96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
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import uuid
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import logging
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from langchain.schema import HumanMessage, SystemMessage
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from ..core.llm import get_llm, COMPLIANCE_CHECK_PROMPT
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from ..services.rag import hybrid_search
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/compliance", tags=["合规审查"])
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class ComplianceCheckRequest(BaseModel):
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query: str
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regulation_domains: list[str] = ["vehicle_safety"]
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top_k: int = 5
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class ComplianceCheckResponse(BaseModel):
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risk_level: str
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risk_score: float
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findings: list[dict]
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recommendations: list[str]
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sources: list[dict]
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@router.post("/check", response_model=ComplianceCheckResponse)
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async def check_compliance(req: ComplianceCheckRequest):
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"""
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对输入内容进行合规性检查，与法规库比对后给出风险评估。
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"""
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# 检索相关法规（从多个域检索）
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all_chunks = []
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for domain in req.regulation_domains:
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chunks = await hybrid_search(
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req.query,
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collection_name="regulation_chunks",
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top_k=req.top_k,
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)
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all_chunks.extend(chunks)
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# 去重 + 按分数排序
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seen = set()
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unique_chunks = []
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for c in sorted(all_chunks, key=lambda x: x["score"], reverse=True):
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if c["id"] not in seen:
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seen.add(c["id"])
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unique_chunks.append(c)
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top_chunks = unique_chunks[:req.top_k]
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if not top_chunks:
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return ComplianceCheckResponse(
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risk_level="unknown",
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risk_score=0,
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findings=[{"issue": "未找到相关法规，请先上传法规文档"}],
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recommendations=["上传相关法规文档到知识库后重试"],
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sources=[],
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)
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# 构建法规上下文
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regulations_text = "\n\n".join(
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f"[{i+1}] {c['content'][:500]}" for i, c in enumerate(top_chunks)
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)
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prompt = COMPLIANCE_CHECK_PROMPT.format(
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content=req.query,
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regulations=regulations_text,
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)
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llm = get_llm(temperature=0.0)
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try:
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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analysis = response.content
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except Exception as e:
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logger.error(f"LLM 合规分析失败：{e}")
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analysis = f"LLM 分析失败：{e}"
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# 简单解析 LLM 输出（生产可用结构化输出）
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risk_level = "medium"
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risk_score = 50.0
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if "critical" in analysis.lower() or "严重" in analysis:
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risk_level, risk_score = "critical", 90.0
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elif "high" in analysis.lower() or "高风险" in analysis:
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risk_level, risk_score = "high", 70.0
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elif "low" in analysis.lower() or "低风险" in analysis:
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risk_level, risk_score = "low", 20.0
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return ComplianceCheckResponse(
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risk_level=risk_level,
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risk_score=risk_score,
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findings=[{"analysis": analysis}],
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recommendations=["请参考上述分析进行整改"],
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sources=[{"content": c["content"][:200], "score": c["score"]} for c in top_chunks],
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)
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