241 lines
8.2 KiB
Python
241 lines
8.2 KiB
Python
"""AI 应用路由：应收风险 + 配额。
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- GET /api/v1/ai/risk/ranking/?top_n=&min_outstanding= 风险客户排行
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- GET /api/v1/ai/risk/customer/<id>/ 单客户风险详情
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- POST /api/v1/ai/risk/scan/ 立即扫描并按规则发预警
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"""
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from asgiref.sync import sync_to_async
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from adrf.decorators import api_view
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from rest_framework import status
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from rest_framework.response import Response
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from rest_framework.exceptions import ValidationError, NotFound
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from apps.core.viewset import resolve_tenant
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from . import risk as ai_risk
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from . import llm
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async def _tenant(request):
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code = request.META.get("HTTP_X_TENANT_ID", "")
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tenant = await sync_to_async(resolve_tenant)(code)
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if tenant is None:
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raise ValidationError({"tenant": "无法识别租户"})
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return tenant
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@api_view(["GET"])
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async def risk_ranking(request):
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"""应收风险客户排行（纯统计，不依赖 LLM）。"""
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tenant = await _tenant(request)
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try:
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top_n = int(request.query_params.get("top_n", 5))
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except (TypeError, ValueError):
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top_n = 5
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min_outstanding = request.query_params.get("min_outstanding", "0")
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from decimal import Decimal
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try:
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min_val = Decimal(str(min_outstanding))
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except Exception:
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min_val = Decimal("0")
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items = await sync_to_async(ai_risk.risk_ranking)(
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tenant, top_n=top_n, min_outstanding=min_val
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)
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return Response({
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"llm_enhanced": llm.available(),
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"count": len(items),
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"results": items,
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})
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@api_view(["GET"])
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async def customer_risk(request, customer_id):
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"""单客户风险评分明细（三因子 + 加权）。"""
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tenant = await _tenant(request)
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from apps.partner.models import Customer
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customer = await sync_to_async(
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Customer.objects.filter(tenant=tenant, pk=customer_id).first
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)()
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if customer is None:
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raise NotFound("customer not found")
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data = await sync_to_async(ai_risk.score_customer)(tenant, customer)
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return Response(data)
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@api_view(["GET"])
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async def collection_advice(request, customer_id):
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"""催收建议：有 LLM KEY 时生成人话建议，否则返回统计理由。"""
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tenant = await _tenant(request)
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from apps.partner.models import Customer
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customer = await sync_to_async(
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Customer.objects.filter(tenant=tenant, pk=customer_id).first
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)()
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if customer is None:
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raise NotFound("customer not found")
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data = await sync_to_async(ai_risk.score_customer)(tenant, customer)
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factors = data["factors"]
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stat_reason = (
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f"风险分 {data['score']:.0f}（{data['level']}），未结 ¥{data['outstanding']:.2f}。"
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f"{factors['cycle_drift']['reason']}；{factors['outstanding_trend']['reason']}；"
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f"{factors['order_drop']['reason']}。"
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)
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advice = None
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if llm.available():
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prompt = (
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f"客户「{data['customer_name']}」当前应收风险情况：{stat_reason}"
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f"请给业务员一句催收行动建议。"
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)
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advice = await sync_to_async(llm.collect_advice)(prompt)
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return Response({
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"customer_id": data["customer_id"],
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"customer_name": data["customer_name"],
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"score": data["score"],
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"level": data["level"],
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"llm_enhanced": bool(advice),
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"advice": advice or stat_reason,
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"stat_reason": stat_reason,
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})
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@api_view(["POST"])
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async def run_risk_scan(request):
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"""立即执行风险扫描（按 risk_score 规则发预警通知）。"""
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tenant = await _tenant(request)
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from apps.notify.services import check_risk_score_alerts
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n = await sync_to_async(check_risk_score_alerts)(tenant)
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return Response({
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"ok": True,
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"created_count": n,
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"message": f"扫描完成，新增 {n} 条风险预警",
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}, status=status.HTTP_200_OK)
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# ============================================================
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# B2 · AI 开单
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# ============================================================
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@api_view(["POST"])
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async def parse_order(request):
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"""AI 录单：POST {text} → 抽取商品行 + 匹配档案（不落库，前端确认后建单）。
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无 LLM KEY 时返回 400 + code=llm_unavailable（明确报错，不是 500）。
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配额超限返回 403 + code=quota_exceeded（引导升级）。
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"""
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tenant = await _tenant(request)
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from . import orders as ai_orders
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from . import usage as ai_usage
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payload = request.data or {}
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text = (payload.get("text") or "").strip()
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if not text:
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raise ValidationError({"detail": "text 必填"})
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# 配额超限由全局处理器映射为 403 + 升级引导
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await sync_to_async(ai_usage.check_quota)(tenant, ai_usage.KIND_PARSE_ORDER)
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try:
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items = await sync_to_async(ai_orders.extract_items)(
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text, allow_rule_fallback=bool(payload.get("allow_rule_fallback"))
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)
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except ai_orders.LlmUnavailable as exc:
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return Response({
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"code": "llm_unavailable",
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"detail": str(exc),
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}, status=status.HTTP_400_BAD_REQUEST)
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if not items:
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return Response({"code": "empty", "detail": "未能从文本中识别出商品行",
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"matched": [], "unmatched": []},
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status=status.HTTP_200_OK)
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def _resolve_unit(tenant_, product, item):
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"""把抽取到的单位名映射到商品单位 + 取价（复用 A1 取价口径）。"""
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from apps.catalog.models import UnitConversion
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out = {"unit_id": None, "unit_name": "", "price": str(product.sale_price)}
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want = (item.get("unit") or "").strip()
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base = product.base_unit
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if base is not None and want and (want == base.name or want == base.code):
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out.update({"unit_id": base.id, "unit_name": base.name})
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return out
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conv = None
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if want:
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conv = (
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UnitConversion.objects.filter(product=product)
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.select_related("unit")
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.filter(unit__name=want).first()
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or UnitConversion.objects.filter(product=product)
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.select_related("unit")
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.filter(unit__code=want).first()
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)
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if conv is not None:
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out.update({
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"unit_id": conv.unit_id,
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"unit_name": conv.unit.name,
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"price": str(product.sale_price * conv.rate),
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})
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return out
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result = await sync_to_async(ai_orders.match_products)(
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tenant, items, unit_resolver=_resolve_unit
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)
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await sync_to_async(ai_usage.record_usage)(
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tenant, ai_usage.KIND_PARSE_ORDER,
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detail={"items": len(items), "matched": len(result["matched"])},
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)
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result["llm_enhanced"] = True
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result["usage"] = await sync_to_async(ai_usage.monthly_count)(
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tenant, ai_usage.KIND_PARSE_ORDER
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)
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return Response(result)
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@api_view(["GET"])
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async def ai_usage_summary(request):
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"""AI 用量总览（前端显示"免费版 10 次/月"进度）。"""
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tenant = await _tenant(request)
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from . import usage as ai_usage
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data = await sync_to_async(ai_usage.usage_summary)(tenant)
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data["llm_available"] = llm.available()
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return Response(data)
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# ============================================================
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# B3 · AI 经营问答
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# ============================================================
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@api_view(["POST"])
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async def ask(request):
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"""老板参谋：POST {question} → 白名单取数（只读）→ 组织语言作答。
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无 LLM 时仍返回结构化统计答案（llm_enhanced=False）；前端据 llm_available
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决定是否展示入口。
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"""
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tenant = await _tenant(request)
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from . import ask as ai_ask
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from . import usage as ai_usage
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payload = request.data or {}
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question = (payload.get("question") or "").strip()
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if not question:
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raise ValidationError({"detail": "question 必填"})
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# 配额超限由全局处理器映射
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await sync_to_async(ai_usage.check_quota)(tenant, ai_usage.KIND_ASK)
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data = await sync_to_async(ai_ask.answer)(tenant, question)
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await sync_to_async(ai_usage.record_usage)(
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tenant, ai_usage.KIND_ASK, detail={"intents": data["intents"]}
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)
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return Response(data)
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