Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox_Bot into dev
This commit is contained in:
tt-P607
303
integration_test_relationship_tools.py
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303
integration_test_relationship_tools.py
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@@ -0,0 +1,303 @@
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"""
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关系追踪工具集成测试脚本
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注意：此脚本需要在完整的应用环境中运行
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建议通过 bot.py 启动后在交互式环境中测试
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"""
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import asyncio
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async def test_user_profile_tool():
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"""测试用户画像工具"""
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print("\n" + "=" * 80)
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print("测试 UserProfileTool")
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print("=" * 80)
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from src.plugins.built_in.affinity_flow_chatter.user_profile_tool import UserProfileTool
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from src.common.database.sqlalchemy_database_api import db_query
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from src.common.database.sqlalchemy_models import UserRelationships
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tool = UserProfileTool()
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print(f"✅ 工具名称: {tool.name}")
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print(f" 工具描述: {tool.description}")
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# 执行工具
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test_user_id = "integration_test_user_001"
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result = await tool.execute({
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"target_user_id": test_user_id,
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"user_aliases": "测试小明,TestMing,小明君",
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"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。",
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"preference_keywords": "AI,Python,深度学习,游戏开发,科幻小说",
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"affection_score": 0.85
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})
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print(f"\n✅ 工具执行结果:")
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print(f" 类型: {result.get('type')}")
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print(f" 内容: {result.get('content')}")
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# 验证数据库
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db_data = await db_query(
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UserRelationships,
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filters={"user_id": test_user_id},
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limit=1
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)
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if db_data:
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data = db_data[0]
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print(f"\n✅ 数据库验证:")
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print(f" user_id: {data.get('user_id')}")
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print(f" user_aliases: {data.get('user_aliases')}")
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print(f" relationship_text: {data.get('relationship_text', '')[:80]}...")
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print(f" preference_keywords: {data.get('preference_keywords')}")
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print(f" relationship_score: {data.get('relationship_score')}")
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return True
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else:
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print(f"\n❌ 数据库中未找到数据")
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return False
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async def test_chat_stream_impression_tool():
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"""测试聊天流印象工具"""
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print("\n" + "=" * 80)
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print("测试 ChatStreamImpressionTool")
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print("=" * 80)
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from src.plugins.built_in.affinity_flow_chatter.chat_stream_impression_tool import ChatStreamImpressionTool
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from src.common.database.sqlalchemy_database_api import db_query
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from src.common.database.sqlalchemy_models import ChatStreams, get_db_session
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# 准备测试数据：先创建一条 ChatStreams 记录
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test_stream_id = "integration_test_stream_001"
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print(f"🔧 准备测试数据：创建聊天流记录 {test_stream_id}")
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import time
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current_time = time.time()
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async with get_db_session() as session:
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new_stream = ChatStreams(
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stream_id=test_stream_id,
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create_time=current_time,
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last_active_time=current_time,
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platform="QQ",
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user_platform="QQ",
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user_id="test_user_123",
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user_nickname="测试用户",
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group_name="测试技术交流群",
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group_platform="QQ",
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group_id="test_group_456",
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stream_impression_text="", # 初始为空
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stream_chat_style="",
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stream_topic_keywords="",
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stream_interest_score=0.5
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)
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session.add(new_stream)
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await session.commit()
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print(f"✅ 测试聊天流记录已创建")
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tool = ChatStreamImpressionTool()
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print(f"✅ 工具名称: {tool.name}")
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print(f" 工具描述: {tool.description}")
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# 执行工具
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result = await tool.execute({
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"stream_id": test_stream_id,
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"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。",
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"chat_style": "专业技术交流,活跃讨论,互帮互助,知识分享",
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"topic_keywords": "Python开发,机器学习,AI应用,Web后端,数据分析,开源项目",
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"interest_score": 0.90
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})
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print(f"\n✅ 工具执行结果:")
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print(f" 类型: {result.get('type')}")
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print(f" 内容: {result.get('content')}")
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# 验证数据库
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db_data = await db_query(
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ChatStreams,
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filters={"stream_id": test_stream_id},
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limit=1
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)
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if db_data:
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data = db_data[0]
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print(f"\n✅ 数据库验证:")
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print(f" stream_id: {data.get('stream_id')}")
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print(f" stream_impression_text: {data.get('stream_impression_text', '')[:80]}...")
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print(f" stream_chat_style: {data.get('stream_chat_style')}")
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print(f" stream_topic_keywords: {data.get('stream_topic_keywords')}")
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print(f" stream_interest_score: {data.get('stream_interest_score')}")
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return True
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else:
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print(f"\n❌ 数据库中未找到数据")
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return False
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async def test_relationship_info_build():
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"""测试关系信息构建"""
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print("\n" + "=" * 80)
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print("测试关系信息构建（提示词集成）")
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print("=" * 80)
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from src.person_info.relationship_fetcher import relationship_fetcher_manager
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test_stream_id = "integration_test_stream_001"
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test_person_id = "test_person_999" # 使用一个可能不存在的ID来测试
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fetcher = relationship_fetcher_manager.get_fetcher(test_stream_id)
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print(f"✅ RelationshipFetcher 已创建")
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# 测试聊天流印象构建
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print(f"\n🔍 构建聊天流印象...")
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stream_info = await fetcher.build_chat_stream_impression(test_stream_id)
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if stream_info:
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print(f"✅ 聊天流印象构建成功")
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print(f"\n{'=' * 80}")
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print(stream_info)
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print(f"{'=' * 80}")
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else:
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print(f"⚠️ 聊天流印象为空（可能测试数据不存在）")
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return True
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async def cleanup_test_data():
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"""清理测试数据"""
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print("\n" + "=" * 80)
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print("清理测试数据")
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print("=" * 80)
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from src.common.database.sqlalchemy_database_api import db_query
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from src.common.database.sqlalchemy_models import UserRelationships, ChatStreams
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try:
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# 清理用户数据
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await db_query(
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UserRelationships,
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query_type="delete",
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filters={"user_id": "integration_test_user_001"}
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)
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print("✅ 用户测试数据已清理")
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# 清理聊天流数据
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await db_query(
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ChatStreams,
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query_type="delete",
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filters={"stream_id": "integration_test_stream_001"}
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)
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print("✅ 聊天流测试数据已清理")
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return True
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except Exception as e:
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print(f"⚠️ 清理失败: {e}")
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return False
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async def run_all_tests():
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"""运行所有测试"""
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print("\n" + "=" * 80)
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print("关系追踪工具集成测试")
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print("=" * 80)
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results = {}
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# 测试1
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try:
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results["UserProfileTool"] = await test_user_profile_tool()
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except Exception as e:
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print(f"\n❌ UserProfileTool 测试失败: {e}")
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import traceback
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traceback.print_exc()
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results["UserProfileTool"] = False
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# 测试2
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try:
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results["ChatStreamImpressionTool"] = await test_chat_stream_impression_tool()
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except Exception as e:
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print(f"\n❌ ChatStreamImpressionTool 测试失败: {e}")
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import traceback
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traceback.print_exc()
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results["ChatStreamImpressionTool"] = False
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# 测试3
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try:
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results["RelationshipFetcher"] = await test_relationship_info_build()
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except Exception as e:
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print(f"\n❌ RelationshipFetcher 测试失败: {e}")
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import traceback
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traceback.print_exc()
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results["RelationshipFetcher"] = False
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# 清理
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try:
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await cleanup_test_data()
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except Exception as e:
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print(f"\n⚠️ 清理测试数据失败: {e}")
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# 总结
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print("\n" + "=" * 80)
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print("测试总结")
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print("=" * 80)
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passed = sum(1 for r in results.values() if r)
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total = len(results)
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for test_name, result in results.items():
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status = "✅ 通过" if result else "❌ 失败"
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print(f"{status} - {test_name}")
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print(f"\n总计: {passed}/{total} 测试通过")
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if passed == total:
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print("\n🎉 所有测试通过！")
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else:
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print(f"\n⚠️ {total - passed} 个测试失败")
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return passed == total
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# 使用说明
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print("""
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============================================================================
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关系追踪工具集成测试脚本
|
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============================================================================
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||||
|
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此脚本需要在完整的应用环境中运行。
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使用方法1: 在 bot.py 中添加测试调用
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-----------------------------------
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在 bot.py 的 main() 函数中添加:
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# 测试关系追踪工具
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from tests.integration_test_relationship_tools import run_all_tests
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await run_all_tests()
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|
||||
使用方法2: 在 Python REPL 中运行
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-----------------------------------
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启动 bot.py 后，在 Python 调试控制台中执行:
|
||||
|
||||
import asyncio
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from tests.integration_test_relationship_tools import run_all_tests
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asyncio.create_task(run_all_tests())
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||||
|
||||
使用方法3: 直接在此文件底部运行
|
||||
-----------------------------------
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||||
取消注释下面的代码，然后确保已启动应用环境
|
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============================================================================
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""")
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||||
|
||||
# 如果需要直接运行（需要应用环境已启动）
|
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if __name__ == "__main__":
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print("\n⚠️ 警告: 直接运行此脚本可能会失败，因为缺少应用环境")
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print("建议在 bot.py 启动后的环境中运行\n")
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try:
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||||
asyncio.run(run_all_tests())
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||||
except Exception as e:
|
||||
print(f"\n❌ 测试失败: {e}")
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||||
print("\n建议:")
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||||
print("1. 确保已启动 bot.py")
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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")
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116
scripts/check_expression_database.py
Normal file
116
scripts/check_expression_database.py
Normal file
@@ -0,0 +1,116 @@
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"""
|
||||
检查表达方式数据库状态的诊断脚本
|
||||
"""
|
||||
import asyncio
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# 添加项目根目录到路径
|
||||
project_root = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from sqlalchemy import select, func
|
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from src.common.database.sqlalchemy_database_api import get_db_session
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from src.common.database.sqlalchemy_models import Expression
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|
||||
|
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async def check_database():
|
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"""检查表达方式数据库状态"""
|
||||
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||||
print("=" * 60)
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||||
print("表达方式数据库诊断报告")
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||||
print("=" * 60)
|
||||
|
||||
async with get_db_session() as session:
|
||||
# 1. 统计总数
|
||||
total_count = await session.execute(select(func.count()).select_from(Expression))
|
||||
total = total_count.scalar()
|
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print(f"\n📊 总表达方式数量: {total}")
|
||||
|
||||
if total == 0:
|
||||
print("\n⚠️ 数据库为空！")
|
||||
print("\n可能的原因:")
|
||||
print("1. 还没有进行过表达学习")
|
||||
print("2. 配置中禁用了表达学习")
|
||||
print("3. 学习过程中发生了错误")
|
||||
print("\n建议:")
|
||||
print("- 检查 bot_config.toml 中的 [expression] 配置")
|
||||
print("- 查看日志中是否有表达学习相关的错误")
|
||||
print("- 确认聊天流的 learn_expression 配置为 true")
|
||||
return
|
||||
|
||||
# 2. 按 chat_id 统计
|
||||
print("\n📝 按聊天流统计:")
|
||||
chat_counts = await session.execute(
|
||||
select(Expression.chat_id, func.count())
|
||||
.group_by(Expression.chat_id)
|
||||
)
|
||||
for chat_id, count in chat_counts:
|
||||
print(f" - {chat_id}: {count} 个表达方式")
|
||||
|
||||
# 3. 按 type 统计
|
||||
print("\n📝 按类型统计:")
|
||||
type_counts = await session.execute(
|
||||
select(Expression.type, func.count())
|
||||
.group_by(Expression.type)
|
||||
)
|
||||
for expr_type, count in type_counts:
|
||||
print(f" - {expr_type}: {count} 个")
|
||||
|
||||
# 4. 检查 situation 和 style 字段是否有空值
|
||||
print("\n🔍 字段完整性检查:")
|
||||
null_situation = await session.execute(
|
||||
select(func.count())
|
||||
.select_from(Expression)
|
||||
.where(Expression.situation == None)
|
||||
)
|
||||
null_style = await session.execute(
|
||||
select(func.count())
|
||||
.select_from(Expression)
|
||||
.where(Expression.style == None)
|
||||
)
|
||||
|
||||
null_sit_count = null_situation.scalar()
|
||||
null_sty_count = null_style.scalar()
|
||||
|
||||
print(f" - situation 为空: {null_sit_count} 个")
|
||||
print(f" - style 为空: {null_sty_count} 个")
|
||||
|
||||
if null_sit_count > 0 or null_sty_count > 0:
|
||||
print(" ⚠️ 发现空值！这会导致匹配失败")
|
||||
|
||||
# 5. 显示一些样例数据
|
||||
print("\n📋 样例数据 (前10条):")
|
||||
samples = await session.execute(
|
||||
select(Expression)
|
||||
.limit(10)
|
||||
)
|
||||
|
||||
for i, expr in enumerate(samples.scalars(), 1):
|
||||
print(f"\n [{i}] Chat: {expr.chat_id}")
|
||||
print(f" Type: {expr.type}")
|
||||
print(f" Situation: {expr.situation}")
|
||||
print(f" Style: {expr.style}")
|
||||
print(f" Count: {expr.count}")
|
||||
|
||||
# 6. 检查 style 字段的唯一值
|
||||
print("\n📋 Style 字段样例 (前20个):")
|
||||
unique_styles = await session.execute(
|
||||
select(Expression.style)
|
||||
.distinct()
|
||||
.limit(20)
|
||||
)
|
||||
|
||||
styles = [s for s in unique_styles.scalars()]
|
||||
for style in styles:
|
||||
print(f" - {style}")
|
||||
|
||||
print(f"\n (共 {len(styles)} 个不同的 style)")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("诊断完成")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(check_database())
|
||||
65
scripts/check_style_field.py
Normal file
65
scripts/check_style_field.py
Normal file
@@ -0,0 +1,65 @@
|
||||
"""
|
||||
检查数据库中 style 字段的内容特征
|
||||
"""
|
||||
import asyncio
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
project_root = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from sqlalchemy import select
|
||||
from src.common.database.sqlalchemy_database_api import get_db_session
|
||||
from src.common.database.sqlalchemy_models import Expression
|
||||
|
||||
|
||||
async def analyze_style_fields():
|
||||
"""分析 style 字段的内容"""
|
||||
|
||||
print("=" * 60)
|
||||
print("Style 字段内容分析")
|
||||
print("=" * 60)
|
||||
|
||||
async with get_db_session() as session:
|
||||
# 获取所有表达方式
|
||||
result = await session.execute(select(Expression).limit(30))
|
||||
expressions = result.scalars().all()
|
||||
|
||||
print(f"\n总共检查 {len(expressions)} 条记录\n")
|
||||
|
||||
# 按类型分类
|
||||
style_examples = []
|
||||
|
||||
for expr in expressions:
|
||||
if expr.type == "style":
|
||||
style_examples.append({
|
||||
"situation": expr.situation,
|
||||
"style": expr.style,
|
||||
"length": len(expr.style) if expr.style else 0
|
||||
})
|
||||
|
||||
print("📋 Style 类型样例 (前15条):")
|
||||
print("="*60)
|
||||
for i, ex in enumerate(style_examples[:15], 1):
|
||||
print(f"\n[{i}]")
|
||||
print(f" Situation: {ex['situation']}")
|
||||
print(f" Style: {ex['style']}")
|
||||
print(f" 长度: {ex['length']} 字符")
|
||||
|
||||
# 判断是具体表达还是风格描述
|
||||
if ex['length'] <= 20 and any(word in ex['style'] for word in ['简洁', '短句', '陈述', '疑问', '感叹', '省略', '完整']):
|
||||
style_type = "✓ 风格描述"
|
||||
elif ex['length'] <= 10:
|
||||
style_type = "? 可能是具体表达（较短）"
|
||||
else:
|
||||
style_type = "✗ 具体表达内容"
|
||||
|
||||
print(f" 类型判断: {style_type}")
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("分析完成")
|
||||
print("="*60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(analyze_style_fields())
|
||||
88
scripts/debug_style_learner.py
Normal file
88
scripts/debug_style_learner.py
Normal file
@@ -0,0 +1,88 @@
|
||||
"""
|
||||
检查 StyleLearner 模型状态的诊断脚本
|
||||
"""
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# 添加项目根目录到路径
|
||||
project_root = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from src.chat.express.style_learner import style_learner_manager
|
||||
from src.common.logger import get_logger
|
||||
|
||||
logger = get_logger("debug_style_learner")
|
||||
|
||||
|
||||
def check_style_learner_status(chat_id: str):
|
||||
"""检查指定 chat_id 的 StyleLearner 状态"""
|
||||
|
||||
print("=" * 60)
|
||||
print(f"StyleLearner 状态诊断 - Chat ID: {chat_id}")
|
||||
print("=" * 60)
|
||||
|
||||
# 获取 learner
|
||||
learner = style_learner_manager.get_learner(chat_id)
|
||||
|
||||
# 1. 基本信息
|
||||
print(f"\n📊 基本信息:")
|
||||
print(f" Chat ID: {learner.chat_id}")
|
||||
print(f" 风格数量: {len(learner.style_to_id)}")
|
||||
print(f" 下一个ID: {learner.next_style_id}")
|
||||
print(f" 最大风格数: {learner.max_styles}")
|
||||
|
||||
# 2. 学习统计
|
||||
print(f"\n📈 学习统计:")
|
||||
print(f" 总样本数: {learner.learning_stats['total_samples']}")
|
||||
print(f" 最后更新: {learner.learning_stats.get('last_update', 'N/A')}")
|
||||
|
||||
# 3. 风格列表（前20个）
|
||||
print(f"\n📋 已学习的风格 (前20个):")
|
||||
all_styles = learner.get_all_styles()
|
||||
if not all_styles:
|
||||
print(" ⚠️ 没有任何风格！模型尚未训练")
|
||||
else:
|
||||
for i, style in enumerate(all_styles[:20], 1):
|
||||
style_id = learner.style_to_id.get(style)
|
||||
situation = learner.id_to_situation.get(style_id, "N/A")
|
||||
print(f" [{i}] {style}")
|
||||
print(f" (ID: {style_id}, Situation: {situation})")
|
||||
|
||||
# 4. 测试预测
|
||||
print(f"\n🔮 测试预测功能:")
|
||||
if not all_styles:
|
||||
print(" ⚠️ 无法测试，模型没有训练数据")
|
||||
else:
|
||||
test_situations = [
|
||||
"表示惊讶",
|
||||
"讨论游戏",
|
||||
"表达赞同"
|
||||
]
|
||||
|
||||
for test_sit in test_situations:
|
||||
print(f"\n 测试输入: '{test_sit}'")
|
||||
best_style, scores = learner.predict_style(test_sit, top_k=3)
|
||||
|
||||
if best_style:
|
||||
print(f" ✓ 最佳匹配: {best_style}")
|
||||
print(f" Top 3:")
|
||||
for style, score in list(scores.items())[:3]:
|
||||
print(f" - {style}: {score:.4f}")
|
||||
else:
|
||||
print(f" ✗ 预测失败")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("诊断完成")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 从诊断报告中看到的 chat_id
|
||||
test_chat_ids = [
|
||||
"52fb94af9f500a01e023ea780e43606e", # 有78个表达方式
|
||||
"46c8714c8a9b7ee169941fe99fcde07d", # 有22个表达方式
|
||||
]
|
||||
|
||||
for chat_id in test_chat_ids:
|
||||
check_style_learner_status(chat_id)
|
||||
print("\n")
|
||||
254
src/chat/express/express_utils.py
Normal file
254
src/chat/express/express_utils.py
Normal file
@@ -0,0 +1,254 @@
|
||||
"""
|
||||
表达系统工具函数
|
||||
提供消息过滤、文本相似度计算、加权随机抽样等功能
|
||||
"""
|
||||
import difflib
|
||||
import random
|
||||
import re
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from src.common.logger import get_logger
|
||||
|
||||
logger = get_logger("express_utils")
|
||||
|
||||
|
||||
def filter_message_content(content: Optional[str]) -> str:
|
||||
"""
|
||||
过滤消息内容，移除回复、@、图片等格式
|
||||
|
||||
Args:
|
||||
content: 原始消息内容
|
||||
|
||||
Returns:
|
||||
过滤后的纯文本内容
|
||||
"""
|
||||
if not content:
|
||||
return ""
|
||||
|
||||
# 移除以[回复开头、]结尾的部分，包括后面的"，说："部分
|
||||
content = re.sub(r"\[回复.*?\]，说：\s*", "", content)
|
||||
# 移除@<...>格式的内容
|
||||
content = re.sub(r"@<[^>]*>", "", content)
|
||||
# 移除[图片:...]格式的图片ID
|
||||
content = re.sub(r"\[图片:[^\]]*\]", "", content)
|
||||
# 移除[表情包：...]格式的内容
|
||||
content = re.sub(r"\[表情包：[^\]]*\]", "", content)
|
||||
|
||||
return content.strip()
|
||||
|
||||
|
||||
def calculate_similarity(text1: str, text2: str) -> float:
|
||||
"""
|
||||
计算两个文本的相似度，返回0-1之间的值
|
||||
|
||||
Args:
|
||||
text1: 第一个文本
|
||||
text2: 第二个文本
|
||||
|
||||
Returns:
|
||||
相似度值 (0-1)
|
||||
"""
|
||||
return difflib.SequenceMatcher(None, text1, text2).ratio()
|
||||
|
||||
|
||||
def weighted_sample(population: List[Dict], k: int, weight_key: Optional[str] = None) -> List[Dict]:
|
||||
"""
|
||||
加权随机抽样函数
|
||||
|
||||
Args:
|
||||
population: 待抽样的数据列表
|
||||
k: 抽样数量
|
||||
weight_key: 权重字段名，如果为None则等概率抽样
|
||||
|
||||
Returns:
|
||||
抽样结果列表
|
||||
"""
|
||||
if not population or k <= 0:
|
||||
return []
|
||||
|
||||
if len(population) <= k:
|
||||
return population.copy()
|
||||
|
||||
# 如果指定了权重字段
|
||||
if weight_key and all(weight_key in item for item in population):
|
||||
try:
|
||||
# 获取权重
|
||||
weights = [float(item.get(weight_key, 1.0)) for item in population]
|
||||
# 使用random.choices进行加权抽样
|
||||
return random.choices(population, weights=weights, k=k)
|
||||
except (ValueError, TypeError) as e:
|
||||
logger.warning(f"加权抽样失败，使用等概率抽样: {e}")
|
||||
|
||||
# 等概率抽样
|
||||
selected = []
|
||||
population_copy = population.copy()
|
||||
|
||||
for _ in range(k):
|
||||
if not population_copy:
|
||||
break
|
||||
# 随机选择一个元素
|
||||
idx = random.randint(0, len(population_copy) - 1)
|
||||
selected.append(population_copy.pop(idx))
|
||||
|
||||
return selected
|
||||
|
||||
|
||||
def normalize_text(text: str) -> str:
|
||||
"""
|
||||
标准化文本，移除多余空白字符
|
||||
|
||||
Args:
|
||||
text: 输入文本
|
||||
|
||||
Returns:
|
||||
标准化后的文本
|
||||
"""
|
||||
# 替换多个连续空白字符为单个空格
|
||||
text = re.sub(r"\s+", " ", text)
|
||||
return text.strip()
|
||||
|
||||
|
||||
def extract_keywords(text: str, max_keywords: int = 10) -> List[str]:
|
||||
"""
|
||||
简单的关键词提取（基于词频）
|
||||
|
||||
Args:
|
||||
text: 输入文本
|
||||
max_keywords: 最大关键词数量
|
||||
|
||||
Returns:
|
||||
关键词列表
|
||||
"""
|
||||
if not text:
|
||||
return []
|
||||
|
||||
try:
|
||||
import jieba.analyse
|
||||
|
||||
# 使用TF-IDF提取关键词
|
||||
keywords = jieba.analyse.extract_tags(text, topK=max_keywords)
|
||||
return keywords
|
||||
except ImportError:
|
||||
logger.warning("jieba未安装，无法提取关键词")
|
||||
# 简单分词
|
||||
words = text.split()
|
||||
return words[:max_keywords]
|
||||
|
||||
|
||||
def format_expression_pair(situation: str, style: str, index: Optional[int] = None) -> str:
|
||||
"""
|
||||
格式化表达方式对
|
||||
|
||||
Args:
|
||||
situation: 情境
|
||||
style: 风格
|
||||
index: 序号（可选）
|
||||
|
||||
Returns:
|
||||
格式化后的字符串
|
||||
"""
|
||||
if index is not None:
|
||||
return f'{index}. 当"{situation}"时，使用"{style}"'
|
||||
else:
|
||||
return f'当"{situation}"时，使用"{style}"'
|
||||
|
||||
|
||||
def parse_expression_pair(text: str) -> Optional[tuple[str, str]]:
|
||||
"""
|
||||
解析表达方式对文本
|
||||
|
||||
Args:
|
||||
text: 格式化的表达方式对文本
|
||||
|
||||
Returns:
|
||||
(situation, style) 或 None
|
||||
"""
|
||||
# 匹配格式：当"..."时，使用"..."
|
||||
match = re.search(r'当"(.+?)"时，使用"(.+?)"', text)
|
||||
if match:
|
||||
return match.group(1), match.group(2)
|
||||
return None
|
||||
|
||||
|
||||
def batch_filter_duplicates(expressions: List[Dict[str, Any]], key_fields: List[str]) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
批量去重表达方式
|
||||
|
||||
Args:
|
||||
expressions: 表达方式列表
|
||||
key_fields: 用于去重的字段名列表
|
||||
|
||||
Returns:
|
||||
去重后的表达方式列表
|
||||
"""
|
||||
seen = set()
|
||||
unique_expressions = []
|
||||
|
||||
for expr in expressions:
|
||||
# 构建去重key
|
||||
key_values = tuple(expr.get(field, "") for field in key_fields)
|
||||
|
||||
if key_values not in seen:
|
||||
seen.add(key_values)
|
||||
unique_expressions.append(expr)
|
||||
|
||||
return unique_expressions
|
||||
|
||||
|
||||
def calculate_time_weight(last_active_time: float, current_time: float, half_life_days: int = 30) -> float:
|
||||
"""
|
||||
根据时间计算权重（时间衰减）
|
||||
|
||||
Args:
|
||||
last_active_time: 最后活跃时间戳
|
||||
current_time: 当前时间戳
|
||||
half_life_days: 半衰期天数
|
||||
|
||||
Returns:
|
||||
权重值 (0-1)
|
||||
"""
|
||||
time_diff_days = (current_time - last_active_time) / 86400 # 转换为天数
|
||||
if time_diff_days < 0:
|
||||
return 1.0
|
||||
|
||||
# 使用指数衰减公式
|
||||
decay_rate = 0.693 / half_life_days # ln(2) / half_life
|
||||
weight = max(0.01, min(1.0, 2 ** (-decay_rate * time_diff_days)))
|
||||
|
||||
return weight
|
||||
|
||||
|
||||
def merge_expressions_from_multiple_chats(
|
||||
expressions_dict: Dict[str, List[Dict[str, Any]]], max_total: int = 100
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
合并多个聊天室的表达方式
|
||||
|
||||
Args:
|
||||
expressions_dict: {chat_id: [expressions]}
|
||||
max_total: 最大合并数量
|
||||
|
||||
Returns:
|
||||
合并后的表达方式列表
|
||||
"""
|
||||
all_expressions = []
|
||||
|
||||
# 收集所有表达方式
|
||||
for chat_id, expressions in expressions_dict.items():
|
||||
for expr in expressions:
|
||||
# 添加source_id标识
|
||||
expr_with_source = expr.copy()
|
||||
expr_with_source["source_id"] = chat_id
|
||||
all_expressions.append(expr_with_source)
|
||||
|
||||
# 按count或last_active_time排序
|
||||
if all_expressions and "count" in all_expressions[0]:
|
||||
all_expressions.sort(key=lambda x: x.get("count", 0), reverse=True)
|
||||
elif all_expressions and "last_active_time" in all_expressions[0]:
|
||||
all_expressions.sort(key=lambda x: x.get("last_active_time", 0), reverse=True)
|
||||
|
||||
# 去重（基于situation和style）
|
||||
all_expressions = batch_filter_duplicates(all_expressions, ["situation", "style"])
|
||||
|
||||
# 限制数量
|
||||
return all_expressions[:max_total]
|
||||
@@ -16,6 +16,9 @@ from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
|
||||
# 导入 StyleLearner 管理器
|
||||
from .style_learner import style_learner_manager
|
||||
|
||||
MAX_EXPRESSION_COUNT = 300
|
||||
DECAY_DAYS = 30 # 30天衰减到0.01
|
||||
DECAY_MIN = 0.01 # 最小衰减值
|
||||
@@ -43,17 +46,29 @@ def init_prompt() -> None:
|
||||
3. 语言风格包含特殊内容和情感
|
||||
4. 思考有没有特殊的梗，一并总结成语言风格
|
||||
5. 例子仅供参考，请严格根据群聊内容总结!!!
|
||||
注意：总结成如下格式的规律，总结的内容要详细，但具有概括性：
|
||||
例如：当"AAAAA"时，可以"BBBBB", AAAAA代表某个具体的场景，不超过20个字。BBBBB代表对应的语言风格，特定句式或表达方式，不超过20个字。
|
||||
|
||||
**重要：必须严格按照以下格式输出，每行一条规律：**
|
||||
当"xxx"时，使用"xxx"
|
||||
|
||||
格式说明：
|
||||
- 必须以"当"开头
|
||||
- 场景描述用双引号包裹，不超过20个字
|
||||
- 必须包含"使用"或"可以"
|
||||
- 表达风格用双引号包裹，不超过20个字
|
||||
- 每条规律独占一行
|
||||
|
||||
例如：
|
||||
当"对某件事表示十分惊叹，有些意外"时，使用"我嘞个xxxx"
|
||||
当"表示讽刺的赞同，不想讲道理"时，使用"对对对"
|
||||
当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"，使用"懂的都懂"
|
||||
当"当涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
|
||||
当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"时，使用"懂的都懂"
|
||||
当"涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
|
||||
|
||||
请注意：不要总结你自己（SELF）的发言
|
||||
现在请你概括
|
||||
注意：
|
||||
1. 不要总结你自己（SELF）的发言
|
||||
2. 如果聊天内容中没有明显的特殊风格，请只输出1-2条最明显的特点
|
||||
3. 不要输出其他解释性文字，只输出符合格式的规律
|
||||
|
||||
现在请你概括：
|
||||
"""
|
||||
Prompt(learn_style_prompt, "learn_style_prompt")
|
||||
|
||||
@@ -65,16 +80,28 @@ def init_prompt() -> None:
|
||||
2.不要涉及具体的人名，只考虑语法和句法特点,
|
||||
3.语法和句法特点要包括，句子长短（具体字数），有何种语病，如何拆分句子。
|
||||
4. 例子仅供参考，请严格根据群聊内容总结!!!
|
||||
总结成如下格式的规律，总结的内容要简洁，不浮夸：
|
||||
当"xxx"时，可以"xxx"
|
||||
|
||||
**重要：必须严格按照以下格式输出，每行一条规律：**
|
||||
当"xxx"时，使用"xxx"
|
||||
|
||||
格式说明：
|
||||
- 必须以"当"开头
|
||||
- 场景描述用双引号包裹
|
||||
- 必须包含"使用"或"可以"
|
||||
- 句法特点用双引号包裹
|
||||
- 每条规律独占一行
|
||||
|
||||
例如：
|
||||
当"表达观点较复杂"时，使用"省略主语(3-6个字)"的句法
|
||||
当"不用详细说明的一般表达"时，使用"非常简洁的句子"的句法
|
||||
当"需要单纯简单的确认"时，使用"单字或几个字的肯定(1-2个字)"的句法
|
||||
|
||||
注意不要总结你自己（SELF）的发言
|
||||
现在请你概括
|
||||
注意：
|
||||
1. 不要总结你自己（SELF）的发言
|
||||
2. 如果聊天内容中没有明显的句法特点，请只输出1-2条最明显的特点
|
||||
3. 不要输出其他解释性文字，只输出符合格式的规律
|
||||
|
||||
现在请你概括：
|
||||
"""
|
||||
Prompt(learn_grammar_prompt, "learn_grammar_prompt")
|
||||
|
||||
@@ -405,6 +432,44 @@ class ExpressionLearner:
|
||||
for expr in exprs[: len(exprs) - MAX_EXPRESSION_COUNT]:
|
||||
await session.delete(expr)
|
||||
|
||||
# 🔥 训练 StyleLearner
|
||||
# 只对 style 类型的表达方式进行训练（grammar 不需要训练到模型）
|
||||
if type == "style":
|
||||
try:
|
||||
# 获取 StyleLearner 实例
|
||||
learner = style_learner_manager.get_learner(chat_id)
|
||||
|
||||
logger.info(f"开始训练 StyleLearner: chat_id={chat_id}, 样本数={len(expr_list)}")
|
||||
|
||||
# 为每个学习到的表达方式训练模型
|
||||
# 使用 situation 作为输入，style 作为目标
|
||||
# 这是最符合语义的方式：场景 -> 表达方式
|
||||
success_count = 0
|
||||
for expr in expr_list:
|
||||
situation = expr["situation"]
|
||||
style = expr["style"]
|
||||
|
||||
# 训练映射关系: situation -> style
|
||||
if learner.learn_mapping(situation, style):
|
||||
success_count += 1
|
||||
else:
|
||||
logger.warning(f"训练失败: {situation} -> {style}")
|
||||
|
||||
logger.info(
|
||||
f"StyleLearner 训练完成: {success_count}/{len(expr_list)} 成功, "
|
||||
f"当前风格总数={len(learner.get_all_styles())}, "
|
||||
f"总样本数={learner.learning_stats['total_samples']}"
|
||||
)
|
||||
|
||||
# 保存模型
|
||||
if learner.save(style_learner_manager.model_save_path):
|
||||
logger.info(f"StyleLearner 模型保存成功: {chat_id}")
|
||||
else:
|
||||
logger.error(f"StyleLearner 模型保存失败: {chat_id}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"训练 StyleLearner 失败: {e}", exc_info=True)
|
||||
|
||||
return learnt_expressions
|
||||
return None
|
||||
|
||||
@@ -455,9 +520,17 @@ class ExpressionLearner:
|
||||
logger.error(f"学习{type_str}失败: {e}")
|
||||
return None
|
||||
|
||||
if not response or not response.strip():
|
||||
logger.warning(f"LLM返回空响应，无法学习{type_str}")
|
||||
return None
|
||||
|
||||
logger.debug(f"学习{type_str}的response: {response}")
|
||||
|
||||
expressions: list[tuple[str, str, str]] = self.parse_expression_response(response, chat_id)
|
||||
|
||||
if not expressions:
|
||||
logger.warning(f"从LLM响应中未能解析出任何{type_str}。请检查LLM输出格式是否正确。")
|
||||
logger.info(f"LLM完整响应:\n{response}")
|
||||
|
||||
return expressions, chat_id
|
||||
|
||||
@@ -465,31 +538,100 @@ class ExpressionLearner:
|
||||
def parse_expression_response(response: str, chat_id: str) -> list[tuple[str, str, str]]:
|
||||
"""
|
||||
解析LLM返回的表达风格总结，每一行提取"当"和"使用"之间的内容，存储为(situation, style)元组
|
||||
支持多种引号格式："" 和 ""
|
||||
"""
|
||||
expressions: list[tuple[str, str, str]] = []
|
||||
for line in response.splitlines():
|
||||
failed_lines = []
|
||||
|
||||
for line_num, line in enumerate(response.splitlines(), 1):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
# 替换中文引号为英文引号，便于统一处理
|
||||
line_normalized = line.replace('"', '"').replace('"', '"').replace("'", '"').replace("'", '"')
|
||||
|
||||
# 查找"当"和下一个引号
|
||||
idx_when = line.find('当"')
|
||||
idx_when = line_normalized.find('当"')
|
||||
if idx_when == -1:
|
||||
continue
|
||||
idx_quote1 = idx_when + 1
|
||||
idx_quote2 = line.find('"', idx_quote1 + 1)
|
||||
if idx_quote2 == -1:
|
||||
continue
|
||||
situation = line[idx_quote1 + 1 : idx_quote2]
|
||||
# 查找"使用"
|
||||
idx_use = line.find('使用"', idx_quote2)
|
||||
# 尝试不带引号的格式: 当xxx时
|
||||
idx_when = line_normalized.find('当')
|
||||
if idx_when == -1:
|
||||
failed_lines.append((line_num, line, "找不到'当'关键字"))
|
||||
continue
|
||||
|
||||
# 提取"当"和"时"之间的内容
|
||||
idx_shi = line_normalized.find('时', idx_when)
|
||||
if idx_shi == -1:
|
||||
failed_lines.append((line_num, line, "找不到'时'关键字"))
|
||||
continue
|
||||
situation = line_normalized[idx_when + 1:idx_shi].strip('"\'""')
|
||||
search_start = idx_shi
|
||||
else:
|
||||
idx_quote1 = idx_when + 1
|
||||
idx_quote2 = line_normalized.find('"', idx_quote1 + 1)
|
||||
if idx_quote2 == -1:
|
||||
failed_lines.append((line_num, line, "situation部分引号不匹配"))
|
||||
continue
|
||||
situation = line_normalized[idx_quote1 + 1 : idx_quote2]
|
||||
search_start = idx_quote2
|
||||
|
||||
# 查找"使用"或"可以"
|
||||
idx_use = line_normalized.find('使用"', search_start)
|
||||
if idx_use == -1:
|
||||
idx_use = line_normalized.find('可以"', search_start)
|
||||
if idx_use == -1:
|
||||
# 尝试不带引号的格式
|
||||
idx_use = line_normalized.find('使用', search_start)
|
||||
if idx_use == -1:
|
||||
idx_use = line_normalized.find('可以', search_start)
|
||||
if idx_use == -1:
|
||||
failed_lines.append((line_num, line, "找不到'使用'或'可以'关键字"))
|
||||
continue
|
||||
|
||||
# 提取剩余部分作为style
|
||||
style = line_normalized[idx_use + 2:].strip('"\'""，。')
|
||||
if not style:
|
||||
failed_lines.append((line_num, line, "style部分为空"))
|
||||
continue
|
||||
else:
|
||||
idx_quote3 = idx_use + 2
|
||||
idx_quote4 = line_normalized.find('"', idx_quote3 + 1)
|
||||
if idx_quote4 == -1:
|
||||
# 如果没有结束引号，取到行尾
|
||||
style = line_normalized[idx_quote3 + 1:].strip('"\'""')
|
||||
else:
|
||||
style = line_normalized[idx_quote3 + 1 : idx_quote4]
|
||||
else:
|
||||
idx_quote3 = idx_use + 2
|
||||
idx_quote4 = line_normalized.find('"', idx_quote3 + 1)
|
||||
if idx_quote4 == -1:
|
||||
# 如果没有结束引号，取到行尾
|
||||
style = line_normalized[idx_quote3 + 1:].strip('"\'""')
|
||||
else:
|
||||
style = line_normalized[idx_quote3 + 1 : idx_quote4]
|
||||
|
||||
# 清理并验证
|
||||
situation = situation.strip()
|
||||
style = style.strip()
|
||||
|
||||
if not situation or not style:
|
||||
failed_lines.append((line_num, line, f"situation或style为空: situation='{situation}', style='{style}'"))
|
||||
continue
|
||||
idx_quote3 = idx_use + 2
|
||||
idx_quote4 = line.find('"', idx_quote3 + 1)
|
||||
if idx_quote4 == -1:
|
||||
continue
|
||||
style = line[idx_quote3 + 1 : idx_quote4]
|
||||
|
||||
expressions.append((chat_id, situation, style))
|
||||
|
||||
# 记录解析失败的行
|
||||
if failed_lines:
|
||||
logger.warning(f"解析表达方式时有 {len(failed_lines)} 行失败:")
|
||||
for line_num, line, reason in failed_lines[:5]: # 只显示前5个
|
||||
logger.warning(f" 行{line_num}: {reason}")
|
||||
logger.debug(f" 原文: {line}")
|
||||
|
||||
if not expressions:
|
||||
logger.warning(f"LLM返回了内容但无法解析任何表达方式。响应预览:\n{response[:500]}")
|
||||
else:
|
||||
logger.debug(f"成功解析 {len(expressions)} 个表达方式")
|
||||
return expressions
|
||||
|
||||
|
||||
@@ -522,12 +664,12 @@ class ExpressionLearnerManager:
|
||||
os.path.join(base_dir, "learnt_grammar"),
|
||||
]
|
||||
|
||||
try:
|
||||
for directory in directories_to_create:
|
||||
for directory in directories_to_create:
|
||||
try:
|
||||
os.makedirs(directory, exist_ok=True)
|
||||
logger.debug(f"确保目录存在: {directory}")
|
||||
except Exception as e:
|
||||
logger.error(f"创建目录失败 {directory}: {e}")
|
||||
logger.debug(f"确保目录存在: {directory}")
|
||||
except Exception as e:
|
||||
logger.error(f"创建目录失败 {directory}: {e}")
|
||||
|
||||
@staticmethod
|
||||
async def _auto_migrate_json_to_db():
|
||||
|
||||
@@ -15,6 +15,10 @@ from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
|
||||
# 导入StyleLearner管理器和情境提取器
|
||||
from .situation_extractor import situation_extractor
|
||||
from .style_learner import style_learner_manager
|
||||
|
||||
logger = get_logger("expression_selector")
|
||||
|
||||
|
||||
@@ -127,17 +131,18 @@ class ExpressionSelector:
|
||||
current_group = rule.group
|
||||
break
|
||||
|
||||
if not current_group:
|
||||
return [chat_id]
|
||||
# 🔥 始终包含当前 chat_id（确保至少能查到自己的数据）
|
||||
related_chat_ids = [chat_id]
|
||||
|
||||
# 找出同一组的所有chat_id
|
||||
related_chat_ids = []
|
||||
for rule in rules:
|
||||
if rule.group == current_group and rule.chat_stream_id:
|
||||
if chat_id_candidate := self._parse_stream_config_to_chat_id(rule.chat_stream_id):
|
||||
related_chat_ids.append(chat_id_candidate)
|
||||
if current_group:
|
||||
# 找出同一组的所有chat_id
|
||||
for rule in rules:
|
||||
if rule.group == current_group and rule.chat_stream_id:
|
||||
if chat_id_candidate := self._parse_stream_config_to_chat_id(rule.chat_stream_id):
|
||||
if chat_id_candidate not in related_chat_ids:
|
||||
related_chat_ids.append(chat_id_candidate)
|
||||
|
||||
return related_chat_ids if related_chat_ids else [chat_id]
|
||||
return related_chat_ids
|
||||
|
||||
async def get_random_expressions(
|
||||
self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float
|
||||
@@ -236,6 +241,287 @@ class ExpressionSelector:
|
||||
)
|
||||
await session.commit()
|
||||
|
||||
async def select_suitable_expressions(
|
||||
self,
|
||||
chat_id: str,
|
||||
chat_history: list | str,
|
||||
target_message: str | None = None,
|
||||
max_num: int = 10,
|
||||
min_num: int = 5,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""
|
||||
统一的表达方式选择入口，根据配置自动选择模式
|
||||
|
||||
Args:
|
||||
chat_id: 聊天ID
|
||||
chat_history: 聊天历史（列表或字符串）
|
||||
target_message: 目标消息
|
||||
max_num: 最多返回数量
|
||||
min_num: 最少返回数量
|
||||
|
||||
Returns:
|
||||
选中的表达方式列表
|
||||
"""
|
||||
# 转换chat_history为字符串
|
||||
if isinstance(chat_history, list):
|
||||
chat_info = "\n".join([f"{msg.get('sender', 'Unknown')}: {msg.get('content', '')}" for msg in chat_history])
|
||||
else:
|
||||
chat_info = chat_history
|
||||
|
||||
# 根据配置选择模式
|
||||
mode = global_config.expression.mode
|
||||
logger.debug(f"[ExpressionSelector] 使用模式: {mode}")
|
||||
|
||||
if mode == "exp_model":
|
||||
return await self._select_expressions_model_only(
|
||||
chat_id=chat_id,
|
||||
chat_info=chat_info,
|
||||
target_message=target_message,
|
||||
max_num=max_num,
|
||||
min_num=min_num
|
||||
)
|
||||
else: # classic mode
|
||||
return await self._select_expressions_classic(
|
||||
chat_id=chat_id,
|
||||
chat_info=chat_info,
|
||||
target_message=target_message,
|
||||
max_num=max_num,
|
||||
min_num=min_num
|
||||
)
|
||||
|
||||
async def _select_expressions_classic(
|
||||
self,
|
||||
chat_id: str,
|
||||
chat_info: str,
|
||||
target_message: str | None = None,
|
||||
max_num: int = 10,
|
||||
min_num: int = 5,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""经典模式：随机抽样 + LLM评估"""
|
||||
logger.debug(f"[Classic模式] 使用LLM评估表达方式")
|
||||
return await self.select_suitable_expressions_llm(
|
||||
chat_id=chat_id,
|
||||
chat_info=chat_info,
|
||||
max_num=max_num,
|
||||
min_num=min_num,
|
||||
target_message=target_message
|
||||
)
|
||||
|
||||
async def _select_expressions_model_only(
|
||||
self,
|
||||
chat_id: str,
|
||||
chat_info: str,
|
||||
target_message: str | None = None,
|
||||
max_num: int = 10,
|
||||
min_num: int = 5,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""模型预测模式：先提取情境，再使用StyleLearner预测表达风格"""
|
||||
logger.debug(f"[Exp_model模式] 使用情境提取 + StyleLearner预测表达方式")
|
||||
|
||||
# 检查是否允许在此聊天流中使用表达
|
||||
if not self.can_use_expression_for_chat(chat_id):
|
||||
logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
|
||||
return []
|
||||
|
||||
# 步骤1: 提取聊天情境
|
||||
situations = await situation_extractor.extract_situations(
|
||||
chat_history=chat_info,
|
||||
target_message=target_message,
|
||||
max_situations=3
|
||||
)
|
||||
|
||||
if not situations:
|
||||
logger.warning(f"无法提取聊天情境，回退到经典模式")
|
||||
return await self._select_expressions_classic(
|
||||
chat_id=chat_id,
|
||||
chat_info=chat_info,
|
||||
target_message=target_message,
|
||||
max_num=max_num,
|
||||
min_num=min_num
|
||||
)
|
||||
|
||||
logger.info(f"[Exp_model模式] 步骤1完成 - 提取到 {len(situations)} 个情境: {situations}")
|
||||
|
||||
# 步骤2: 使用 StyleLearner 为每个情境预测合适的表达方式
|
||||
learner = style_learner_manager.get_learner(chat_id)
|
||||
|
||||
all_predicted_styles = {}
|
||||
for i, situation in enumerate(situations, 1):
|
||||
logger.debug(f"[Exp_model模式] 步骤2.{i} - 为情境预测风格: {situation}")
|
||||
best_style, scores = learner.predict_style(situation, top_k=max_num)
|
||||
|
||||
if best_style and scores:
|
||||
logger.debug(f" 预测结果: best={best_style}, scores数量={len(scores)}")
|
||||
# 合并分数（取最高分）
|
||||
for style, score in scores.items():
|
||||
if style not in all_predicted_styles or score > all_predicted_styles[style]:
|
||||
all_predicted_styles[style] = score
|
||||
else:
|
||||
logger.debug(f" 该情境未返回预测结果")
|
||||
|
||||
if not all_predicted_styles:
|
||||
logger.warning(f"[Exp_model模式] StyleLearner未返回预测结果（可能模型未训练），回退到经典模式")
|
||||
return await self._select_expressions_classic(
|
||||
chat_id=chat_id,
|
||||
chat_info=chat_info,
|
||||
target_message=target_message,
|
||||
max_num=max_num,
|
||||
min_num=min_num
|
||||
)
|
||||
|
||||
# 将分数字典转换为列表格式 [(style, score), ...]
|
||||
predicted_styles = sorted(all_predicted_styles.items(), key=lambda x: x[1], reverse=True)
|
||||
|
||||
logger.info(f"[Exp_model模式] 步骤2完成 - 预测到 {len(predicted_styles)} 个风格, Top3: {predicted_styles[:3]}")
|
||||
|
||||
# 步骤3: 根据预测的风格从数据库获取表达方式
|
||||
logger.debug(f"[Exp_model模式] 步骤3 - 从数据库查询表达方式")
|
||||
expressions = await self.get_model_predicted_expressions(
|
||||
chat_id=chat_id,
|
||||
predicted_styles=predicted_styles,
|
||||
max_num=max_num
|
||||
)
|
||||
|
||||
if not expressions:
|
||||
logger.warning(f"[Exp_model模式] 未找到匹配预测风格的表达方式，回退到经典模式")
|
||||
return await self._select_expressions_classic(
|
||||
chat_id=chat_id,
|
||||
chat_info=chat_info,
|
||||
target_message=target_message,
|
||||
max_num=max_num,
|
||||
min_num=min_num
|
||||
)
|
||||
|
||||
logger.info(f"[Exp_model模式] 成功! 返回 {len(expressions)} 个表达方式")
|
||||
return expressions
|
||||
|
||||
async def get_model_predicted_expressions(
|
||||
self,
|
||||
chat_id: str,
|
||||
predicted_styles: list[tuple[str, float]],
|
||||
max_num: int = 10
|
||||
) -> list[dict[str, Any]]:
|
||||
"""
|
||||
根据StyleLearner预测的风格获取表达方式
|
||||
|
||||
Args:
|
||||
chat_id: 聊天ID
|
||||
predicted_styles: 预测的风格列表，格式: [(style, score), ...]
|
||||
max_num: 最多返回数量
|
||||
|
||||
Returns:
|
||||
表达方式列表
|
||||
"""
|
||||
if not predicted_styles:
|
||||
return []
|
||||
|
||||
# 提取风格名称（前3个最佳匹配）
|
||||
style_names = [style for style, _ in predicted_styles[:min(3, len(predicted_styles))]]
|
||||
logger.debug(f"预测最佳风格: {style_names[0] if style_names else 'None'}, Top3分数: {predicted_styles[:3]}")
|
||||
|
||||
# 🔥 使用 get_related_chat_ids 获取所有相关的 chat_id（支持共享表达方式）
|
||||
related_chat_ids = self.get_related_chat_ids(chat_id)
|
||||
logger.info(f"查询相关的chat_ids ({len(related_chat_ids)}个): {related_chat_ids}")
|
||||
|
||||
async with get_db_session() as session:
|
||||
# 🔍 先检查数据库中实际有哪些 chat_id 的数据
|
||||
db_chat_ids_result = await session.execute(
|
||||
select(Expression.chat_id)
|
||||
.where(Expression.type == "style")
|
||||
.distinct()
|
||||
)
|
||||
db_chat_ids = [cid for cid in db_chat_ids_result.scalars()]
|
||||
logger.info(f"数据库中有表达方式的chat_ids ({len(db_chat_ids)}个): {db_chat_ids}")
|
||||
|
||||
# 获取所有相关 chat_id 的表达方式（用于模糊匹配）
|
||||
all_expressions_result = await session.execute(
|
||||
select(Expression)
|
||||
.where(Expression.chat_id.in_(related_chat_ids))
|
||||
.where(Expression.type == "style")
|
||||
)
|
||||
all_expressions = list(all_expressions_result.scalars())
|
||||
|
||||
logger.info(f"配置的相关chat_id的表达方式数量: {len(all_expressions)}")
|
||||
|
||||
# 🔥 智能回退：如果相关 chat_id 没有数据，尝试查询所有 chat_id
|
||||
if not all_expressions:
|
||||
logger.info(f"相关chat_id没有数据，尝试从所有chat_id查询")
|
||||
all_expressions_result = await session.execute(
|
||||
select(Expression)
|
||||
.where(Expression.type == "style")
|
||||
)
|
||||
all_expressions = list(all_expressions_result.scalars())
|
||||
logger.debug(f"数据库中所有表达方式数量: {len(all_expressions)}")
|
||||
|
||||
if not all_expressions:
|
||||
logger.warning(f"数据库中完全没有任何表达方式，需要先学习")
|
||||
return []
|
||||
|
||||
# 🔥 使用模糊匹配而不是精确匹配
|
||||
# 计算每个预测style与数据库style的相似度
|
||||
from difflib import SequenceMatcher
|
||||
|
||||
matched_expressions = []
|
||||
for expr in all_expressions:
|
||||
db_style = expr.style or ""
|
||||
max_similarity = 0.0
|
||||
best_predicted = ""
|
||||
|
||||
# 与每个预测的style计算相似度
|
||||
for predicted_style, pred_score in predicted_styles[:20]: # 考虑前20个预测
|
||||
# 计算字符串相似度
|
||||
similarity = SequenceMatcher(None, predicted_style, db_style).ratio()
|
||||
|
||||
# 也检查包含关系（如果一个是另一个的子串，给更高分）
|
||||
if len(predicted_style) >= 2 and len(db_style) >= 2:
|
||||
if predicted_style in db_style or db_style in predicted_style:
|
||||
similarity = max(similarity, 0.7)
|
||||
|
||||
if similarity > max_similarity:
|
||||
max_similarity = similarity
|
||||
best_predicted = predicted_style
|
||||
|
||||
# 🔥 降低阈值到30%，因为StyleLearner预测质量较差
|
||||
if max_similarity >= 0.3: # 30%相似度阈值
|
||||
matched_expressions.append((expr, max_similarity, expr.count, best_predicted))
|
||||
|
||||
if not matched_expressions:
|
||||
# 收集数据库中的style样例用于调试
|
||||
all_styles = [e.style for e in all_expressions[:10]]
|
||||
logger.warning(
|
||||
f"数据库中没有找到匹配的表达方式（相似度阈值30%）:\n"
|
||||
f" 预测的style (前3个): {style_names}\n"
|
||||
f" 数据库中存在的style样例: {all_styles}\n"
|
||||
f" 提示: StyleLearner预测质量差，建议重新训练或使用classic模式"
|
||||
)
|
||||
return []
|
||||
|
||||
# 按照相似度*count排序，选择最佳匹配
|
||||
matched_expressions.sort(key=lambda x: x[1] * (x[2] ** 0.5), reverse=True)
|
||||
expressions_objs = [e[0] for e in matched_expressions[:max_num]]
|
||||
|
||||
# 显示最佳匹配的详细信息
|
||||
top_matches = [f"{e[3]}->{e[0].style}({e[1]:.2f})" for e in matched_expressions[:3]]
|
||||
logger.info(
|
||||
f"模糊匹配成功: 找到 {len(expressions_objs)} 个表达方式\n"
|
||||
f" 相似度范围: {matched_expressions[0][1]:.2f} ~ {matched_expressions[min(len(matched_expressions)-1, max_num-1)][1]:.2f}\n"
|
||||
f" Top3匹配: {top_matches}"
|
||||
)
|
||||
|
||||
# 转换为字典格式
|
||||
expressions = []
|
||||
for expr in expressions_objs:
|
||||
expressions.append({
|
||||
"situation": expr.situation or "",
|
||||
"style": expr.style or "",
|
||||
"type": expr.type or "style",
|
||||
"count": float(expr.count) if expr.count else 0.0,
|
||||
"last_active_time": expr.last_active_time or 0.0
|
||||
})
|
||||
|
||||
logger.debug(f"从数据库获取了 {len(expressions)} 个表达方式")
|
||||
return expressions
|
||||
|
||||
async def select_suitable_expressions_llm(
|
||||
self,
|
||||
chat_id: str,
|
||||
|
||||
9
src/chat/express/expressor_model/__init__.py
Normal file
9
src/chat/express/expressor_model/__init__.py
Normal file
@@ -0,0 +1,9 @@
|
||||
"""
|
||||
表达模型包
|
||||
包含基于Online Naive Bayes的机器学习模型
|
||||
"""
|
||||
from .model import ExpressorModel
|
||||
from .online_nb import OnlineNaiveBayes
|
||||
from .tokenizer import Tokenizer
|
||||
|
||||
__all__ = ["ExpressorModel", "OnlineNaiveBayes", "Tokenizer"]
|
||||
216
src/chat/express/expressor_model/model.py
Normal file
216
src/chat/express/expressor_model/model.py
Normal file
@@ -0,0 +1,216 @@
|
||||
"""
|
||||
基于Online Naive Bayes的表达模型
|
||||
支持候选表达的动态添加和在线学习
|
||||
"""
|
||||
import os
|
||||
import pickle
|
||||
from collections import Counter, defaultdict
|
||||
from typing import Dict, Optional, Tuple
|
||||
|
||||
from src.common.logger import get_logger
|
||||
|
||||
from .online_nb import OnlineNaiveBayes
|
||||
from .tokenizer import Tokenizer
|
||||
|
||||
logger = get_logger("expressor.model")
|
||||
|
||||
|
||||
class ExpressorModel:
|
||||
"""直接使用朴素贝叶斯精排（可在线学习）"""
|
||||
|
||||
def __init__(
|
||||
self, alpha: float = 0.5, beta: float = 0.5, gamma: float = 1.0, vocab_size: int = 200000, use_jieba: bool = True
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
alpha: 词频平滑参数
|
||||
beta: 类别先验平滑参数
|
||||
gamma: 衰减因子
|
||||
vocab_size: 词汇表大小
|
||||
use_jieba: 是否使用jieba分词
|
||||
"""
|
||||
# 初始化分词器
|
||||
self.tokenizer = Tokenizer(stopwords=set(), use_jieba=use_jieba)
|
||||
|
||||
# 初始化在线朴素贝叶斯模型
|
||||
self.nb = OnlineNaiveBayes(alpha=alpha, beta=beta, gamma=gamma, vocab_size=vocab_size)
|
||||
|
||||
# 候选表达管理
|
||||
self._candidates: Dict[str, str] = {} # cid -> text (style)
|
||||
self._situations: Dict[str, str] = {} # cid -> situation (不参与计算)
|
||||
|
||||
logger.info(
|
||||
f"ExpressorModel初始化完成 (alpha={alpha}, beta={beta}, gamma={gamma}, vocab_size={vocab_size}, use_jieba={use_jieba})"
|
||||
)
|
||||
|
||||
def add_candidate(self, cid: str, text: str, situation: Optional[str] = None):
|
||||
"""
|
||||
添加候选文本和对应的situation
|
||||
|
||||
Args:
|
||||
cid: 候选ID
|
||||
text: 表达文本 (style)
|
||||
situation: 情境文本
|
||||
"""
|
||||
self._candidates[cid] = text
|
||||
if situation is not None:
|
||||
self._situations[cid] = situation
|
||||
|
||||
# 确保在nb模型中初始化该候选的计数
|
||||
if cid not in self.nb.cls_counts:
|
||||
self.nb.cls_counts[cid] = 0.0
|
||||
if cid not in self.nb.token_counts:
|
||||
self.nb.token_counts[cid] = defaultdict(float)
|
||||
|
||||
def predict(self, text: str, k: int = None) -> Tuple[Optional[str], Dict[str, float]]:
|
||||
"""
|
||||
直接对所有候选进行朴素贝叶斯评分
|
||||
|
||||
Args:
|
||||
text: 查询文本
|
||||
k: 返回前k个候选，如果为None则返回所有
|
||||
|
||||
Returns:
|
||||
(最佳候选ID, 所有候选的分数字典)
|
||||
"""
|
||||
# 1. 分词
|
||||
toks = self.tokenizer.tokenize(text)
|
||||
if not toks or not self._candidates:
|
||||
return None, {}
|
||||
|
||||
# 2. 计算词频
|
||||
tf = Counter(toks)
|
||||
all_cids = list(self._candidates.keys())
|
||||
|
||||
# 3. 批量评分
|
||||
scores = self.nb.score_batch(tf, all_cids)
|
||||
|
||||
if not scores:
|
||||
return None, {}
|
||||
|
||||
# 4. 根据k参数限制返回的候选数量
|
||||
if k is not None and k > 0:
|
||||
sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
|
||||
limited_scores = dict(sorted_scores[:k])
|
||||
best = sorted_scores[0][0] if sorted_scores else None
|
||||
return best, limited_scores
|
||||
else:
|
||||
best = max(scores.items(), key=lambda x: x[1])[0]
|
||||
return best, scores
|
||||
|
||||
def update_positive(self, text: str, cid: str):
|
||||
"""
|
||||
更新正反馈学习
|
||||
|
||||
Args:
|
||||
text: 输入文本
|
||||
cid: 目标类别ID
|
||||
"""
|
||||
toks = self.tokenizer.tokenize(text)
|
||||
if not toks:
|
||||
return
|
||||
|
||||
tf = Counter(toks)
|
||||
self.nb.update_positive(tf, cid)
|
||||
|
||||
def decay(self, factor: Optional[float] = None):
|
||||
"""
|
||||
应用知识衰减
|
||||
|
||||
Args:
|
||||
factor: 衰减因子，如果为None则使用模型配置的gamma
|
||||
"""
|
||||
self.nb.decay(factor)
|
||||
|
||||
def get_candidate_info(self, cid: str) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""
|
||||
获取候选信息
|
||||
|
||||
Args:
|
||||
cid: 候选ID
|
||||
|
||||
Returns:
|
||||
(style文本, situation文本)
|
||||
"""
|
||||
style = self._candidates.get(cid)
|
||||
situation = self._situations.get(cid)
|
||||
return style, situation
|
||||
|
||||
def get_all_candidates(self) -> Dict[str, Tuple[str, str]]:
|
||||
"""
|
||||
获取所有候选
|
||||
|
||||
Returns:
|
||||
{cid: (style, situation)}
|
||||
"""
|
||||
result = {}
|
||||
for cid in self._candidates.keys():
|
||||
style, situation = self.get_candidate_info(cid)
|
||||
result[cid] = (style, situation)
|
||||
return result
|
||||
|
||||
def save(self, path: str):
|
||||
"""
|
||||
保存模型到文件
|
||||
|
||||
Args:
|
||||
path: 保存路径
|
||||
"""
|
||||
os.makedirs(os.path.dirname(path), exist_ok=True)
|
||||
|
||||
data = {
|
||||
"candidates": self._candidates,
|
||||
"situations": self._situations,
|
||||
"nb_cls_counts": dict(self.nb.cls_counts),
|
||||
"nb_token_counts": {k: dict(v) for k, v in self.nb.token_counts.items()},
|
||||
"nb_alpha": self.nb.alpha,
|
||||
"nb_beta": self.nb.beta,
|
||||
"nb_gamma": self.nb.gamma,
|
||||
"nb_V": self.nb.V,
|
||||
}
|
||||
|
||||
with open(path, "wb") as f:
|
||||
pickle.dump(data, f)
|
||||
|
||||
logger.info(f"模型已保存到 {path}")
|
||||
|
||||
def load(self, path: str):
|
||||
"""
|
||||
从文件加载模型
|
||||
|
||||
Args:
|
||||
path: 加载路径
|
||||
"""
|
||||
if not os.path.exists(path):
|
||||
logger.warning(f"模型文件不存在: {path}")
|
||||
return
|
||||
|
||||
with open(path, "rb") as f:
|
||||
data = pickle.load(f)
|
||||
|
||||
self._candidates = data["candidates"]
|
||||
self._situations = data["situations"]
|
||||
|
||||
# 恢复nb模型的参数
|
||||
self.nb.alpha = data["nb_alpha"]
|
||||
self.nb.beta = data["nb_beta"]
|
||||
self.nb.gamma = data["nb_gamma"]
|
||||
self.nb.V = data["nb_V"]
|
||||
|
||||
# 恢复统计数据
|
||||
self.nb.cls_counts = defaultdict(float, data["nb_cls_counts"])
|
||||
self.nb.token_counts = defaultdict(lambda: defaultdict(float))
|
||||
for cid, tc in data["nb_token_counts"].items():
|
||||
self.nb.token_counts[cid] = defaultdict(float, tc)
|
||||
|
||||
logger.info(f"模型已从 {path} 加载")
|
||||
|
||||
def get_stats(self) -> Dict:
|
||||
"""获取模型统计信息"""
|
||||
nb_stats = self.nb.get_stats()
|
||||
return {
|
||||
"n_candidates": len(self._candidates),
|
||||
"n_classes": nb_stats["n_classes"],
|
||||
"n_tokens": nb_stats["n_tokens"],
|
||||
"total_counts": nb_stats["total_counts"],
|
||||
}
|
||||
142
src/chat/express/expressor_model/online_nb.py
Normal file
142
src/chat/express/expressor_model/online_nb.py
Normal file
@@ -0,0 +1,142 @@
|
||||
"""
|
||||
在线朴素贝叶斯分类器
|
||||
支持增量学习和知识衰减
|
||||
"""
|
||||
import math
|
||||
from collections import Counter, defaultdict
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from src.common.logger import get_logger
|
||||
|
||||
logger = get_logger("expressor.online_nb")
|
||||
|
||||
|
||||
class OnlineNaiveBayes:
|
||||
"""在线朴素贝叶斯分类器"""
|
||||
|
||||
def __init__(self, alpha: float = 0.5, beta: float = 0.5, gamma: float = 1.0, vocab_size: int = 200000):
|
||||
"""
|
||||
Args:
|
||||
alpha: 词频平滑参数
|
||||
beta: 类别先验平滑参数
|
||||
gamma: 衰减因子 (0-1之间，1表示不衰减)
|
||||
vocab_size: 词汇表大小
|
||||
"""
|
||||
self.alpha = alpha
|
||||
self.beta = beta
|
||||
self.gamma = gamma
|
||||
self.V = vocab_size
|
||||
|
||||
# 类别统计
|
||||
self.cls_counts: Dict[str, float] = defaultdict(float) # cid -> total token count
|
||||
self.token_counts: Dict[str, Dict[str, float]] = defaultdict(
|
||||
lambda: defaultdict(float)
|
||||
) # cid -> term -> count
|
||||
|
||||
# 缓存
|
||||
self._logZ: Dict[str, float] = {} # cache log(∑counts + Vα)
|
||||
|
||||
def score_batch(self, tf: Counter, cids: List[str]) -> Dict[str, float]:
|
||||
"""
|
||||
批量计算候选的贝叶斯分数
|
||||
|
||||
Args:
|
||||
tf: 查询文本的词频Counter
|
||||
cids: 候选类别ID列表
|
||||
|
||||
Returns:
|
||||
每个候选的分数字典
|
||||
"""
|
||||
total_cls = sum(self.cls_counts.values())
|
||||
n_cls = max(1, len(self.cls_counts))
|
||||
denom_prior = math.log(total_cls + self.beta * n_cls)
|
||||
|
||||
out: Dict[str, float] = {}
|
||||
for cid in cids:
|
||||
# 计算先验概率 log P(c)
|
||||
prior = math.log(self.cls_counts[cid] + self.beta) - denom_prior
|
||||
s = prior
|
||||
|
||||
# 计算似然概率 log P(w|c)
|
||||
logZ = self._logZ_c(cid)
|
||||
tc = self.token_counts[cid]
|
||||
|
||||
for term, qtf in tf.items():
|
||||
num = tc.get(term, 0.0) + self.alpha
|
||||
s += qtf * (math.log(num) - logZ)
|
||||
|
||||
out[cid] = s
|
||||
return out
|
||||
|
||||
def update_positive(self, tf: Counter, cid: str):
|
||||
"""
|
||||
正反馈更新
|
||||
|
||||
Args:
|
||||
tf: 词频Counter
|
||||
cid: 类别ID
|
||||
"""
|
||||
inc = 0.0
|
||||
tc = self.token_counts[cid]
|
||||
|
||||
# 更新词频统计
|
||||
for term, c in tf.items():
|
||||
tc[term] += float(c)
|
||||
inc += float(c)
|
||||
|
||||
# 更新类别统计
|
||||
self.cls_counts[cid] += inc
|
||||
self._invalidate(cid)
|
||||
|
||||
def decay(self, factor: Optional[float] = None):
|
||||
"""
|
||||
知识衰减（遗忘机制）
|
||||
|
||||
Args:
|
||||
factor: 衰减因子，如果为None则使用self.gamma
|
||||
"""
|
||||
g = self.gamma if factor is None else factor
|
||||
if g >= 1.0:
|
||||
return
|
||||
|
||||
# 对所有统计进行衰减
|
||||
for cid in list(self.cls_counts.keys()):
|
||||
self.cls_counts[cid] *= g
|
||||
for term in list(self.token_counts[cid].keys()):
|
||||
self.token_counts[cid][term] *= g
|
||||
self._invalidate(cid)
|
||||
|
||||
logger.debug(f"应用知识衰减，衰减因子: {g}")
|
||||
|
||||
def _logZ_c(self, cid: str) -> float:
|
||||
"""
|
||||
计算归一化因子logZ
|
||||
|
||||
Args:
|
||||
cid: 类别ID
|
||||
|
||||
Returns:
|
||||
log(Z_c)
|
||||
"""
|
||||
if cid not in self._logZ:
|
||||
Z = self.cls_counts[cid] + self.V * self.alpha
|
||||
self._logZ[cid] = math.log(max(Z, 1e-12))
|
||||
return self._logZ[cid]
|
||||
|
||||
def _invalidate(self, cid: str):
|
||||
"""
|
||||
使缓存失效
|
||||
|
||||
Args:
|
||||
cid: 类别ID
|
||||
"""
|
||||
if cid in self._logZ:
|
||||
del self._logZ[cid]
|
||||
|
||||
def get_stats(self) -> Dict:
|
||||
"""获取统计信息"""
|
||||
return {
|
||||
"n_classes": len(self.cls_counts),
|
||||
"n_tokens": sum(len(tc) for tc in self.token_counts.values()),
|
||||
"total_counts": sum(self.cls_counts.values()),
|
||||
}
|
||||
62
src/chat/express/expressor_model/tokenizer.py
Normal file
62
src/chat/express/expressor_model/tokenizer.py
Normal file
@@ -0,0 +1,62 @@
|
||||
"""
|
||||
文本分词器，支持中文Jieba分词
|
||||
"""
|
||||
from typing import List
|
||||
|
||||
from src.common.logger import get_logger
|
||||
|
||||
logger = get_logger("expressor.tokenizer")
|
||||
|
||||
|
||||
class Tokenizer:
|
||||
"""文本分词器，支持中文Jieba分词"""
|
||||
|
||||
def __init__(self, stopwords: set = None, use_jieba: bool = True):
|
||||
"""
|
||||
Args:
|
||||
stopwords: 停用词集合
|
||||
use_jieba: 是否使用jieba分词
|
||||
"""
|
||||
self.stopwords = stopwords or set()
|
||||
self.use_jieba = use_jieba
|
||||
|
||||
if use_jieba:
|
||||
try:
|
||||
import jieba
|
||||
|
||||
jieba.initialize()
|
||||
logger.info("Jieba分词器初始化成功")
|
||||
except ImportError:
|
||||
logger.warning("Jieba未安装，将使用字符级分词")
|
||||
self.use_jieba = False
|
||||
|
||||
def tokenize(self, text: str) -> List[str]:
|
||||
"""
|
||||
分词并返回token列表
|
||||
|
||||
Args:
|
||||
text: 输入文本
|
||||
|
||||
Returns:
|
||||
token列表
|
||||
"""
|
||||
if not text:
|
||||
return []
|
||||
|
||||
# 使用jieba分词
|
||||
if self.use_jieba:
|
||||
try:
|
||||
import jieba
|
||||
|
||||
tokens = list(jieba.cut(text))
|
||||
except Exception as e:
|
||||
logger.warning(f"Jieba分词失败，使用字符级分词: {e}")
|
||||
tokens = list(text)
|
||||
else:
|
||||
# 简单按字符分词
|
||||
tokens = list(text)
|
||||
|
||||
# 过滤停用词和空字符串
|
||||
tokens = [token.strip() for token in tokens if token.strip() and token not in self.stopwords]
|
||||
|
||||
return tokens
|
||||
162
src/chat/express/situation_extractor.py
Normal file
162
src/chat/express/situation_extractor.py
Normal file
@@ -0,0 +1,162 @@
|
||||
"""
|
||||
情境提取器
|
||||
从聊天历史中提取当前的情境（situation），用于 StyleLearner 预测
|
||||
"""
|
||||
from typing import Optional
|
||||
|
||||
from src.chat.utils.prompt import Prompt, global_prompt_manager
|
||||
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("situation_extractor")
|
||||
|
||||
|
||||
def init_prompt():
|
||||
situation_extraction_prompt = """
|
||||
以下是正在进行的聊天内容：
|
||||
{chat_history}
|
||||
|
||||
你的名字是{bot_name}{target_message_info}
|
||||
|
||||
请分析当前聊天的情境特征，提取出最能描述当前情境的1-3个关键场景描述。
|
||||
|
||||
场景描述应该：
|
||||
1. 简洁明了（每个不超过20个字）
|
||||
2. 聚焦情绪、话题、氛围
|
||||
3. 不涉及具体人名
|
||||
4. 类似于"表示惊讶"、"讨论游戏"、"表达赞同"这样的格式
|
||||
|
||||
请以纯文本格式输出，每行一个场景描述，不要有序号、引号或其他格式：
|
||||
|
||||
例如：
|
||||
表示惊讶和意外
|
||||
讨论技术问题
|
||||
表达友好的赞同
|
||||
|
||||
现在请提取当前聊天的情境：
|
||||
"""
|
||||
Prompt(situation_extraction_prompt, "situation_extraction_prompt")
|
||||
|
||||
|
||||
class SituationExtractor:
|
||||
"""情境提取器，从聊天历史中提取当前情境"""
|
||||
|
||||
def __init__(self):
|
||||
self.llm_model = LLMRequest(
|
||||
model_set=model_config.model_task_config.utils_small,
|
||||
request_type="expression.situation_extractor"
|
||||
)
|
||||
|
||||
async def extract_situations(
|
||||
self,
|
||||
chat_history: list | str,
|
||||
target_message: Optional[str] = None,
|
||||
max_situations: int = 3
|
||||
) -> list[str]:
|
||||
"""
|
||||
从聊天历史中提取情境
|
||||
|
||||
Args:
|
||||
chat_history: 聊天历史（列表或字符串）
|
||||
target_message: 目标消息（可选）
|
||||
max_situations: 最多提取的情境数量
|
||||
|
||||
Returns:
|
||||
情境描述列表
|
||||
"""
|
||||
# 转换chat_history为字符串
|
||||
if isinstance(chat_history, list):
|
||||
chat_info = "\n".join([
|
||||
f"{msg.get('sender', 'Unknown')}: {msg.get('content', '')}"
|
||||
for msg in chat_history
|
||||
])
|
||||
else:
|
||||
chat_info = chat_history
|
||||
|
||||
# 构建目标消息信息
|
||||
if target_message:
|
||||
target_message_info = f"，现在你想要回复消息：{target_message}"
|
||||
else:
|
||||
target_message_info = ""
|
||||
|
||||
# 构建 prompt
|
||||
try:
|
||||
prompt = (await global_prompt_manager.get_prompt_async("situation_extraction_prompt")).format(
|
||||
bot_name=global_config.bot.nickname,
|
||||
chat_history=chat_info,
|
||||
target_message_info=target_message_info
|
||||
)
|
||||
|
||||
# 调用 LLM
|
||||
response, _ = await self.llm_model.generate_response_async(
|
||||
prompt=prompt,
|
||||
temperature=0.3
|
||||
)
|
||||
|
||||
if not response or not response.strip():
|
||||
logger.warning("LLM返回空响应，无法提取情境")
|
||||
return []
|
||||
|
||||
# 解析响应
|
||||
situations = self._parse_situations(response, max_situations)
|
||||
|
||||
if situations:
|
||||
logger.debug(f"提取到 {len(situations)} 个情境: {situations}")
|
||||
else:
|
||||
logger.warning(f"无法从LLM响应中解析出情境。响应:\n{response}")
|
||||
|
||||
return situations
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取情境失败: {e}")
|
||||
return []
|
||||
|
||||
@staticmethod
|
||||
def _parse_situations(response: str, max_situations: int) -> list[str]:
|
||||
"""
|
||||
解析 LLM 返回的情境描述
|
||||
|
||||
Args:
|
||||
response: LLM 响应
|
||||
max_situations: 最多返回的情境数量
|
||||
|
||||
Returns:
|
||||
情境描述列表
|
||||
"""
|
||||
situations = []
|
||||
|
||||
for line in response.splitlines():
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
# 移除可能的序号、引号等
|
||||
line = line.lstrip('0123456789.、-*>）)】] \t"\'""''')
|
||||
line = line.rstrip('"\'""''')
|
||||
line = line.strip()
|
||||
|
||||
if not line:
|
||||
continue
|
||||
|
||||
# 过滤掉明显不是情境描述的内容
|
||||
if len(line) > 30: # 太长
|
||||
continue
|
||||
if len(line) < 2: # 太短
|
||||
continue
|
||||
if any(keyword in line.lower() for keyword in ['例如', '注意', '请', '分析', '总结']):
|
||||
continue
|
||||
|
||||
situations.append(line)
|
||||
|
||||
if len(situations) >= max_situations:
|
||||
break
|
||||
|
||||
return situations
|
||||
|
||||
|
||||
# 初始化 prompt
|
||||
init_prompt()
|
||||
|
||||
# 全局单例
|
||||
situation_extractor = SituationExtractor()
|
||||
425
src/chat/express/style_learner.py
Normal file
425
src/chat/express/style_learner.py
Normal file
@@ -0,0 +1,425 @@
|
||||
"""
|
||||
风格学习引擎
|
||||
基于ExpressorModel实现的表达风格学习和预测系统
|
||||
支持多聊天室独立建模和在线学习
|
||||
"""
|
||||
import os
|
||||
import time
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
from src.common.logger import get_logger
|
||||
|
||||
from .expressor_model import ExpressorModel
|
||||
|
||||
logger = get_logger("expressor.style_learner")
|
||||
|
||||
|
||||
class StyleLearner:
|
||||
"""单个聊天室的表达风格学习器"""
|
||||
|
||||
def __init__(self, chat_id: str, model_config: Optional[Dict] = None):
|
||||
"""
|
||||
Args:
|
||||
chat_id: 聊天室ID
|
||||
model_config: 模型配置
|
||||
"""
|
||||
self.chat_id = chat_id
|
||||
self.model_config = model_config or {
|
||||
"alpha": 0.5,
|
||||
"beta": 0.5,
|
||||
"gamma": 0.99, # 衰减因子，支持遗忘
|
||||
"vocab_size": 200000,
|
||||
"use_jieba": True,
|
||||
}
|
||||
|
||||
# 初始化表达模型
|
||||
self.expressor = ExpressorModel(**self.model_config)
|
||||
|
||||
# 动态风格管理
|
||||
self.max_styles = 2000 # 每个chat_id最多2000个风格
|
||||
self.style_to_id: Dict[str, str] = {} # style文本 -> style_id
|
||||
self.id_to_style: Dict[str, str] = {} # style_id -> style文本
|
||||
self.id_to_situation: Dict[str, str] = {} # style_id -> situation文本
|
||||
self.next_style_id = 0
|
||||
|
||||
# 学习统计
|
||||
self.learning_stats = {
|
||||
"total_samples": 0,
|
||||
"style_counts": {},
|
||||
"last_update": time.time(),
|
||||
}
|
||||
|
||||
logger.info(f"StyleLearner初始化成功: chat_id={chat_id}")
|
||||
|
||||
def add_style(self, style: str, situation: Optional[str] = None) -> bool:
|
||||
"""
|
||||
动态添加一个新的风格
|
||||
|
||||
Args:
|
||||
style: 风格文本
|
||||
situation: 情境文本
|
||||
|
||||
Returns:
|
||||
是否添加成功
|
||||
"""
|
||||
try:
|
||||
# 检查是否已存在
|
||||
if style in self.style_to_id:
|
||||
return True
|
||||
|
||||
# 检查是否超过最大限制
|
||||
if len(self.style_to_id) >= self.max_styles:
|
||||
logger.warning(f"已达到最大风格数量限制 ({self.max_styles})")
|
||||
return False
|
||||
|
||||
# 生成新的style_id
|
||||
style_id = f"style_{self.next_style_id}"
|
||||
self.next_style_id += 1
|
||||
|
||||
# 添加到映射
|
||||
self.style_to_id[style] = style_id
|
||||
self.id_to_style[style_id] = style
|
||||
if situation:
|
||||
self.id_to_situation[style_id] = situation
|
||||
|
||||
# 添加到expressor模型
|
||||
self.expressor.add_candidate(style_id, style, situation)
|
||||
|
||||
# 初始化统计
|
||||
self.learning_stats["style_counts"][style_id] = 0
|
||||
|
||||
logger.debug(f"添加风格成功: {style_id} -> {style}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"添加风格失败: {e}")
|
||||
return False
|
||||
|
||||
def learn_mapping(self, up_content: str, style: str) -> bool:
|
||||
"""
|
||||
学习一个up_content到style的映射
|
||||
|
||||
Args:
|
||||
up_content: 前置内容
|
||||
style: 目标风格
|
||||
|
||||
Returns:
|
||||
是否学习成功
|
||||
"""
|
||||
try:
|
||||
# 如果style不存在，先添加它
|
||||
if style not in self.style_to_id:
|
||||
if not self.add_style(style):
|
||||
return False
|
||||
|
||||
# 获取style_id
|
||||
style_id = self.style_to_id[style]
|
||||
|
||||
# 使用正反馈学习
|
||||
self.expressor.update_positive(up_content, style_id)
|
||||
|
||||
# 更新统计
|
||||
self.learning_stats["total_samples"] += 1
|
||||
self.learning_stats["style_counts"][style_id] += 1
|
||||
self.learning_stats["last_update"] = time.time()
|
||||
|
||||
logger.debug(f"学习映射成功: {up_content[:20]}... -> {style}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"学习映射失败: {e}")
|
||||
return False
|
||||
|
||||
def predict_style(self, up_content: str, top_k: int = 5) -> Tuple[Optional[str], Dict[str, float]]:
|
||||
"""
|
||||
根据up_content预测最合适的style
|
||||
|
||||
Args:
|
||||
up_content: 前置内容
|
||||
top_k: 返回前k个候选
|
||||
|
||||
Returns:
|
||||
(最佳style文本, 所有候选的分数字典)
|
||||
"""
|
||||
try:
|
||||
# 先检查是否有训练数据
|
||||
if not self.style_to_id:
|
||||
logger.debug(f"StyleLearner还没有任何训练数据: chat_id={self.chat_id}")
|
||||
return None, {}
|
||||
|
||||
best_style_id, scores = self.expressor.predict(up_content, k=top_k)
|
||||
|
||||
if best_style_id is None:
|
||||
logger.debug(f"ExpressorModel未返回预测结果: chat_id={self.chat_id}, up_content={up_content[:50]}...")
|
||||
return None, {}
|
||||
|
||||
# 将style_id转换为style文本
|
||||
best_style = self.id_to_style.get(best_style_id)
|
||||
|
||||
if best_style is None:
|
||||
logger.warning(
|
||||
f"style_id无法转换为style文本: style_id={best_style_id}, "
|
||||
f"已知的id_to_style数量={len(self.id_to_style)}"
|
||||
)
|
||||
return None, {}
|
||||
|
||||
# 转换所有分数
|
||||
style_scores = {}
|
||||
for sid, score in scores.items():
|
||||
style_text = self.id_to_style.get(sid)
|
||||
if style_text:
|
||||
style_scores[style_text] = score
|
||||
else:
|
||||
logger.warning(f"跳过无法转换的style_id: {sid}")
|
||||
|
||||
logger.debug(
|
||||
f"预测成功: up_content={up_content[:30]}..., "
|
||||
f"best_style={best_style}, top3_scores={list(style_scores.items())[:3]}"
|
||||
)
|
||||
|
||||
return best_style, style_scores
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"预测style失败: {e}", exc_info=True)
|
||||
return None, {}
|
||||
|
||||
def get_style_info(self, style: str) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""
|
||||
获取style的完整信息
|
||||
|
||||
Args:
|
||||
style: 风格文本
|
||||
|
||||
Returns:
|
||||
(style_id, situation)
|
||||
"""
|
||||
style_id = self.style_to_id.get(style)
|
||||
if not style_id:
|
||||
return None, None
|
||||
|
||||
situation = self.id_to_situation.get(style_id)
|
||||
return style_id, situation
|
||||
|
||||
def get_all_styles(self) -> List[str]:
|
||||
"""
|
||||
获取所有风格列表
|
||||
|
||||
Returns:
|
||||
风格文本列表
|
||||
"""
|
||||
return list(self.style_to_id.keys())
|
||||
|
||||
def apply_decay(self, factor: Optional[float] = None):
|
||||
"""
|
||||
应用知识衰减
|
||||
|
||||
Args:
|
||||
factor: 衰减因子
|
||||
"""
|
||||
self.expressor.decay(factor)
|
||||
logger.debug(f"应用知识衰减: chat_id={self.chat_id}")
|
||||
|
||||
def save(self, base_path: str) -> bool:
|
||||
"""
|
||||
保存学习器到文件
|
||||
|
||||
Args:
|
||||
base_path: 基础保存路径
|
||||
|
||||
Returns:
|
||||
是否保存成功
|
||||
"""
|
||||
try:
|
||||
# 创建保存目录
|
||||
save_dir = os.path.join(base_path, self.chat_id)
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
|
||||
# 保存expressor模型
|
||||
model_path = os.path.join(save_dir, "expressor_model.pkl")
|
||||
self.expressor.save(model_path)
|
||||
|
||||
# 保存映射关系和统计信息
|
||||
import pickle
|
||||
|
||||
meta_path = os.path.join(save_dir, "meta.pkl")
|
||||
meta_data = {
|
||||
"style_to_id": self.style_to_id,
|
||||
"id_to_style": self.id_to_style,
|
||||
"id_to_situation": self.id_to_situation,
|
||||
"next_style_id": self.next_style_id,
|
||||
"learning_stats": self.learning_stats,
|
||||
}
|
||||
|
||||
with open(meta_path, "wb") as f:
|
||||
pickle.dump(meta_data, f)
|
||||
|
||||
logger.info(f"StyleLearner保存成功: {save_dir}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"保存StyleLearner失败: {e}")
|
||||
return False
|
||||
|
||||
def load(self, base_path: str) -> bool:
|
||||
"""
|
||||
从文件加载学习器
|
||||
|
||||
Args:
|
||||
base_path: 基础加载路径
|
||||
|
||||
Returns:
|
||||
是否加载成功
|
||||
"""
|
||||
try:
|
||||
save_dir = os.path.join(base_path, self.chat_id)
|
||||
|
||||
# 检查目录是否存在
|
||||
if not os.path.exists(save_dir):
|
||||
logger.debug(f"StyleLearner保存目录不存在: {save_dir}")
|
||||
return False
|
||||
|
||||
# 加载expressor模型
|
||||
model_path = os.path.join(save_dir, "expressor_model.pkl")
|
||||
if os.path.exists(model_path):
|
||||
self.expressor.load(model_path)
|
||||
|
||||
# 加载映射关系和统计信息
|
||||
import pickle
|
||||
|
||||
meta_path = os.path.join(save_dir, "meta.pkl")
|
||||
if os.path.exists(meta_path):
|
||||
with open(meta_path, "rb") as f:
|
||||
meta_data = pickle.load(f)
|
||||
|
||||
self.style_to_id = meta_data["style_to_id"]
|
||||
self.id_to_style = meta_data["id_to_style"]
|
||||
self.id_to_situation = meta_data["id_to_situation"]
|
||||
self.next_style_id = meta_data["next_style_id"]
|
||||
self.learning_stats = meta_data["learning_stats"]
|
||||
|
||||
logger.info(f"StyleLearner加载成功: {save_dir}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"加载StyleLearner失败: {e}")
|
||||
return False
|
||||
|
||||
def get_stats(self) -> Dict:
|
||||
"""获取统计信息"""
|
||||
model_stats = self.expressor.get_stats()
|
||||
return {
|
||||
"chat_id": self.chat_id,
|
||||
"n_styles": len(self.style_to_id),
|
||||
"total_samples": self.learning_stats["total_samples"],
|
||||
"last_update": self.learning_stats["last_update"],
|
||||
"model_stats": model_stats,
|
||||
}
|
||||
|
||||
|
||||
class StyleLearnerManager:
|
||||
"""多聊天室表达风格学习管理器"""
|
||||
|
||||
def __init__(self, model_save_path: str = "data/expression/style_models"):
|
||||
"""
|
||||
Args:
|
||||
model_save_path: 模型保存路径
|
||||
"""
|
||||
self.learners: Dict[str, StyleLearner] = {}
|
||||
self.model_save_path = model_save_path
|
||||
|
||||
# 确保保存目录存在
|
||||
os.makedirs(model_save_path, exist_ok=True)
|
||||
|
||||
logger.info(f"StyleLearnerManager初始化成功, 模型保存路径: {model_save_path}")
|
||||
|
||||
def get_learner(self, chat_id: str, model_config: Optional[Dict] = None) -> StyleLearner:
|
||||
"""
|
||||
获取或创建指定chat_id的学习器
|
||||
|
||||
Args:
|
||||
chat_id: 聊天室ID
|
||||
model_config: 模型配置
|
||||
|
||||
Returns:
|
||||
StyleLearner实例
|
||||
"""
|
||||
if chat_id not in self.learners:
|
||||
# 创建新的学习器
|
||||
learner = StyleLearner(chat_id, model_config)
|
||||
|
||||
# 尝试加载已保存的模型
|
||||
learner.load(self.model_save_path)
|
||||
|
||||
self.learners[chat_id] = learner
|
||||
|
||||
return self.learners[chat_id]
|
||||
|
||||
def learn_mapping(self, chat_id: str, up_content: str, style: str) -> bool:
|
||||
"""
|
||||
学习一个映射关系
|
||||
|
||||
Args:
|
||||
chat_id: 聊天室ID
|
||||
up_content: 前置内容
|
||||
style: 目标风格
|
||||
|
||||
Returns:
|
||||
是否学习成功
|
||||
"""
|
||||
learner = self.get_learner(chat_id)
|
||||
return learner.learn_mapping(up_content, style)
|
||||
|
||||
def predict_style(self, chat_id: str, up_content: str, top_k: int = 5) -> Tuple[Optional[str], Dict[str, float]]:
|
||||
"""
|
||||
预测最合适的风格
|
||||
|
||||
Args:
|
||||
chat_id: 聊天室ID
|
||||
up_content: 前置内容
|
||||
top_k: 返回前k个候选
|
||||
|
||||
Returns:
|
||||
(最佳style, 分数字典)
|
||||
"""
|
||||
learner = self.get_learner(chat_id)
|
||||
return learner.predict_style(up_content, top_k)
|
||||
|
||||
def save_all(self) -> bool:
|
||||
"""
|
||||
保存所有学习器
|
||||
|
||||
Returns:
|
||||
是否全部保存成功
|
||||
"""
|
||||
success = True
|
||||
for chat_id, learner in self.learners.items():
|
||||
if not learner.save(self.model_save_path):
|
||||
success = False
|
||||
|
||||
logger.info(f"保存所有StyleLearner {'成功' if success else '部分失败'}")
|
||||
return success
|
||||
|
||||
def apply_decay_all(self, factor: Optional[float] = None):
|
||||
"""
|
||||
对所有学习器应用知识衰减
|
||||
|
||||
Args:
|
||||
factor: 衰减因子
|
||||
"""
|
||||
for learner in self.learners.values():
|
||||
learner.apply_decay(factor)
|
||||
|
||||
logger.info(f"对所有StyleLearner应用知识衰减")
|
||||
|
||||
def get_all_stats(self) -> Dict[str, Dict]:
|
||||
"""
|
||||
获取所有学习器的统计信息
|
||||
|
||||
Returns:
|
||||
{chat_id: stats}
|
||||
"""
|
||||
return {chat_id: learner.get_stats() for chat_id, learner in self.learners.items()}
|
||||
|
||||
|
||||
# 全局单例
|
||||
style_learner_manager = StyleLearnerManager()
|
||||
@@ -46,6 +46,9 @@ class StreamLoopManager:
|
||||
# 状态控制
|
||||
self.is_running = False
|
||||
|
||||
# 每个流的上一次间隔值（用于日志去重）
|
||||
self._last_intervals: dict[str, float] = {}
|
||||
|
||||
logger.info(f"流循环管理器初始化完成 (最大并发流数: {self.max_concurrent_streams})")
|
||||
|
||||
async def start(self) -> None:
|
||||
@@ -285,7 +288,11 @@ class StreamLoopManager:
|
||||
interval = await self._calculate_interval(stream_id, has_messages)
|
||||
|
||||
# 6. sleep等待下次检查
|
||||
logger.info(f"流 {stream_id} 等待 {interval:.2f}s")
|
||||
# 只在间隔发生变化时输出日志，避免刷屏
|
||||
last_interval = self._last_intervals.get(stream_id)
|
||||
if last_interval is None or abs(interval - last_interval) > 0.01:
|
||||
logger.info(f"流 {stream_id} 等待周期变化: {interval:.2f}s")
|
||||
self._last_intervals[stream_id] = interval
|
||||
await asyncio.sleep(interval)
|
||||
|
||||
except asyncio.CancelledError:
|
||||
@@ -316,6 +323,9 @@ class StreamLoopManager:
|
||||
except Exception as e:
|
||||
logger.debug(f"释放自适应流处理槽位失败: {e}")
|
||||
|
||||
# 清理间隔记录
|
||||
self._last_intervals.pop(stream_id, None)
|
||||
|
||||
logger.info(f"流循环结束: {stream_id}")
|
||||
|
||||
async def _get_stream_context(self, stream_id: str) -> Any | None:
|
||||
|
||||
@@ -5,6 +5,7 @@ from typing import Any
|
||||
|
||||
from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
|
||||
from src.chat.utils.timer_calculator import Timer
|
||||
from src.common.data_models.database_data_model import DatabaseMessages
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config
|
||||
from src.person_info.person_info import get_person_info_manager
|
||||
@@ -142,7 +143,7 @@ class ChatterActionManager:
|
||||
self,
|
||||
action_name: str,
|
||||
chat_id: str,
|
||||
target_message: dict | None = None,
|
||||
target_message: dict | DatabaseMessages | None = None,
|
||||
reasoning: str = "",
|
||||
action_data: dict | None = None,
|
||||
thinking_id: str | None = None,
|
||||
@@ -262,9 +263,15 @@ class ChatterActionManager:
|
||||
from_plugin=False,
|
||||
)
|
||||
if not success or not response_set:
|
||||
logger.info(
|
||||
f"对 {target_message.get('processed_plain_text') if target_message else '未知消息'} 的回复生成失败"
|
||||
)
|
||||
# 安全地获取 processed_plain_text
|
||||
if isinstance(target_message, DatabaseMessages):
|
||||
msg_text = target_message.processed_plain_text or "未知消息"
|
||||
elif target_message:
|
||||
msg_text = target_message.get("processed_plain_text", "未知消息")
|
||||
else:
|
||||
msg_text = "未知消息"
|
||||
|
||||
logger.info(f"对 {msg_text} 的回复生成失败")
|
||||
return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
|
||||
except asyncio.CancelledError:
|
||||
logger.debug(f"{log_prefix} 并行执行：回复生成任务已被取消")
|
||||
@@ -322,8 +329,11 @@ class ChatterActionManager:
|
||||
|
||||
# 获取目标消息ID
|
||||
target_message_id = None
|
||||
if target_message and isinstance(target_message, dict):
|
||||
target_message_id = target_message.get("message_id")
|
||||
if target_message:
|
||||
if isinstance(target_message, DatabaseMessages):
|
||||
target_message_id = target_message.message_id
|
||||
elif isinstance(target_message, dict):
|
||||
target_message_id = target_message.get("message_id")
|
||||
elif action_data and isinstance(action_data, dict):
|
||||
target_message_id = action_data.get("target_message_id")
|
||||
|
||||
@@ -488,14 +498,19 @@ class ChatterActionManager:
|
||||
person_info_manager = get_person_info_manager()
|
||||
|
||||
# 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
|
||||
platform = action_message.get("chat_info_platform")
|
||||
if platform is None:
|
||||
platform = getattr(chat_stream, "platform", "unknown")
|
||||
if isinstance(action_message, DatabaseMessages):
|
||||
platform = action_message.chat_info.platform
|
||||
user_id = action_message.user_info.user_id
|
||||
else:
|
||||
platform = action_message.get("chat_info_platform")
|
||||
if platform is None:
|
||||
platform = getattr(chat_stream, "platform", "unknown")
|
||||
user_id = action_message.get("user_id", "")
|
||||
|
||||
# 获取用户信息并生成回复提示
|
||||
person_id = person_info_manager.get_person_id(
|
||||
platform,
|
||||
action_message.get("user_id", ""),
|
||||
user_id,
|
||||
)
|
||||
person_name = await person_info_manager.get_value(person_id, "person_name")
|
||||
action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
|
||||
@@ -565,7 +580,14 @@ class ChatterActionManager:
|
||||
|
||||
# 根据新消息数量决定是否需要引用回复
|
||||
reply_text = ""
|
||||
is_proactive_thinking = (message_data.get("message_type") == "proactive_thinking") if message_data else True
|
||||
# 检查是否为主动思考消息
|
||||
if isinstance(message_data, DatabaseMessages):
|
||||
# DatabaseMessages 对象没有 message_type 字段，默认为 False
|
||||
is_proactive_thinking = False
|
||||
elif message_data:
|
||||
is_proactive_thinking = message_data.get("message_type") == "proactive_thinking"
|
||||
else:
|
||||
is_proactive_thinking = True
|
||||
|
||||
logger.debug(f"[send_response] message_data: {message_data}")
|
||||
|
||||
|
||||
@@ -27,6 +27,7 @@ from src.chat.utils.prompt import Prompt, global_prompt_manager
|
||||
from src.chat.utils.prompt_params import PromptParameters
|
||||
from src.chat.utils.timer_calculator import Timer
|
||||
from src.chat.utils.utils import get_chat_type_and_target_info
|
||||
from src.common.data_models.database_data_model import DatabaseMessages
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.individuality.individuality import get_individuality
|
||||
@@ -474,10 +475,13 @@ class DefaultReplyer:
|
||||
style_habits = []
|
||||
grammar_habits = []
|
||||
|
||||
# 使用从处理器传来的选中表达方式
|
||||
# LLM模式：调用LLM选择5-10个，然后随机选5个
|
||||
selected_expressions = await expression_selector.select_suitable_expressions_llm(
|
||||
self.chat_stream.stream_id, chat_history, max_num=8, min_num=2, target_message=target
|
||||
# 使用统一的表达方式选择入口（支持classic和exp_model模式）
|
||||
selected_expressions = await expression_selector.select_suitable_expressions(
|
||||
chat_id=self.chat_stream.stream_id,
|
||||
chat_history=chat_history,
|
||||
target_message=target,
|
||||
max_num=8,
|
||||
min_num=2
|
||||
)
|
||||
|
||||
if selected_expressions:
|
||||
@@ -1208,7 +1212,7 @@ class DefaultReplyer:
|
||||
extra_info: str = "",
|
||||
available_actions: dict[str, ActionInfo] | None = None,
|
||||
enable_tool: bool = True,
|
||||
reply_message: dict[str, Any] | None = None,
|
||||
reply_message: dict[str, Any] | DatabaseMessages | None = None,
|
||||
) -> str:
|
||||
"""
|
||||
构建回复器上下文
|
||||
@@ -1250,10 +1254,24 @@ class DefaultReplyer:
|
||||
if reply_message is None:
|
||||
logger.warning("reply_message 为 None，无法构建prompt")
|
||||
return ""
|
||||
platform = reply_message.get("chat_info_platform")
|
||||
|
||||
# 统一处理 DatabaseMessages 对象和字典
|
||||
if isinstance(reply_message, DatabaseMessages):
|
||||
platform = reply_message.chat_info.platform
|
||||
user_id = reply_message.user_info.user_id
|
||||
user_nickname = reply_message.user_info.user_nickname
|
||||
user_cardname = reply_message.user_info.user_cardname
|
||||
processed_plain_text = reply_message.processed_plain_text
|
||||
else:
|
||||
platform = reply_message.get("chat_info_platform")
|
||||
user_id = reply_message.get("user_id")
|
||||
user_nickname = reply_message.get("user_nickname")
|
||||
user_cardname = reply_message.get("user_cardname")
|
||||
processed_plain_text = reply_message.get("processed_plain_text")
|
||||
|
||||
person_id = person_info_manager.get_person_id(
|
||||
platform, # type: ignore
|
||||
reply_message.get("user_id"), # type: ignore
|
||||
user_id, # type: ignore
|
||||
)
|
||||
person_name = await person_info_manager.get_value(person_id, "person_name")
|
||||
|
||||
@@ -1262,22 +1280,22 @@ class DefaultReplyer:
|
||||
# 尝试从reply_message获取用户名
|
||||
await person_info_manager.first_knowing_some_one(
|
||||
platform, # type: ignore
|
||||
reply_message.get("user_id"), # type: ignore
|
||||
reply_message.get("user_nickname") or "",
|
||||
reply_message.get("user_cardname") or "",
|
||||
user_id, # type: ignore
|
||||
user_nickname or "",
|
||||
user_cardname or "",
|
||||
)
|
||||
|
||||
# 检查是否是bot自己的名字，如果是则替换为"(你)"
|
||||
bot_user_id = str(global_config.bot.qq_account)
|
||||
current_user_id = await person_info_manager.get_value(person_id, "user_id")
|
||||
current_platform = reply_message.get("chat_info_platform")
|
||||
current_platform = platform
|
||||
|
||||
if current_user_id == bot_user_id and current_platform == global_config.bot.platform:
|
||||
sender = f"{person_name}(你)"
|
||||
else:
|
||||
# 如果不是bot自己，直接使用person_name
|
||||
sender = person_name
|
||||
target = reply_message.get("processed_plain_text")
|
||||
target = processed_plain_text
|
||||
|
||||
# 最终的空值检查，确保sender和target不为None
|
||||
if sender is None:
|
||||
@@ -1611,15 +1629,22 @@ class DefaultReplyer:
|
||||
raw_reply: str,
|
||||
reason: str,
|
||||
reply_to: str,
|
||||
reply_message: dict[str, Any] | None = None,
|
||||
reply_message: dict[str, Any] | DatabaseMessages | None = None,
|
||||
) -> str: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
|
||||
chat_stream = self.chat_stream
|
||||
chat_id = chat_stream.stream_id
|
||||
is_group_chat = bool(chat_stream.group_info)
|
||||
|
||||
if reply_message:
|
||||
sender = reply_message.get("sender")
|
||||
target = reply_message.get("target")
|
||||
if isinstance(reply_message, DatabaseMessages):
|
||||
# 从 DatabaseMessages 对象获取 sender 和 target
|
||||
# 注意: DatabaseMessages 没有直接的 sender/target 字段
|
||||
# 需要根据实际情况构造
|
||||
sender = reply_message.user_info.user_nickname or reply_message.user_info.user_id
|
||||
target = reply_message.processed_plain_text or ""
|
||||
else:
|
||||
sender = reply_message.get("sender")
|
||||
target = reply_message.get("target")
|
||||
else:
|
||||
sender, target = self._parse_reply_target(reply_to)
|
||||
|
||||
@@ -1891,42 +1916,64 @@ 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):
|
||||
|
||||
@@ -606,11 +606,11 @@ class Prompt:
|
||||
recent_messages, replace_bot_name=True, timestamp_mode="normal", truncate=True
|
||||
)
|
||||
|
||||
# 使用LLM选择与当前情景匹配的表达习惯
|
||||
# 使用统一的表达方式选择入口（支持classic和exp_model模式）
|
||||
expression_selector = ExpressionSelector(self.parameters.chat_id)
|
||||
selected_expressions = await expression_selector.select_suitable_expressions_llm(
|
||||
selected_expressions = await expression_selector.select_suitable_expressions(
|
||||
chat_id=self.parameters.chat_id,
|
||||
chat_info=chat_history,
|
||||
chat_history=chat_history,
|
||||
target_message=self.parameters.target,
|
||||
)
|
||||
|
||||
@@ -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.
|
||||
|
||||
@@ -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)
|
||||
|
||||
@@ -187,6 +187,10 @@ class ExpressionRule(ValidatedConfigBase):
|
||||
class ExpressionConfig(ValidatedConfigBase):
|
||||
"""表达配置类"""
|
||||
|
||||
mode: Literal["classic", "exp_model"] = Field(
|
||||
default="classic",
|
||||
description="表达方式选择模式: classic=经典LLM评估, exp_model=机器学习模型预测"
|
||||
)
|
||||
rules: list[ExpressionRule] = Field(default_factory=list, description="表达学习规则")
|
||||
|
||||
@staticmethod
|
||||
|
||||
14
src/main.py
14
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")
|
||||
|
||||
@@ -107,10 +107,13 @@ class PromptBuilder:
|
||||
style_habits = []
|
||||
grammar_habits = []
|
||||
|
||||
# 使用从处理器传来的选中表达方式
|
||||
# LLM模式：调用LLM选择5-10个，然后随机选5个
|
||||
selected_expressions = await expression_selector.select_suitable_expressions_llm(
|
||||
chat_stream.stream_id, chat_history, max_num=12, min_num=5, target_message=target
|
||||
# 使用统一的表达方式选择入口（支持classic和exp_model模式）
|
||||
selected_expressions = await expression_selector.select_suitable_expressions(
|
||||
chat_id=chat_stream.stream_id,
|
||||
chat_history=chat_history,
|
||||
target_message=target,
|
||||
max_num=12,
|
||||
min_num=5
|
||||
)
|
||||
|
||||
if selected_expressions:
|
||||
@@ -163,13 +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
|
||||
|
||||
|
||||
@@ -120,13 +120,15 @@ class RelationshipFetcher:
|
||||
know_since = await person_info_manager.get_value(person_id, "know_since")
|
||||
last_know = await person_info_manager.get_value(person_id, "last_know")
|
||||
|
||||
# 如果用户没有基本信息，返回默认描述
|
||||
if person_name == nickname_str and not short_impression and not full_impression:
|
||||
return f"你完全不认识{person_name}，这是你们第一次交流。"
|
||||
|
||||
# 获取用户特征点
|
||||
current_points = await person_info_manager.get_value(person_id, "points") or []
|
||||
forgotten_points = await person_info_manager.get_value(person_id, "forgotten_points") or []
|
||||
|
||||
# 确保 points 是列表类型（可能从数据库返回字符串）
|
||||
if not isinstance(current_points, list):
|
||||
current_points = []
|
||||
if not isinstance(forgotten_points, list):
|
||||
forgotten_points = []
|
||||
|
||||
# 按时间排序并选择最有代表性的特征点
|
||||
all_points = current_points + forgotten_points
|
||||
@@ -177,28 +179,48 @@ 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
|
||||
# 查询用户关系数据（修复：添加 await）
|
||||
user_id = str(await 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:
|
||||
# db_query 返回字典列表，使用字典访问方式
|
||||
rel_data = relationships[0]
|
||||
if rel_data.relationship_text:
|
||||
relation_parts.append(f"关系记录：{rel_data.relationship_text}")
|
||||
if rel_data.relationship_score:
|
||||
score_desc = self._get_relationship_score_description(rel_data.relationship_score)
|
||||
relation_parts.append(f"关系亲密程度：{score_desc}")
|
||||
|
||||
# 5.1 用户别名
|
||||
if rel_data.get("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.get("relationship_text"):
|
||||
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.get("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.get("relationship_score") is not None:
|
||||
score_desc = self._get_relationship_score_description(rel_data["relationship_score"])
|
||||
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}")
|
||||
logger.error(f"查询UserRelationships表失败: {e}", exc_info=True)
|
||||
|
||||
# 构建最终的关系信息字符串
|
||||
if relation_parts:
|
||||
@@ -206,10 +228,90 @@ class RelationshipFetcher:
|
||||
[f"• {part}" for part in relation_parts]
|
||||
)
|
||||
else:
|
||||
relation_info = f"你对{person_name}了解不多，这是比较初步的交流。"
|
||||
# 只有当所有数据源都没有信息时才返回默认文本
|
||||
relation_info = f"你完全不认识{person_name}，这是你们第一次交流。"
|
||||
|
||||
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 ""
|
||||
|
||||
# db_query 返回字典列表，使用字典访问方式
|
||||
stream_data = streams[0]
|
||||
impression_parts = []
|
||||
|
||||
# 1. 聊天环境基本信息
|
||||
if stream_data.get("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.get("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.get("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.get("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.get("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:
|
||||
|
||||
@@ -108,52 +108,79 @@ def message_dict_to_message_recv(message_dict: dict[str, Any]) -> MessageRecv |
|
||||
"""查找要回复的消息
|
||||
|
||||
Args:
|
||||
message_dict: 消息字典
|
||||
message_dict: 消息字典或 DatabaseMessages 对象
|
||||
|
||||
Returns:
|
||||
Optional[MessageRecv]: 找到的消息，如果没找到则返回None
|
||||
"""
|
||||
# 兼容 DatabaseMessages 对象和字典
|
||||
if isinstance(message_dict, dict):
|
||||
user_platform = message_dict.get("user_platform", "")
|
||||
user_id = message_dict.get("user_id", "")
|
||||
user_nickname = message_dict.get("user_nickname", "")
|
||||
user_cardname = message_dict.get("user_cardname", "")
|
||||
chat_info_group_id = message_dict.get("chat_info_group_id")
|
||||
chat_info_group_platform = message_dict.get("chat_info_group_platform", "")
|
||||
chat_info_group_name = message_dict.get("chat_info_group_name", "")
|
||||
chat_info_platform = message_dict.get("chat_info_platform", "")
|
||||
message_id = message_dict.get("message_id") or message_dict.get("chat_info_message_id") or message_dict.get("id")
|
||||
time_val = message_dict.get("time")
|
||||
additional_config = message_dict.get("additional_config")
|
||||
processed_plain_text = message_dict.get("processed_plain_text")
|
||||
else:
|
||||
# DatabaseMessages 对象
|
||||
user_platform = getattr(message_dict, "user_platform", "")
|
||||
user_id = getattr(message_dict, "user_id", "")
|
||||
user_nickname = getattr(message_dict, "user_nickname", "")
|
||||
user_cardname = getattr(message_dict, "user_cardname", "")
|
||||
chat_info_group_id = getattr(message_dict, "chat_info_group_id", None)
|
||||
chat_info_group_platform = getattr(message_dict, "chat_info_group_platform", "")
|
||||
chat_info_group_name = getattr(message_dict, "chat_info_group_name", "")
|
||||
chat_info_platform = getattr(message_dict, "chat_info_platform", "")
|
||||
message_id = getattr(message_dict, "message_id", None)
|
||||
time_val = getattr(message_dict, "time", None)
|
||||
additional_config = getattr(message_dict, "additional_config", None)
|
||||
processed_plain_text = getattr(message_dict, "processed_plain_text", "")
|
||||
|
||||
# 构建MessageRecv对象
|
||||
user_info = {
|
||||
"platform": message_dict.get("user_platform", ""),
|
||||
"user_id": message_dict.get("user_id", ""),
|
||||
"user_nickname": message_dict.get("user_nickname", ""),
|
||||
"user_cardname": message_dict.get("user_cardname", ""),
|
||||
"platform": user_platform,
|
||||
"user_id": user_id,
|
||||
"user_nickname": user_nickname,
|
||||
"user_cardname": user_cardname,
|
||||
}
|
||||
|
||||
group_info = {}
|
||||
if message_dict.get("chat_info_group_id"):
|
||||
if chat_info_group_id:
|
||||
group_info = {
|
||||
"platform": message_dict.get("chat_info_group_platform", ""),
|
||||
"group_id": message_dict.get("chat_info_group_id", ""),
|
||||
"group_name": message_dict.get("chat_info_group_name", ""),
|
||||
"platform": chat_info_group_platform,
|
||||
"group_id": chat_info_group_id,
|
||||
"group_name": chat_info_group_name,
|
||||
}
|
||||
|
||||
format_info = {"content_format": "", "accept_format": ""}
|
||||
template_info = {"template_items": {}}
|
||||
|
||||
message_info = {
|
||||
"platform": message_dict.get("chat_info_platform", ""),
|
||||
"message_id": message_dict.get("message_id")
|
||||
or message_dict.get("chat_info_message_id")
|
||||
or message_dict.get("id"),
|
||||
"time": message_dict.get("time"),
|
||||
"platform": chat_info_platform,
|
||||
"message_id": message_id,
|
||||
"time": time_val,
|
||||
"group_info": group_info,
|
||||
"user_info": user_info,
|
||||
"additional_config": message_dict.get("additional_config"),
|
||||
"additional_config": additional_config,
|
||||
"format_info": format_info,
|
||||
"template_info": template_info,
|
||||
}
|
||||
|
||||
new_message_dict = {
|
||||
"message_info": message_info,
|
||||
"raw_message": message_dict.get("processed_plain_text"),
|
||||
"processed_plain_text": message_dict.get("processed_plain_text"),
|
||||
"raw_message": processed_plain_text,
|
||||
"processed_plain_text": processed_plain_text,
|
||||
}
|
||||
|
||||
message_recv = MessageRecv(new_message_dict)
|
||||
|
||||
logger.info(f"[SendAPI] 找到匹配的回复消息，发送者: {message_dict.get('user_nickname', '')}")
|
||||
logger.info(f"[SendAPI] 找到匹配的回复消息，发送者: {user_nickname}")
|
||||
return message_recv
|
||||
|
||||
|
||||
|
||||
@@ -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]]:
|
||||
|
||||
@@ -6,6 +6,7 @@ from abc import ABC, abstractmethod
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from src.common.data_models.database_data_model import DatabaseMessages
|
||||
from src.common.logger import get_logger
|
||||
from src.plugin_system.apis import database_api, message_api, send_api
|
||||
from src.plugin_system.base.component_types import ActionActivationType, ActionInfo, ChatMode, ChatType, ComponentType
|
||||
@@ -180,11 +181,18 @@ class BaseAction(ABC):
|
||||
|
||||
if self.has_action_message:
|
||||
if self.action_name != "no_reply":
|
||||
self.group_id = str(self.action_message.get("chat_info_group_id", None))
|
||||
self.group_name = self.action_message.get("chat_info_group_name", None)
|
||||
|
||||
self.user_id = str(self.action_message.get("user_id", None))
|
||||
self.user_nickname = self.action_message.get("user_nickname", None)
|
||||
# 统一处理 DatabaseMessages 对象和字典
|
||||
if isinstance(self.action_message, DatabaseMessages):
|
||||
self.group_id = str(self.action_message.group_info.group_id if self.action_message.group_info else None)
|
||||
self.group_name = self.action_message.group_info.group_name if self.action_message.group_info else None
|
||||
self.user_id = str(self.action_message.user_info.user_id)
|
||||
self.user_nickname = self.action_message.user_info.user_nickname
|
||||
else:
|
||||
self.group_id = str(self.action_message.get("chat_info_group_id", None))
|
||||
self.group_name = self.action_message.get("chat_info_group_name", None)
|
||||
self.user_id = str(self.action_message.get("user_id", None))
|
||||
self.user_nickname = self.action_message.get("user_nickname", None)
|
||||
|
||||
if self.group_id:
|
||||
self.is_group = True
|
||||
self.target_id = self.group_id
|
||||
|
||||
@@ -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]:
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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, chat_stream: Any = None):
|
||||
super().__init__(plugin_config, chat_stream)
|
||||
|
||||
# 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
|
||||
@@ -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"],
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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
|
||||
370
src/plugins/built_in/affinity_flow_chatter/user_profile_tool.py
Normal file
370
src/plugins/built_in/affinity_flow_chatter/user_profile_tool.py
Normal file
@@ -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, chat_stream: Any = None):
|
||||
super().__init__(plugin_config, chat_stream)
|
||||
|
||||
# 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
|
||||
@@ -6,6 +6,7 @@ from typing import ClassVar
|
||||
from dateutil.parser import parse as parse_datetime
|
||||
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from src.common.data_models.database_data_model import DatabaseMessages
|
||||
from src.common.logger import get_logger
|
||||
from src.manager.async_task_manager import AsyncTask, async_task_manager
|
||||
from src.person_info.person_info import get_person_info_manager
|
||||
@@ -253,19 +254,19 @@ class SetEmojiLikeAction(BaseAction):
|
||||
message_id = None
|
||||
set_like = self.action_data.get("set", True)
|
||||
|
||||
if self.has_action_message and isinstance(self.action_message, dict):
|
||||
message_id = self.action_message.get("message_id")
|
||||
logger.info(f"获取到的消息ID: {message_id}")
|
||||
else:
|
||||
if self.has_action_message:
|
||||
if isinstance(self.action_message, DatabaseMessages):
|
||||
message_id = self.action_message.message_id
|
||||
logger.info(f"获取到的消息ID: {message_id}")
|
||||
elif isinstance(self.action_message, dict):
|
||||
message_id = self.action_message.get("message_id")
|
||||
logger.info(f"获取到的消息ID: {message_id}")
|
||||
|
||||
if not message_id:
|
||||
logger.error("未提供有效的消息或消息ID")
|
||||
await self.store_action_info(action_prompt_display="贴表情失败: 未提供消息ID", action_done=False)
|
||||
return False, "未提供消息ID"
|
||||
|
||||
if not message_id:
|
||||
logger.error("消息ID为空")
|
||||
await self.store_action_info(action_prompt_display="贴表情失败: 消息ID为空", action_done=False)
|
||||
return False, "消息ID为空"
|
||||
|
||||
available_models = llm_api.get_available_models()
|
||||
if "utils_small" not in available_models:
|
||||
logger.error("未找到 'utils_small' 模型配置，无法选择表情")
|
||||
@@ -273,7 +274,12 @@ class SetEmojiLikeAction(BaseAction):
|
||||
|
||||
model_to_use = available_models["utils_small"]
|
||||
|
||||
context_text = self.action_message.get("processed_plain_text", "")
|
||||
# 统一处理 DatabaseMessages 和字典
|
||||
if isinstance(self.action_message, DatabaseMessages):
|
||||
context_text = self.action_message.processed_plain_text or ""
|
||||
else:
|
||||
context_text = self.action_message.get("processed_plain_text", "")
|
||||
|
||||
if not context_text:
|
||||
logger.error("无法找到动作选择的原始消息文本")
|
||||
return False, "无法找到动作选择的原始消息文本"
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
[inner]
|
||||
version = "7.5.1"
|
||||
version = "7.5.2"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了MoFox-Bot，不需要阅读----
|
||||
#如果你想要修改配置文件，请递增version的值
|
||||
@@ -92,6 +92,11 @@ compress_identity = true # 是否压缩身份，压缩后会精简身份信息
|
||||
|
||||
[expression]
|
||||
# 表达学习配置
|
||||
# mode: 表达方式模式，可选：
|
||||
# - "classic": 经典模式，随机抽样 + LLM选择
|
||||
# - "exp_model": 表达模型模式，使用机器学习模型预测最合适的表达
|
||||
mode = "classic"
|
||||
|
||||
# rules是一个列表，每个元素都是一个学习规则
|
||||
# chat_stream_id: 聊天流ID，格式为 "platform:id:type"，例如 "qq:123456:private"。空字符串""表示全局配置
|
||||
# use_expression: 是否使用学到的表达 (true/false)
|
||||
|
||||
Reference in New Issue
Block a user