168 lines
4.7 KiB
Python
168 lines
4.7 KiB
Python
"""
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智能体执行 API 端点。
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"""
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from typing import List, Optional
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import uuid
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from fastapi import APIRouter, Depends, HTTPException, status
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from pydantic import BaseModel, Field
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from sqlalchemy.orm import Session
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from app.db.session import get_db
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from app.services.agent_orchestrator import AgentOrchestrator
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from app.services.model_manager import ModelManager
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from app.services.kb_manager import KnowledgeBaseManager
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from app.services.job_manager import AsyncJobManager
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from app.config import get_settings
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from app.utils.logger import get_logger
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logger = get_logger(__name__)
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router = APIRouter(prefix="/agent", tags=["agent"])
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# Pydantic 模式
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class AgentExecuteRequest(BaseModel):
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"""智能体执行请求的模式。"""
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task: str = Field(..., description="Task description for the agent")
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kb_id: Optional[int] = Field(None, description="Knowledge base ID for retriever tool")
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llm_name: Optional[str] = Field(None, description="LLM model name")
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class AgentExecuteResponse(BaseModel):
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"""智能体执行响应的模式。"""
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agent_id: str
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output: str
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success: bool
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error: Optional[str] = None
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class ToolCallResponse(BaseModel):
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"""工具调用响应的模式。"""
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id: int
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agent_id: str
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tool_name: str
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call_input: dict
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call_output: dict
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created_at: str
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class Config:
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from_attributes = True
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class AgentLogsResponse(BaseModel):
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"""智能体日志响应的模式。"""
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agent_id: str
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tool_calls: List[ToolCallResponse]
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total_calls: int
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# 依赖项
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def get_agent_orchestrator(
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db: Session = Depends(get_db),
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) -> AgentOrchestrator:
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"""获取 AgentOrchestrator 实例。"""
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settings = get_settings()
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model_manager = ModelManager(db, settings)
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job_manager = AsyncJobManager()
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kb_manager = KnowledgeBaseManager(db, model_manager, job_manager, settings)
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return AgentOrchestrator(db, model_manager, kb_manager, settings)
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@router.post("/execute", response_model=AgentExecuteResponse)
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async def execute_agent(
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request: AgentExecuteRequest,
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orchestrator: AgentOrchestrator = Depends(get_agent_orchestrator),
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):
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"""
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使用工具执行智能体任务。
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智能体可以访问：
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- 计算器工具：用于数学计算
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- 知识库检索工具：用于搜索文档（如果提供了 kb_id）
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Args:
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request: 智能体执行请求
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orchestrator: AgentOrchestrator 实例
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Returns:
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带有输出和状态的执行结果
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"""
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try:
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# 生成唯一的智能体 ID
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agent_id = str(uuid.uuid4())
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logger.info(
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"agent_execute_request",
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agent_id=agent_id,
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task=request.task[:100],
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kb_id=request.kb_id,
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)
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# 执行智能体
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result = await orchestrator.execute_agent(
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task=request.task,
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agent_id=agent_id,
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kb_id=request.kb_id,
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llm_name=request.llm_name,
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)
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return AgentExecuteResponse(**result)
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except Exception as e:
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logger.error("agent_execute_api_failed", error=str(e))
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return AgentExecuteResponse(
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agent_id=str(uuid.uuid4()),
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output="",
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success=False,
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error=f"Agent execution failed: {str(e)}",
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)
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@router.get("/logs/{agent_id}", response_model=AgentLogsResponse)
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def get_agent_logs(
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agent_id: str,
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limit: int = 50,
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offset: int = 0,
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orchestrator: AgentOrchestrator = Depends(get_agent_orchestrator),
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):
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"""
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获取智能体执行的工具调用日志。
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Args:
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agent_id: 智能体执行 ID
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limit: 最大记录数（默认 50）
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offset: 分页偏移量（默认 0）
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orchestrator: AgentOrchestrator 实例
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Returns:
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包含工具调用的智能体执行日志
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"""
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try:
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tool_calls = orchestrator.get_tool_calls(
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agent_id=agent_id,
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limit=limit,
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offset=offset,
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)
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return AgentLogsResponse(
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agent_id=agent_id,
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tool_calls=[
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ToolCallResponse(
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id=tc.id,
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agent_id=tc.agent_id,
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tool_name=tc.tool_name,
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call_input=tc.call_input or {},
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call_output=tc.call_output or {},
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created_at=tc.created_at.isoformat() if tc.created_at else "",
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)
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for tc in tool_calls
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],
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total_calls=len(tool_calls),
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)
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except Exception as e:
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logger.error("agent_logs_failed", agent_id=agent_id, error=str(e))
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Failed to get agent logs: {str(e)}",
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)
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