From ea7c1f22f903f9c8f2f41809449cfb2204b9b557 Mon Sep 17 00:00:00 2001 From: Windpicker-owo <3431391539@qq.com> Date: Thu, 30 Oct 2025 16:58:26 +0800 Subject: [PATCH] =?UTF-8?q?feat(relationship):=20=E9=87=8D=E6=9E=84?= =?UTF-8?q?=E5=85=B3=E7=B3=BB=E4=BF=A1=E6=81=AF=E6=8F=90=E5=8F=96=E7=B3=BB?= =?UTF-8?q?=E7=BB=9F=E5=B9=B6=E9=9B=86=E6=88=90=E8=81=8A=E5=A4=A9=E6=B5=81?= =?UTF-8?q?=E5=8D=B0=E8=B1=A1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 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 | 303 +++++++ src/chat/replyer/default_generator.py | 74 +- src/chat/utils/prompt.py | 14 +- src/common/database/sqlalchemy_models.py | 7 + src/main.py | 14 - src/mais4u/mais4u_chat/s4u_prompt.py | 24 +- src/person_info/relationship_fetcher.py | 107 ++- src/plugin_system/apis/tool_api.py | 14 +- src/plugin_system/base/base_tool.py | 3 +- src/plugin_system/core/tool_use.py | 6 +- .../affinity_interest_calculator.py | 10 +- .../chat_stream_impression_tool.py | 363 ++++++++ .../affinity_flow_chatter/plan_executor.py | 98 +-- .../built_in/affinity_flow_chatter/planner.py | 10 - .../built_in/affinity_flow_chatter/plugin.py | 16 + .../relationship_tracker.py | 820 ------------------ .../user_profile_tool.py | 370 ++++++++ 17 files changed, 1264 insertions(+), 989 deletions(-) create mode 100644 integration_test_relationship_tools.py create mode 100644 src/plugins/built_in/affinity_flow_chatter/chat_stream_impression_tool.py delete mode 100644 src/plugins/built_in/affinity_flow_chatter/relationship_tracker.py create mode 100644 src/plugins/built_in/affinity_flow_chatter/user_profile_tool.py diff --git a/integration_test_relationship_tools.py b/integration_test_relationship_tools.py new file mode 100644 index 000000000..a2ac3a7fa --- /dev/null +++ b/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") diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py index 3a90d36a0..a04804544 100644 --- a/src/chat/replyer/default_generator.py +++ b/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 - user_info = await person_info_manager.get_values(person_id, ["user_id", "platform"]) - user_id = user_info.get("user_id", "unknown") + # 获取 chat_id + chat_id = self.chat_stream.stream_id - # 从统一API获取关系数据 - 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) + # 获取 RelationshipFetcher 实例 + relationship_fetcher = relationship_fetcher_manager.get_fetcher(chat_id) - # 构建丰富的关系信息描述 - if relationship_text: - # 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" + # 构建用户关系信息（包含别名、偏好关键词等新字段） + user_relation_info = await relationship_fetcher.build_relation_info(person_id, points_num=5) - 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。" + # 构建聊天流印象信息 + stream_impression = await relationship_fetcher.build_chat_stream_impression(chat_id) + + # 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): diff --git a/src/chat/utils/prompt.py b/src/chat/utils/prompt.py index 0d83f7cfd..2e141e6ad 100644 --- a/src/chat/utils/prompt.py +++ b/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 - return await relationship_fetcher.build_relation_info(person_id, points_num=5) + # 使用关系提取器构建用户关系信息和聊天流印象 + 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. diff --git a/src/common/database/sqlalchemy_models.py b/src/common/database/sqlalchemy_models.py index 4d8046e16..9f03aa43c 100644 --- a/src/common/database/sqlalchemy_models.py +++ b/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) diff --git a/src/main.py b/src/main.py index 941814435..1400b3568 100644 --- a/src/main.py +++ b/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") diff --git a/src/mais4u/mais4u_chat/s4u_prompt.py b/src/mais4u/mais4u_chat/s4u_prompt.py index c612fab48..eba734184 100644 --- a/src/mais4u/mais4u_chat/s4u_prompt.py +++ b/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 - relation_info_list = await asyncio.gather( - *[relationship_fetcher.build_relation_info(person_id, points_num=3) for person_id in person_ids] - ) - if relation_info := "".join(relation_info_list): + # 构建用户关系信息和聊天流印象信息 + user_relation_tasks = [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) + + # 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 diff --git a/src/person_info/relationship_fetcher.py b/src/person_info/relationship_fetcher.py index 8783d5e7f..036ebefe8 100644 --- a/src/person_info/relationship_fetcher.py +++ b/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}") - if rel_data.relationship_score: + relation_parts.append(f"你对{person_name}的整体认知：{rel_data.relationship_text}") + + # 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: diff --git a/src/plugin_system/apis/tool_api.py b/src/plugin_system/apis/tool_api.py index 01ce4c7dc..2eac60402 100644 --- a/src/plugin_system/apis/tool_api.py +++ b/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: - """获取公开工具实例""" +def get_tool_instance(tool_name: str, chat_stream: Any = None) -> BaseTool | None: + """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]]: diff --git a/src/plugin_system/base/base_tool.py b/src/plugin_system/base/base_tool.py index 5cd04b485..5ad4c6dbc 100644 --- a/src/plugin_system/base/base_tool.py +++ b/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]: diff --git a/src/plugin_system/core/tool_use.py b/src/plugin_system/core/tool_use.py index 44d47eb9f..14e6fcd7c 100644 --- a/src/plugin_system/core/tool_use.py +++ b/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 diff --git a/src/plugins/built_in/affinity_flow_chatter/affinity_interest_calculator.py b/src/plugins/built_in/affinity_flow_chatter/affinity_interest_calculator.py index 524dfb80d..abf581203 100644 --- a/src/plugins/built_in/affinity_flow_chatter/affinity_interest_calculator.py +++ b/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() - if global_tracker: - relationship_score = await global_tracker.get_user_relationship_score(user_id) + relationship_data = await scoring_api.get_user_relationship_data(user_id) + if relationship_data: + 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 diff --git a/src/plugins/built_in/affinity_flow_chatter/chat_stream_impression_tool.py b/src/plugins/built_in/affinity_flow_chatter/chat_stream_impression_tool.py new file mode 100644 index 000000000..b70a7d8d3 --- /dev/null +++ b/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 diff --git a/src/plugins/built_in/affinity_flow_chatter/plan_executor.py b/src/plugins/built_in/affinity_flow_chatter/plan_executor.py index 53c327561..3af389f9f 100644 --- a/src/plugins/built_in/affinity_flow_chatter/plan_executor.py +++ b/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 + + # 将机器人回复添加到已读消息中 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 - import asyncio - 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"], diff --git a/src/plugins/built_in/affinity_flow_chatter/planner.py b/src/plugins/built_in/affinity_flow_chatter/planner.py index a24059c05..a8ae019a0 100644 --- a/src/plugins/built_in/affinity_flow_chatter/planner.py +++ b/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 diff --git a/src/plugins/built_in/affinity_flow_chatter/plugin.py b/src/plugins/built_in/affinity_flow_chatter/plugin.py index 26b83a696..6a8ee7fdb 100644 --- a/src/plugins/built_in/affinity_flow_chatter/plugin.py +++ b/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 diff --git a/src/plugins/built_in/affinity_flow_chatter/relationship_tracker.py b/src/plugins/built_in/affinity_flow_chatter/relationship_tracker.py deleted file mode 100644 index 5a0433028..000000000 --- a/src/plugins/built_in/affinity_flow_chatter/relationship_tracker.py +++ /dev/null @@ -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 diff --git a/src/plugins/built_in/affinity_flow_chatter/user_profile_tool.py b/src/plugins/built_in/affinity_flow_chatter/user_profile_tool.py new file mode 100644 index 000000000..25811a8c7 --- /dev/null +++ b/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