格特点、表达了兴趣偏好，或者你们的互动让你对TA的看法发生变化时。"
+    parameters = [
+        ("target_user_id", ToolParamType.STRING, "目标用户的ID（必须）", True, None),
+        ("user_aliases", ToolParamType.STRING, "该用户的昵称或别名，如果发现用户自称或被他人称呼的其他名字时填写，多个别名用逗号分隔（可选）", False, None),
+        ("impression_description", ToolParamType.STRING, "你对该用户的整体印象和性格感受，例如'这个用户很幽默开朗'、'TA对技术很有热情'等。当你通过对话了解到用户的性格、态度、行为特点时填写（可选）", False, None),
+        ("preference_keywords", ToolParamType.STRING, "该用户表现出的兴趣爱好或偏好，如'编程,游戏,动漫'。当用户谈论自己喜欢的事物时填写，多个关键词用逗号分隔（可选）", False, None),
+        ("affection_score", ToolParamType.FLOAT, "你对该用户的好感程度，0.0(陌生/不喜欢)到1.0(很喜欢/好友)。当你们的互动让你对TA的感觉发生变化时更新（可选）", False, None),
+    ]
+    available_for_llm = True
+    history_ttl = 5
+
+    def __init__(self, plugin_config: dict | None = None):
+        super().__init__(plugin_config)
+        
+        # 初始化用于二步调用的LLM
+        try:
+            self.profile_llm = LLMRequest(
+                model_set=model_config.model_task_config.relationship_tracker,
+                request_type="user_profile_update"
+            )
+        except AttributeError:
+            # 降级处理
+            available_models = [
+                attr for attr in dir(model_config.model_task_config)
+                if not attr.startswith("_") and attr != "model_dump"
+            ]
+            if available_models:
+                fallback_model = available_models[0]
+                logger.warning(f"relationship_tracker配置不存在，使用降级模型: {fallback_model}")
+                self.profile_llm = LLMRequest(
+                    model_set=getattr(model_config.model_task_config, fallback_model),
+                    request_type="user_profile_update"
+                )
+            else:
+                logger.error("无可用的模型配置")
+                self.profile_llm = None
+
+    async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
+        """执行用户画像更新
+        
+        Args:
+            function_args: 工具参数
+            
+        Returns:
+            dict: 执行结果
+        """
+        try:
+            # 提取参数
+            target_user_id = function_args.get("target_user_id")
+            if not target_user_id:
+                return {
+                    "type": "error",
+                    "id": "user_profile_update",
+                    "content": "错误：必须提供目标用户ID"
+                }
+            
+            # 从LLM传入的参数
+            new_aliases = function_args.get("user_aliases", "")
+            new_impression = function_args.get("impression_description", "")
+            new_keywords = function_args.get("preference_keywords", "")
+            new_score = function_args.get("affection_score")
+            
+            # 从数据库获取现有用户画像
+            existing_profile = await self._get_user_profile(target_user_id)
+            
+            # 如果LLM没有传入任何有效参数，返回提示
+            if not any([new_aliases, new_impression, new_keywords, new_score is not None]):
+                return {
+                    "type": "info",
+                    "id": target_user_id,
+                    "content": f"提示：需要提供至少一项更新内容（别名、印象描述、偏好关键词或好感分数）"
+                }
+            
+            # 调用LLM进行二步决策
+            if self.profile_llm is None:
+                logger.error("LLM未正确初始化，无法执行二步调用")
+                return {
+                    "type": "error",
+                    "id": target_user_id,
+                    "content": "系统错误：LLM未正确初始化"
+                }
+            
+            final_profile = await self._llm_decide_final_profile(
+                target_user_id=target_user_id,
+                existing_profile=existing_profile,
+                new_aliases=new_aliases,
+                new_impression=new_impression,
+                new_keywords=new_keywords,
+                new_score=new_score
+            )
+            
+            if not final_profile:
+                return {
+                    "type": "error",
+                    "id": target_user_id,
+                    "content": "LLM决策失败，无法更新用户画像"
+                }
+            
+            # 更新数据库
+            await self._update_user_profile_in_db(target_user_id, final_profile)
+            
+            # 构建返回信息
+            updates = []
+            if final_profile.get("user_aliases"):
+                updates.append(f"别名: {final_profile['user_aliases']}")
+            if final_profile.get("relationship_text"):
+                updates.append(f"印象: {final_profile['relationship_text'][:50]}...")
+            if final_profile.get("preference_keywords"):
+                updates.append(f"偏好: {final_profile['preference_keywords']}")
+            if final_profile.get("relationship_score") is not None:
+                updates.append(f"好感分: {final_profile['relationship_score']:.2f}")
+            
+            result_text = f"已更新用户 {target_user_id} 的画像：\n" + "\n".join(updates)
+            logger.info(f"用户画像更新成功: {target_user_id}")
+            
+            return {
+                "type": "user_profile_update",
+                "id": target_user_id,
+                "content": result_text
+            }
+            
+        except Exception as e:
+            logger.error(f"用户画像更新失败: {e}", exc_info=True)
+            return {
+                "type": "error",
+                "id": function_args.get("target_user_id", "unknown"),
+                "content": f"用户画像更新失败: {str(e)}"
+            }
+
+    async def _get_user_profile(self, user_id: str) -> dict[str, Any]:
+        """从数据库获取用户现有画像
+        
+        Args:
+            user_id: 用户ID
+            
+        Returns:
+            dict: 用户画像数据
+        """
+        try:
+            async with get_db_session() as session:
+                stmt = select(UserRelationships).where(UserRelationships.user_id == user_id)
+                result = await session.execute(stmt)
+                profile = result.scalar_one_or_none()
+                
+                if profile:
+                    return {
+                        "user_name": profile.user_name or user_id,
+                        "user_aliases": profile.user_aliases or "",
+                        "relationship_text": profile.relationship_text or "",
+                        "preference_keywords": profile.preference_keywords or "",
+                        "relationship_score": float(profile.relationship_score) if profile.relationship_score is not None else global_config.affinity_flow.base_relationship_score,
+                    }
+                else:
+                    # 用户不存在，返回默认值
+                    return {
+                        "user_name": user_id,
+                        "user_aliases": "",
+                        "relationship_text": "",
+                        "preference_keywords": "",
+                        "relationship_score": global_config.affinity_flow.base_relationship_score,
+                    }
+        except Exception as e:
+            logger.error(f"获取用户画像失败: {e}")
+            return {
+                "user_name": user_id,
+                "user_aliases": "",
+                "relationship_text": "",
+                "preference_keywords": "",
+                "relationship_score": global_config.affinity_flow.base_relationship_score,
+            }
+
+    async def _llm_decide_final_profile(
+        self,
+        target_user_id: str,
+        existing_profile: dict[str, Any],
+        new_aliases: str,
+        new_impression: str,
+        new_keywords: str,
+        new_score: float | None
+    ) -> dict[str, Any] | None:
+        """使用LLM决策最终的用户画像内容
+        
+        Args:
+            target_user_id: 目标用户ID
+            existing_profile: 现有画像数据
+            new_aliases: LLM传入的新别名
+            new_impression: LLM传入的新印象
+            new_keywords: LLM传入的新关键词
+            new_score: LLM传入的新分数
+            
+        Returns:
+            dict: 最终决定的画像数据，如果失败返回None
+        """
+        try:
+            # 获取bot人设
+            from src.individuality.individuality import Individuality
+            individuality = Individuality()
+            bot_personality = await individuality.get_personality_block()
+            
+            prompt = f"""
+你现在是一个有着特定性格和身份的AI助手。你的人设是：{bot_personality}
+
+你正在更新对用户 {target_user_id} 的画像认识。
+
+【当前画像信息】
+- 用户名: {existing_profile.get('user_name', target_user_id)}
+- 已知别名: {existing_profile.get('user_aliases', '无')}
+- 当前印象: {existing_profile.get('relationship_text', '暂无印象')}
+- 偏好关键词: {existing_profile.get('preference_keywords', '未知')}
+- 当前好感分: {existing_profile.get('relationship_score', 0.3):.2f}
+
+【本次想要更新的内容】
+- 新增/更新别名: {new_aliases if new_aliases else '不更新'}
+- 新的印象描述: {new_impression if new_impression else '不更新'}
+- 新的偏好关键词: {new_keywords if new_keywords else '不更新'}
+- 新的好感分数: {new_score if new_score is not None else '不更新'}
+
+请综合考虑现有信息和新信息，决定最终的用户画像内容。注意：
+1. 别名：如果提供了新别名，应该与现有别名合并（去重），而不是替换
+2. 印象描述：如果提供了新印象，应该综合现有印象和新印象，形成更完整的认识（100-200字）
+3. 偏好关键词：如果提供了新关键词，应该与现有关键词合并（去重），每个关键词简短
+4. 好感分数：如果提供了新分数，需要结合现有分数合理调整（变化不宜过大，遵循现实逻辑）
+
+请以JSON格式返回最终决定：
+{{
+    "user_aliases": "最终的别名列表，逗号分隔",
+    "relationship_text": "最终的印象描述（100-200字），整体性、泛化的理解",
+    "preference_keywords": "最终的偏好关键词，逗号分隔",
+    "relationship_score": 最终的好感分数（0.0-1.0）,
+    "reasoning": "你的决策理由"
+}}
+"""
+            
+            # 调用LLM
+            llm_response, _ = await self.profile_llm.generate_response_async(prompt=prompt)
+            
+            if not llm_response:
+                logger.warning("LLM未返回有效响应")
+                return None
+            
+            # 清理并解析响应
+            cleaned_response = self._clean_llm_json_response(llm_response)
+            response_data = orjson.loads(cleaned_response)
+            
+            # 提取最终决定的数据
+            final_profile = {
+                "user_aliases": response_data.get("user_aliases", existing_profile.get("user_aliases", "")),
+                "relationship_text": response_data.get("relationship_text", existing_profile.get("relationship_text", "")),
+                "preference_keywords": response_data.get("preference_keywords", existing_profile.get("preference_keywords", "")),
+                "relationship_score": max(0.0, min(1.0, float(response_data.get("relationship_score", existing_profile.get("relationship_score", 0.3))))),
+            }
+            
+            logger.info(f"LLM决策完成: {target_user_id}")
+            logger.debug(f"决策理由: {response_data.get('reasoning', '无')}")
+            
+            return final_profile
+            
+        except orjson.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 if 'llm_response' in locals() else 'N/A'}")
+            return None
+        except Exception as e:
+            logger.error(f"LLM决策失败: {e}", exc_info=True)
+            return None
+
+    async def _update_user_profile_in_db(self, user_id: str, profile: dict[str, Any]):
+        """更新数据库中的用户画像
+        
+        Args:
+            user_id: 用户ID
+            profile: 画像数据
+        """
+        try:
+            current_time = time.time()
+            
+            async with get_db_session() as session:
+                stmt = select(UserRelationships).where(UserRelationships.user_id == user_id)
+                result = await session.execute(stmt)
+                existing = result.scalar_one_or_none()
+                
+                if existing:
+                    # 更新现有记录
+                    existing.user_aliases = profile.get("user_aliases", "")
+                    existing.relationship_text = profile.get("relationship_text", "")
+                    existing.preference_keywords = profile.get("preference_keywords", "")
+                    existing.relationship_score = profile.get("relationship_score", global_config.affinity_flow.base_relationship_score)
+                    existing.last_updated = current_time
+                else:
+                    # 创建新记录
+                    new_profile = UserRelationships(
+                        user_id=user_id,
+                        user_name=user_id,
+                        user_aliases=profile.get("user_aliases", ""),
+                        relationship_text=profile.get("relationship_text", ""),
+                        preference_keywords=profile.get("preference_keywords", ""),
+                        relationship_score=profile.get("relationship_score", global_config.affinity_flow.base_relationship_score),
+                        last_updated=current_time
+                    )
+                    session.add(new_profile)
+                
+                await session.commit()
+                logger.info(f"用户画像已更新到数据库: {user_id}")
+                
+        except Exception as e:
+            logger.error(f"更新用户画像到数据库失败: {e}", exc_info=True)
+            raise
+
+    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:
+            str: 清理后的JSON字符串
+        """
+        try:
+            import re
+            
+            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)
+            
+            # 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:
+                cleaned = cleaned[json_start:json_end + 1]
+            
+            cleaned = cleaned.strip()
+            
+            return cleaned
+            
+        except Exception as e:
+            logger.warning(f"清理LLM响应失败: {e}")
+            return response