NathanAlex7
c4ac5aa82e
新增兴邦金租评审项目模块, 作为 mini-OpenClaw 之上的完整交付: - 五智能体流水线(data_curator/risk_scout/case_finder/approval_advisor/risk_monitor) - evolution 自进化子系统(轨迹采集+奖励计算+模式归纳prompt增强) - /api/xingbang/* 端点(config/workflow/stream/evolution/trajectories) - 运行时产物(客户产出情报卡 + 进化轨迹数据)已 gitignore, 不含隐私 遵循兴邦三档分级审批制度, 数据源指向兴邦实验数据包
146 lines
4.7 KiB
Python
146 lines
4.7 KiB
Python
"""Xingbang API — 兴邦金租评审工作流 + 自进化模块的 HTTP 接口。
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端:
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POST /api/xingbang/workflow 运行五智能体审批流水线(返回全部产出)
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POST /api/xingbang/workflow/stream 流式运行(SSE, 逐步展示)
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GET /api/xingbang/evolution 查看自进化洞察
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GET /api/xingbang/trajectories 列出历史轨迹
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GET /api/xingbang/trajectories/{id} 读取单条轨迹
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复用 mini-OpenClaw 的 AgentManager.llm 与后端基础设施。
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"""
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from __future__ import annotations
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import json
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import os
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from pathlib import Path
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from typing import Any
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from fastapi import APIRouter
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from pydantic import BaseModel
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from sse_starlette.sse import EventSourceResponse
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from graph.agent import agent_manager
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from xingbang.workflow import XingbangWorkflow
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from xingbang.config import XINGBANG_CONFIG
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router = APIRouter(prefix="/api/xingbang", tags=["xingbang"])
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# 懒加载单例工作流(复用 agent_manager.llm)
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_workflow: XingbangWorkflow | None = None
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def _get_workflow() -> XingbangWorkflow:
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global _workflow
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if _workflow is None:
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if agent_manager._llm is None:
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# 若 agent 未初始化, 手动初始化(需要 BASE_DIR)
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from pathlib import Path as _P
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_workflow = XingbangWorkflow(
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llm=None,
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base_dir=_P(__file__).resolve().parent.parent,
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evolution_enabled=True,
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)
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raise RuntimeError("Agent 未初始化, 请先启动后端(会自动初始化 agent_manager)")
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_workflow = XingbangWorkflow(
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llm=agent_manager._llm,
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base_dir=Path(__file__).resolve().parent.parent,
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evolution_enabled=True,
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)
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return _workflow
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class WorkflowRequest(BaseModel):
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task: str # 客户/项目描述
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customer: str = "" # 客户名(用于轨迹标注)
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@router.get("/config")
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async def get_config():
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"""返回兴邦项目配置(数据源、五智能体、档位)。"""
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return XINGBANG_CONFIG
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@router.post("/workflow")
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async def run_workflow(req: WorkflowRequest):
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"""运行完整五智能体审批流水线。"""
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wf = _get_workflow()
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try:
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result = await wf.run(req.task, customer=req.customer)
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return result
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except RuntimeError as e:
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from fastapi import HTTPException
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raise HTTPException(status_code=500, detail=str(e))
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@router.post("/workflow/stream")
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async def run_workflow_stream(req: WorkflowRequest):
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"""流式运行(SSE), 逐步展示五智能体进度与产出。"""
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wf = _get_workflow()
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async def gen():
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async for event in wf.run_stream(req.task, customer=req.customer):
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yield {
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"event": event.get("event", "event"),
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"data": json.dumps(
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{k: v for k, v in event.items() if k != "event"},
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ensure_ascii=False,
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),
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}
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return EventSourceResponse(gen())
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class FeedbackRequest(BaseModel):
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trajectory_id: str
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feedback: str # "positive" | "negative"
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@router.post("/evolution/feedback")
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async def submit_feedback(req: FeedbackRequest):
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"""提交用户反馈(Level 2 奖励信号), 追加进轨迹。"""
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try:
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traj_file = (
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Path(__file__).resolve().parent.parent
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/ "xingbang_evolution" / "trajectories" / f"{req.trajectory_id}.json"
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)
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if not traj_file.exists():
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return {"error": f"轨迹不存在: {req.trajectory_id}"}
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data = json.loads(traj_file.read_text(encoding="utf-8"))
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data.setdefault("user_feedback", req.feedback)
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data["user_feedback"] = req.feedback
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traj_file.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
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return {"status": "ok", "trajectory_id": req.trajectory_id, "feedback": req.feedback}
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except Exception as e:
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return {"error": str(e)}
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@router.get("/evolution")
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async def get_evolution():
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"""查看自进化洞察报告。"""
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wf = _get_workflow()
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report = wf.evolution.generate_insights()
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return {"insights": report, "config": XINGBANG_CONFIG}
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@router.get("/trajectories")
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async def list_trajectories():
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"""列出所有已保存轨迹。"""
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wf = _get_workflow()
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paths = wf.recorder.list_trajectories()
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return {"count": len(paths),
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"trajectories": [p.stem for p in paths]}
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@router.get("/trajectories/{trajectory_id}")
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async def get_trajectory(trajectory_id: str):
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"""读取单条轨迹详情。"""
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wf = _get_workflow()
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traj = wf.recorder.load(trajectory_id)
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if not traj:
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from fastapi import HTTPException
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raise HTTPException(status_code=404, detail="轨迹不存在")
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return traj
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