7989f3a159
后端新增风险图谱算法模块、风险观察与反馈服务、规则 DSL 校验器和可解释性引擎，完善系统仪表盘和财务仪表盘统计， 优化 agent 运行和编排执行链路，清理旧开发文档，前端新增 系统趋势、负载热力图等多种仪表盘图表组件，完善操作反馈 对话框和工作台日期选择器，优化报销创建和审批详情交互， 补充单元测试覆盖。
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
from __future__ import annotations
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from typing import Annotated
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from fastapi import APIRouter, Depends, Query, status
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from sqlalchemy.orm import Session
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from app.api.deps import get_db
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from app.schemas.agent_feedback import (
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AgentFeedbackCreate,
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AgentFeedbackRead,
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AgentFeedbackSummaryRead,
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)
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from app.services.agent_feedback import AgentFeedbackService
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router = APIRouter(prefix="/agent-feedback")
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DbSession = Annotated[Session, Depends(get_db)]
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@router.post(
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"",
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response_model=AgentFeedbackRead,
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status_code=status.HTTP_201_CREATED,
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summary="记录 Agent 操作评价",
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description="记录用户对一次智能体处理结果的 1-5 星评价和低分原因。",
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)
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def create_agent_feedback(payload: AgentFeedbackCreate, db: DbSession) -> AgentFeedbackRead:
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return AgentFeedbackService(db).create_feedback(payload)
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@router.get(
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"/summary",
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response_model=AgentFeedbackSummaryRead,
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summary="查询 Agent 操作评价统计",
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description="按最近反馈记录汇总评分分布、低分数量和低分原因。",
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)
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def summarize_agent_feedback(
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db: DbSession,
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agent: Annotated[str | None, Query(description="Agent 名称筛选。")] = None,
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session_type: Annotated[str | None, Query(description="会话类型筛选。")] = None,
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limit: Annotated[int, Query(ge=1, le=500, description="统计最近记录数。")] = 200,
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) -> AgentFeedbackSummaryRead:
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return AgentFeedbackService(db).summarize_feedback(
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agent=agent,
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session_type=session_type,
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limit=limit,
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)
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