wehub-resource-sync
109 lines
4.5 KiB
Python
109 lines
4.5 KiB
Python
from __future__ import annotations
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from typing import Any
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from fastapi import APIRouter, Depends
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from pydantic import BaseModel, Field
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from sqlalchemy.ext.asyncio import AsyncSession
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from yuxi.services.agent_invocation_service import (
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create_agent_call_run_view,
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create_agent_eval_run_view,
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get_agent_call_run_result_view,
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)
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from yuxi.storage.postgres.models_business import User
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from server.utils.auth_middleware import get_db, get_required_user
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agent_invocation_router = APIRouter(prefix="/agent-invocation", tags=["agent-invocation"])
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class AgentCallRunCreate(BaseModel):
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agent_slug: str = Field(..., description="要调用的智能体 slug")
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messages: list[dict[str, Any]] = Field(..., description="消息列表，取最后一条 user 消息作为输入")
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stream: bool = Field(False, description="暂不支持流式，传 true 会返回 422")
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agent_call_meta: dict[str, Any] = Field(
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default_factory=dict,
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description="Agent Call 元数据；不允许通过 context 覆盖 Agent 运行上下文",
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)
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thread_id: str | None = Field(None, description="可选会话线程 ID，不传则自动创建临时线程")
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request_id: str | None = Field(None, description="可选请求幂等 ID，不传则自动生成")
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model_spec: str | None = Field(None, description="可选模型覆盖")
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async_mode: bool = Field(False, description="是否只创建运行并立即返回 run_id")
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class AgentCallRunResultRequest(BaseModel):
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run_id: str = Field(..., description="AgentRun ID")
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agent_slug: str | None = Field(None, description="可选，传入时校验 run 归属")
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class AgentEvaluationContext(BaseModel):
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dataset_name: str | None = Field(None, description="Langfuse dataset 名称")
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dataset_item_id: str | None = Field(None, description="Langfuse dataset item ID")
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experiment_name: str | None = Field(None, description="Langfuse experiment/run 名称")
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class AgentEvalRunCreate(BaseModel):
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query: str = Field(..., description="评估样例输入")
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agent_slug: str = Field(..., description="要运行的智能体 slug")
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evaluation: AgentEvaluationContext = Field(default_factory=AgentEvaluationContext, description="评估上下文")
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meta: dict = Field(default_factory=dict, description="可选，请求追踪信息，例如 request_id、attachment_file_ids")
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image_content: str | None = Field(None, description="可选，base64 图片内容")
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model_spec: str | None = Field(None, description="可选，对话级模型覆盖，优先级高于智能体配置")
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include_trajectory_summary: bool = Field(False, description="是否返回轻量工具调用轨迹摘要")
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@agent_invocation_router.post("/agent-call/runs")
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async def create_agent_call_run(
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payload: AgentCallRunCreate,
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current_user: User = Depends(get_required_user),
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db: AsyncSession = Depends(get_db),
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):
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"""创建外部系统 Agent 调用 run，并按 async_mode 决定是否等待最终结果。"""
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return await create_agent_call_run_view(
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agent_slug=payload.agent_slug,
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messages=payload.messages,
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agent_call_meta=payload.agent_call_meta,
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requested_thread_id=payload.thread_id,
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request_id=payload.request_id,
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model_spec=payload.model_spec,
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async_mode=payload.async_mode,
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stream=payload.stream,
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current_user=current_user,
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db=db,
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)
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@agent_invocation_router.post("/agent-call/runs/result")
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async def get_agent_call_run_result(
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payload: AgentCallRunResultRequest,
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current_user: User = Depends(get_required_user),
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db: AsyncSession = Depends(get_db),
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):
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"""读取外部 Agent 调用 run 的 OpenAI-compatible 结果结构。"""
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return await get_agent_call_run_result_view(
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run_id=payload.run_id,
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agent_slug=payload.agent_slug,
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current_uid=str(current_user.uid),
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db=db,
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)
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@agent_invocation_router.post("/eval/runs")
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async def create_agent_eval_run(
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payload: AgentEvalRunCreate,
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current_user: User = Depends(get_required_user),
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db: AsyncSession = Depends(get_db),
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):
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"""运行一次 CLI/Langfuse Agent 评估样例，并阻塞等待最终输出。"""
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return await create_agent_eval_run_view(
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query=payload.query,
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agent_slug=payload.agent_slug,
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evaluation=payload.evaluation.model_dump(exclude_none=True),
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meta=dict(payload.meta or {}),
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image_content=payload.image_content,
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model_spec=payload.model_spec,
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include_trajectory_summary=payload.include_trajectory_summary,
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current_user=current_user,
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db=db,
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)
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