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services/compliance-backend/app/api/compliance.py

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