415 lines
15 KiB
Python
415 lines
15 KiB
Python
"""
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AI对话系统 v2.0.0 - 数据库模型重构
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支持：大模型池、Agent管理、渠道独立绑定、思考功能开关
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"""
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from sqlalchemy import create_engine, Column, Integer, String, Text, Boolean, DateTime, ForeignKey, JSON, Float
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import sessionmaker, relationship
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from datetime import datetime
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import os
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DATABASE_URL = os.environ.get('DATABASE_URL', 'sqlite:///./ai_chat_v2.db')
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engine = create_engine(DATABASE_URL, connect_args={"check_same_thread": False})
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SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
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Base = declarative_base()
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# ==================== 大模型池 ====================
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class LLMProvider(Base):
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"""大模型提供商/池配置"""
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__tablename__ = 'llm_providers'
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id = Column(Integer, primary_key=True, index=True)
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name = Column(String(100), unique=True, index=True) # 名称标识，如 "DeepSeek", "本地LLM"
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api_base = Column(String(500)) # API地址
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api_key = Column(String(500)) # API密钥
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models = Column(JSON, default=list) # 可用模型列表 [{"id": "xxx", "name": "xxx"}]
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default_model = Column(String(100)) # 默认模型
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# 思考功能支持
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supports_thinking = Column(Boolean, default=False) # 是否原生支持思考
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thinking_model = Column(String(100), nullable=True) # 思考模式模型名（如有单独模型）
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# 视觉能力支持
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supports_vision = Column(Boolean, default=False) # 是否支持图片理解（多模态）
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vision_model = Column(String(100), nullable=True) # 视觉模型名（如与默认模型不同）
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# 配额和限制
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max_tokens = Column(Integer, default=4096)
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temperature = Column(Float, default=0.7)
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# 状态
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is_active = Column(Boolean, default=True)
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priority = Column(Integer, default=0) # 优先级，用于自动选择
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description = Column(Text, nullable=True)
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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# 关联
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agents = relationship("Agent", back_populates="llm_provider")
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# ==================== Agent ====================
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class Agent(Base):
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"""智能体配置"""
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__tablename__ = 'agents'
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id = Column(Integer, primary_key=True, index=True)
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name = Column(String(100), unique=True, index=True) # Agent名称
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display_name = Column(String(100)) # 显示名称
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# 工具配置
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tools = Column(JSON, default=list) # 可用工具列表 ["search", "calculator", ...]
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# 大模型配置
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llm_provider_id = Column(Integer, ForeignKey('llm_providers.id'))
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model_override = Column(String(100), nullable=True) # 覆盖Provider默认模型
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# 系统设定
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system_prompt = Column(Text, default="你是一个有用的AI助手。") # 系统提示词
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# 思考功能
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enable_thinking = Column(Boolean, default=True) # 是否启用思考
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thinking_prompt = Column(Text, nullable=True) # 思考提示词模板
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thinking_prefix = Column(String(50), default="<think>") # 思考内容前缀标识
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thinking_suffix = Column(String(50), default="</think>") # 思考内容后缀标识
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# 其他配置
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max_history = Column(Integer, default=20) # 最大历史消息数
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temperature_override = Column(Float, nullable=True) # 覆盖温度
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# 状态
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is_active = Column(Boolean, default=True)
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is_default = Column(Boolean, default=False) # 是否为默认Agent
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description = Column(Text, nullable=True)
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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# 关联
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llm_provider = relationship("LLMProvider", back_populates="agents")
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channel_mappings = relationship("ChannelAgentMapping", back_populates="agent")
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# ==================== 渠道 ====================
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class Channel(Base):
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"""渠道配置 - 网页端、Matrix端等"""
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__tablename__ = 'channels'
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id = Column(Integer, primary_key=True, index=True)
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channel_type = Column(String(20), index=True) # web, matrix, telegram, etc.
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name = Column(String(100)) # 渠道名称，如 "网页端主入口", "Matrix AI Bot"
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# 渠道配置（JSON）
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config = Column(JSON, default=dict)
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# Matrix示例: {"homeserver": "xxx", "username": "xxx", "password": "xxx"}
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# Web示例: {"require_login": false, "session_timeout": 3600}
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# 状态
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is_active = Column(Boolean, default=True)
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is_primary = Column(Boolean, default=False) # 是否为主要渠道
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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# 关联
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agent_mappings = relationship("ChannelAgentMapping", back_populates="channel", order_by="ChannelAgentMapping.priority")
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conversations = relationship("Conversation", back_populates="channel")
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# ==================== 渠道-Agent映射 ====================
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class ChannelAgentMapping(Base):
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"""渠道与Agent的映射关系"""
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__tablename__ = 'channel_agent_mappings'
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id = Column(Integer, primary_key=True, index=True)
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channel_id = Column(Integer, ForeignKey('channels.id'))
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agent_id = Column(Integer, ForeignKey('agents.id'))
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# 映射配置
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priority = Column(Integer, default=0) # 优先级（数字越小优先级越高）
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mode = Column(String(20), default='single') # single(单Agent), round_robin(轮询), fallback(备用)
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weight = Column(Integer, default=1) # 轮询权重
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# 条件配置（可选）
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conditions = Column(JSON, nullable=True) # 条件触发，如 {"user_type": "vip"}
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is_active = Column(Boolean, default=True)
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created_at = Column(DateTime, default=datetime.utcnow)
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# 关联
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channel = relationship("Channel", back_populates="agent_mappings")
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agent = relationship("Agent", back_populates="channel_mappings")
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# ==================== 用户和会话（保留原有结构，增加渠道关联） ====================
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class User(Base):
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"""用户表"""
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__tablename__ = 'users'
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id = Column(Integer, primary_key=True, index=True)
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user_id = Column(String(100), unique=True, index=True)
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display_name = Column(String(100))
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user_type = Column(String(20), default='web') # web, matrix, telegram
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matrix_user_id = Column(String(200), nullable=True)
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# 用户级别（用于Agent选择条件）
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user_level = Column(String(20), default='normal') # normal, vip, admin
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created_at = Column(DateTime, default=datetime.utcnow)
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last_active_at = Column(DateTime, default=datetime.utcnow)
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is_active = Column(Boolean, default=True)
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conversations = relationship("Conversation", back_populates="user")
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class Conversation(Base):
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"""对话会话"""
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__tablename__ = 'conversations'
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id = Column(Integer, primary_key=True, index=True)
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conversation_id = Column(String(100), unique=True, index=True)
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user_id = Column(Integer, ForeignKey('users.id'))
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channel_id = Column(Integer, ForeignKey('channels.id'), nullable=True) # 新增渠道关联
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title = Column(String(200), nullable=True)
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# 当前使用的Agent（会话可切换Agent）
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current_agent_id = Column(Integer, ForeignKey('agents.id'), nullable=True)
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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is_active = Column(Boolean, default=True)
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extra_data = Column(JSON, nullable=True)
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user = relationship("User", back_populates="conversations")
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channel = relationship("Channel", back_populates="conversations")
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messages = relationship("Message", back_populates="conversation")
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class Message(Base):
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"""消息表"""
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__tablename__ = 'messages'
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id = Column(Integer, primary_key=True, index=True)
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conversation_id = Column(Integer, ForeignKey('conversations.id'))
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role = Column(String(20)) # user, assistant, system, thinking(思考过程)
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content = Column(Text)
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source = Column(String(20)) # web, matrix
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# 思考内容（如果启用了思考功能）
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thinking_content = Column(Text, nullable=True) # 思考过程
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# Agent信息
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agent_id = Column(Integer, ForeignKey('agents.id'), nullable=True) # 生成此消息的Agent
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model_used = Column(String(100), nullable=True) # 使用的模型
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created_at = Column(DateTime, default=datetime.utcnow)
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extra_data = Column(JSON, nullable=True)
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conversation = relationship("Conversation", back_populates="messages")
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# ==================== Matrix房间映射（保留） ====================
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class MatrixRoomMapping(Base):
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"""Matrix房间映射"""
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__tablename__ = 'matrix_room_mapping'
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id = Column(Integer, primary_key=True, index=True)
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room_id = Column(String(200), unique=True, index=True)
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user_id = Column(Integer, ForeignKey('users.id'))
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conversation_id = Column(Integer, ForeignKey('conversations.id'))
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channel_id = Column(Integer, ForeignKey('channels.id'), nullable=True)
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created_at = Column(DateTime, default=datetime.utcnow)
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# ==================== 搜索工具配置 ====================
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class ToolConfig(Base):
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"""工具配置（通用，支持搜索、计算器、代码执行等）"""
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__tablename__ = 'tool_configs'
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id = Column(Integer, primary_key=True, index=True)
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name = Column(String(100)) # 工具名称，如 "Tavily Search"、"Calculator"
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tool_type = Column(String(50), index=True) # 工具类型：search, calculator, code_runner, image_gen, etc.
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provider = Column(String(50), nullable=True) # 提供商（可选）：tavily, google, wolfram, etc.
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# API配置（JSON，不同工具可能有不同配置）
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config = Column(JSON, default=dict)
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# search示例: {"api_key": "xxx", "max_results": 5, "search_depth": "basic"}
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# calculator示例: {"api_base": "xxx"}
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# 状态
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is_active = Column(Boolean, default=True)
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is_default = Column(Boolean, default=False) # 是否为该类型的默认工具
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# 统计
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total_calls = Column(Integer, default=0) # 总调用次数
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success_calls = Column(Integer, default=0) # 成功次数
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failed_calls = Column(Integer, default=0) # 失败次数
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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class ToolUsageLog(Base):
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"""工具使用日志"""
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__tablename__ = 'tool_usage_logs'
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id = Column(Integer, primary_key=True, index=True)
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tool_id = Column(Integer, ForeignKey('tool_configs.id'))
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tool_type = Column(String(50), index=True)
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# 调用信息
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query = Column(Text) # 调用参数/查询内容
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success = Column(Boolean, default=True)
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error_message = Column(Text, nullable=True)
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result_summary = Column(Text, nullable=True) # 结果摘要
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# 关联信息
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conversation_id = Column(String(100), nullable=True)
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agent_id = Column(Integer, nullable=True)
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user_id = Column(String(100), nullable=True)
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# 性能
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duration_ms = Column(Integer, nullable=True) # 调用耗时（毫秒）
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called_at = Column(DateTime, default=datetime.utcnow)
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# ==================== 系统配置（保留） ====================
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class SystemConfig(Base):
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"""系统配置表"""
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__tablename__ = 'system_config'
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id = Column(Integer, primary_key=True, index=True)
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key = Column(String(100), unique=True, index=True)
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value = Column(Text)
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description = Column(String(500))
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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def init_db():
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"""初始化数据库"""
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Base.metadata.create_all(bind=engine)
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def get_db():
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"""获取数据库会话"""
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db = SessionLocal()
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try:
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yield db
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finally:
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db.close()
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# ==================== 初始化默认数据 ====================
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def init_default_data():
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"""初始化默认数据：大模型池、Agent、渠道"""
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db = SessionLocal()
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try:
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# 检查是否已有数据
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if db.query(LLMProvider).count() > 0:
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return
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# 1. 创建默认大模型池
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default_llm = LLMProvider(
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name="本地LLM Proxy",
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api_base="http://192.168.2.17:19007/v1",
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api_key="xxxx",
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models=[
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{"id": "auto", "name": "auto (自动选择)"},
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{"id": "qwen3.5-4b", "name": "qwen3.5-4b"},
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{"id": "dsv32", "name": "dsv32"},
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{"id": "glm-4", "name": "glm-4"}
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],
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default_model="auto",
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supports_thinking=False,
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is_active=True,
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priority=0,
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description="本地LLM代理服务"
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)
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db.add(default_llm)
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# 2. 创建默认Agent
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default_agent = Agent(
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name="default",
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display_name="默认助手",
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llm_provider_id=1,
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system_prompt="你是一个有用的AI助手。",
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enable_thinking=True,
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thinking_prompt="请先仔细思考这个问题，然后给出回答。",
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is_active=True,
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is_default=True,
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description="默认AI助手"
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)
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db.add(default_agent)
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# 3. 创建渠道
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web_channel = Channel(
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channel_type="web",
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name="网页端",
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config={"require_login": False},
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is_active=True,
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is_primary=True
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)
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db.add(web_channel)
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matrix_channel = Channel(
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channel_type="matrix",
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name="Matrix AI Bot",
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config={
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"homeserver": "http://matrix.tphai.com",
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"username": "@tester:matrix.tphai.com",
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"password": "tester12345@!"
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},
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is_active=True
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)
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db.add(matrix_channel)
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db.commit()
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# 4. 创建渠道-Agent映射
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web_mapping = ChannelAgentMapping(
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channel_id=1,
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agent_id=1,
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priority=0,
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mode='single'
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)
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db.add(web_mapping)
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matrix_mapping = ChannelAgentMapping(
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channel_id=2,
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agent_id=1,
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priority=0,
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mode='single'
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)
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db.add(matrix_mapping)
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# 5. 创建默认搜索工具配置
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search_config = SearchToolConfig(
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name="Tavily Search",
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provider="tavily",
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api_key="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",
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max_results=5,
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search_depth="basic",
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is_active=True,
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is_default=True
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)
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db.add(search_config)
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db.commit()
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print("默认数据初始化完成")
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finally:
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db.close() |