baseline: 批次A-D 成果 + membership 半成品（测试红）

2026-09-11 23:11:35 +08:00
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+"""AI 应用路由：应收风险 + 配额。
+
+- GET  /api/v1/ai/risk/ranking/?top_n=&min_outstanding=  风险客户排行
+- GET  /api/v1/ai/risk/customer/<id>/                    单客户风险详情
+- POST /api/v1/ai/risk/scan/                             立即扫描并按规则发预警
+"""
+
+from asgiref.sync import sync_to_async
+from adrf.decorators import api_view
+from rest_framework import status
+from rest_framework.response import Response
+from rest_framework.exceptions import ValidationError, NotFound
+
+from apps.core.viewset import resolve_tenant
+from . import risk as ai_risk
+from . import llm
+
+
+async def _tenant(request):
+    code = request.META.get("HTTP_X_TENANT_ID", "")
+    tenant = await sync_to_async(resolve_tenant)(code)
+    if tenant is None:
+        raise ValidationError({"tenant": "无法识别租户"})
+    return tenant
+
+
+@api_view(["GET"])
+async def risk_ranking(request):
+    """应收风险客户排行（纯统计，不依赖 LLM）。"""
+    tenant = await _tenant(request)
+    try:
+        top_n = int(request.query_params.get("top_n", 5))
+    except (TypeError, ValueError):
+        top_n = 5
+    min_outstanding = request.query_params.get("min_outstanding", "0")
+
+    from decimal import Decimal
+    try:
+        min_val = Decimal(str(min_outstanding))
+    except Exception:
+        min_val = Decimal("0")
+
+    items = await sync_to_async(ai_risk.risk_ranking)(
+        tenant, top_n=top_n, min_outstanding=min_val
+    )
+    return Response({
+        "llm_enhanced": llm.available(),
+        "count": len(items),
+        "results": items,
+    })
+
+
+@api_view(["GET"])
+async def customer_risk(request, customer_id):
+    """单客户风险评分明细（三因子 + 加权）。"""
+    tenant = await _tenant(request)
+    from apps.partner.models import Customer
+
+    customer = await sync_to_async(
+        Customer.objects.filter(tenant=tenant, pk=customer_id).first
+    )()
+    if customer is None:
+        raise NotFound("customer not found")
+
+    data = await sync_to_async(ai_risk.score_customer)(tenant, customer)
+    return Response(data)
+
+
+@api_view(["GET"])
+async def collection_advice(request, customer_id):
+    """催收建议：有 LLM KEY 时生成人话建议，否则返回统计理由。"""
+    tenant = await _tenant(request)
+    from apps.partner.models import Customer
+
+    customer = await sync_to_async(
+        Customer.objects.filter(tenant=tenant, pk=customer_id).first
+    )()
+    if customer is None:
+        raise NotFound("customer not found")
+
+    data = await sync_to_async(ai_risk.score_customer)(tenant, customer)
+    factors = data["factors"]
+    stat_reason = (
+        f"风险分 {data['score']:.0f}（{data['level']}），未结 ¥{data['outstanding']:.2f}。"
+        f"{factors['cycle_drift']['reason']}；{factors['outstanding_trend']['reason']}；"
+        f"{factors['order_drop']['reason']}。"
+    )
+
+    advice = None
+    if llm.available():
+        prompt = (
+            f"客户「{data['customer_name']}」当前应收风险情况：{stat_reason}"
+            f"请给业务员一句催收行动建议。"
+        )
+        advice = await sync_to_async(llm.collect_advice)(prompt)
+
+    return Response({
+        "customer_id": data["customer_id"],
+        "customer_name": data["customer_name"],
+        "score": data["score"],
+        "level": data["level"],
+        "llm_enhanced": bool(advice),
+        "advice": advice or stat_reason,
+        "stat_reason": stat_reason,
+    })
+
+
+@api_view(["POST"])
+async def run_risk_scan(request):
+    """立即执行风险扫描（按 risk_score 规则发预警通知）。"""
+    tenant = await _tenant(request)
+    from apps.notify.services import check_risk_score_alerts
+
+    n = await sync_to_async(check_risk_score_alerts)(tenant)
+    return Response({
+        "ok": True,
+        "created_count": n,
+        "message": f"扫描完成，新增 {n} 条风险预警",
+    }, status=status.HTTP_200_OK)
+
+
+# ============================================================
+# B2 · AI 开单
+# ============================================================
+
+@api_view(["POST"])
+async def parse_order(request):
+    """AI 录单：POST {text} → 抽取商品行 + 匹配档案（不落库，前端确认后建单）。
+
+    无 LLM KEY 时返回 400 + code=llm_unavailable（明确报错，不是 500）。
+    配额超限返回 403 + code=quota_exceeded（引导升级）。
+    """
+    tenant = await _tenant(request)
+    from . import orders as ai_orders
+    from . import usage as ai_usage
+
+    payload = request.data or {}
+    text = (payload.get("text") or "").strip()
+    if not text:
+        raise ValidationError({"detail": "text 必填"})
+
+    # 配额超限由全局处理器映射为 403 + 升级引导
+    await sync_to_async(ai_usage.check_quota)(tenant, ai_usage.KIND_PARSE_ORDER)
+
+    try:
+        items = await sync_to_async(ai_orders.extract_items)(
+            text, allow_rule_fallback=bool(payload.get("allow_rule_fallback"))
+        )
+    except ai_orders.LlmUnavailable as exc:
+        return Response({
+            "code": "llm_unavailable",
+            "detail": str(exc),
+        }, status=status.HTTP_400_BAD_REQUEST)
+
+    if not items:
+        return Response({"code": "empty", "detail": "未能从文本中识别出商品行",
+                         "matched": [], "unmatched": []},
+                        status=status.HTTP_200_OK)
+
+    def _resolve_unit(tenant_, product, item):
+        """把抽取到的单位名映射到商品单位 + 取价（复用 A1 取价口径）。"""
+        from apps.catalog.models import UnitConversion
+
+        out = {"unit_id": None, "unit_name": "", "price": str(product.sale_price)}
+        want = (item.get("unit") or "").strip()
+        base = product.base_unit
+        if base is not None and want and (want == base.name or want == base.code):
+            out.update({"unit_id": base.id, "unit_name": base.name})
+            return out
+        conv = None
+        if want:
+            conv = (
+                UnitConversion.objects.filter(product=product)
+                .select_related("unit")
+                .filter(unit__name=want).first()
+                or UnitConversion.objects.filter(product=product)
+                .select_related("unit")
+                .filter(unit__code=want).first()
+            )
+        if conv is not None:
+            out.update({
+                "unit_id": conv.unit_id,
+                "unit_name": conv.unit.name,
+                "price": str(product.sale_price * conv.rate),
+            })
+        return out
+
+    result = await sync_to_async(ai_orders.match_products)(
+        tenant, items, unit_resolver=_resolve_unit
+    )
+    await sync_to_async(ai_usage.record_usage)(
+        tenant, ai_usage.KIND_PARSE_ORDER,
+        detail={"items": len(items), "matched": len(result["matched"])},
+    )
+    result["llm_enhanced"] = True
+    result["usage"] = await sync_to_async(ai_usage.monthly_count)(
+        tenant, ai_usage.KIND_PARSE_ORDER
+    )
+    return Response(result)
+
+
+@api_view(["GET"])
+async def ai_usage_summary(request):
+    """AI 用量总览（前端显示"免费版 10 次/月"进度）。"""
+    tenant = await _tenant(request)
+    from . import usage as ai_usage
+
+    data = await sync_to_async(ai_usage.usage_summary)(tenant)
+    data["llm_available"] = llm.available()
+    return Response(data)
+
+
+# ============================================================
+# B3 · AI 经营问答
+# ============================================================
+
+@api_view(["POST"])
+async def ask(request):
+    """老板参谋：POST {question} → 白名单取数（只读）→ 组织语言作答。
+
+    无 LLM 时仍返回结构化统计答案（llm_enhanced=False）；前端据 llm_available
+    决定是否展示入口。
+    """
+    tenant = await _tenant(request)
+    from . import ask as ai_ask
+    from . import usage as ai_usage
+
+    payload = request.data or {}
+    question = (payload.get("question") or "").strip()
+    if not question:
+        raise ValidationError({"detail": "question 必填"})
+
+    # 配额超限由全局处理器映射
+    await sync_to_async(ai_usage.check_quota)(tenant, ai_usage.KIND_ASK)
+
+    data = await sync_to_async(ai_ask.answer)(tenant, question)
+    await sync_to_async(ai_usage.record_usage)(
+        tenant, ai_usage.KIND_ASK, detail={"intents": data["intents"]}
+    )
+    return Response(data)