592bca4f39
- 4.1 Fallback chain: LLM fallback_llm config in AgentLLMConfig, retry with alternate model on API failure; Agent fallback_agent in DAG nodes - 4.2 Knowledge sharing: GlobalKnowledge model with embedding-based semantic search, auto-extraction of tool names as tags after execution - 4.3 Async execution: execute_agent_task fully implemented with AgentRuntime, scheduler dual-path for workflow/non-workflow agents Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
71 lines
3.3 KiB
Python
71 lines
3.3 KiB
Python
"""
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智能体模型
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"""
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from sqlalchemy import Column, String, Text, Integer, DateTime, JSON, ForeignKey, func
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from sqlalchemy.dialects.mysql import CHAR
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from sqlalchemy.orm import relationship
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from app.core.database import Base
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import uuid
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class Agent(Base):
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"""智能体表"""
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__tablename__ = "agents"
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id = Column(CHAR(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="智能体ID")
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name = Column(String(100), nullable=False, comment="智能体名称")
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description = Column(Text, comment="描述")
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workflow_config = Column(JSON, nullable=False, comment="工作流配置")
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budget_config = Column(
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JSON,
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nullable=True,
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comment="执行预算：max_steps/max_llm_invocations/max_tool_calls（可选，覆盖全局默认）",
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)
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version = Column(Integer, default=1, comment="版本号")
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status = Column(String(20), default="draft", comment="状态: draft/published/running/stopped")
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user_id = Column(CHAR(36), ForeignKey("users.id"), comment="创建者ID")
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created_at = Column(DateTime, default=func.now(), comment="创建时间")
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updated_at = Column(DateTime, default=func.now(), onupdate=func.now(), comment="更新时间")
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# 关系
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user = relationship("User", backref="agents")
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def __repr__(self):
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return f"<Agent(id={self.id}, name={self.name})>"
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class AgentExtension(Base):
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"""Agent 自主扩展记录表 — 用于 extension_log 工具"""
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__tablename__ = "agent_extensions"
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id = Column(CHAR(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="扩展记录ID")
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extension_type = Column(String(50), nullable=False, comment="扩展类型: agent_created/tool_registered/code_tool_created")
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name = Column(String(100), nullable=False, comment="创建的Agent或工具名称")
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reason = Column(Text, comment="创建原因（为什么需要扩展）")
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detail = Column(JSON, comment="扩展详情（参数、结果等）")
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success_rating = Column(String(20), comment="效果评价: success/partial/failed")
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note = Column(Text, comment="反馈备注")
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user_id = Column(CHAR(36), ForeignKey("users.id"), nullable=True, comment="创建者ID")
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created_at = Column(DateTime, default=func.now(), comment="创建时间")
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def __repr__(self):
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return f"<AgentExtension(id={self.id}, type={self.extension_type}, name={self.name})>"
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class GlobalKnowledge(Base):
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"""Agent 间知识共享表 — 跨 Agent 的全局知识池"""
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__tablename__ = "global_knowledge"
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id = Column(CHAR(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="知识ID")
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content = Column(Text, nullable=False, comment="知识内容摘要")
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embedding = Column(Text, nullable=True, comment="内容 embedding（JSON 序列化）")
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source_agent_id = Column(CHAR(36), nullable=True, comment="来源 Agent ID")
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source_user_id = Column(CHAR(36), nullable=True, comment="来源用户 ID")
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tags = Column(JSON, nullable=True, comment="分类标签")
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scope_kind = Column(String(50), default="agent", comment="作用域类型")
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scope_id = Column(String(100), default="", comment="作用域 ID")
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created_at = Column(DateTime, default=func.now(), comment="创建时间")
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def __repr__(self):
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return f"<GlobalKnowledge(id={self.id}, source_agent={self.source_agent_id})>"
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