e3802eff60
- 新增 AgentLearningPattern 模型和 agent_learning_service 服务 - 执行前注入历史学习模式到 system prompt 作为工具选择建议 - 执行后自动提取工具序列并保存/累计学习模式 - 支持任务分类(11类)、关键词提取、工具序列合并、有效性评分 - 集成到 AgentRuntime.run()/run_stream()，支持 bare chat 和 Agent 模式 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
89 lines
3.5 KiB
Python
89 lines
3.5 KiB
Python
"""
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Agent Runtime 配置与数据结构 Schema
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Field
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class AgentToolConfig(BaseModel):
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"""Agent 可用工具配置"""
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# 若为空列表则使用全部已注册工具
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include_tools: List[str] = Field(default_factory=list, description="允许的工具名称白名单")
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exclude_tools: List[str] = Field(default_factory=list, description="排除的工具名称黑名单")
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class AgentMemoryConfig(BaseModel):
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"""Agent 记忆配置"""
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enabled: bool = True
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max_history_messages: int = 20 # 注入 LLM 的上文最大消息数
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session_key: Optional[str] = None # 会话标识，默认自动生成
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persist_to_db: bool = True # 是否写入 MySQL 长期记忆
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vector_memory_enabled: bool = True # 是否启用向量记忆（语义检索）
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vector_memory_top_k: int = 5 # 向量检索 Top-K
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learning_enabled: bool = True # 是否启用自主学习（工具模式学习）
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class AgentLLMConfig(BaseModel):
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"""Agent 模型配置"""
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provider: str = "openai" # openai / deepseek
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model: str = "gpt-4o-mini"
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temperature: float = 0.7
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max_tokens: Optional[int] = None
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api_key: Optional[str] = None
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base_url: Optional[str] = None
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max_iterations: int = 10 # ReAct 循环最大步数
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request_timeout: float = 120.0
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extra_body: Optional[Dict[str, Any]] = None
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class AgentBudgetConfig(BaseModel):
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"""Agent 执行预算配置"""
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max_llm_invocations: int = 200 # LLM 调用次数上限
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max_tool_calls: int = 500 # 工具调用次数上限
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class AgentConfig(BaseModel):
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"""Agent 完整配置"""
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name: str = "default_agent"
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system_prompt: str = "你是一个有用的AI助手。请使用可用工具来帮助用户完成任务。"
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llm: AgentLLMConfig = Field(default_factory=AgentLLMConfig)
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tools: AgentToolConfig = Field(default_factory=AgentToolConfig)
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memory: AgentMemoryConfig = Field(default_factory=AgentMemoryConfig)
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budget: AgentBudgetConfig = Field(default_factory=AgentBudgetConfig)
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user_id: Optional[str] = None
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# 持久记忆 / 向量记忆的 scope_id；不设时沿用 user_id 或 name（易与其他 Agent 串记忆）
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memory_scope_id: Optional[str] = None
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class AgentMessage(BaseModel):
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"""Agent 对话消息"""
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role: str # user / assistant / tool
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content: str
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tool_calls: Optional[List[Dict[str, Any]]] = None
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tool_call_id: Optional[str] = None
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name: Optional[str] = None
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class AgentStep(BaseModel):
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"""Agent 单步执行记录（用于执行追踪）"""
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iteration: int = Field(..., description="第几步")
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type: str = Field(..., description="步骤类型: think / tool_call / tool_result / final")
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content: str = Field(default="", description="步骤内容")
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tool_name: Optional[str] = Field(default=None, description="工具名称（tool_call/tool_result 类型时）")
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tool_input: Optional[Dict[str, Any]] = Field(default=None, description="工具输入参数")
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tool_result: Optional[str] = Field(default=None, description="工具执行结果")
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reasoning: Optional[str] = Field(default=None, description="思考过程")
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class AgentResult(BaseModel):
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"""Agent 执行结果"""
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success: bool = True
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content: str = ""
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truncated: bool = False
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iterations_used: int = 0
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tool_calls_made: int = 0
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error: Optional[str] = None
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steps: List[AgentStep] = Field(default_factory=list, description="执行追踪步骤详情")
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