feat: new file

backend/app/routes/__init__.py

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+

backend/app/routes/ai.py

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+from __future__ import annotations
+
+from fastapi import APIRouter
+from pydantic import BaseModel, Field
+
+from ..services.ai_insight import generate_insight
+
+
+router = APIRouter()
+
+
+class InsightRequest(BaseModel):
+    query: str = Field(..., min_length=1, max_length=2000)
+    product_id: str | None = None
+    top_k: int = Field(6, ge=1, le=20)
+
+
+@router.post("/insight")
+def insight(req: InsightRequest):
+    """
+    基于向量检索 + LLM（可选）输出“爆款发现 -> 数据验证 -> 决策跟卖”的建议。
+    """
+    return generate_insight(query=req.query, product_id=req.product_id, top_k=req.top_k)
+

backend/app/routes/debug.py

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+from __future__ import annotations
+
+from fastapi import APIRouter, Query
+
+from ..services.db_sample import print_db_sample_to_logs
+
+
+router = APIRouter()
+
+
+@router.post("/db-sample")
+def db_sample(limit: int = Query(10, ge=1, le=200)):
+    """
+    触发一次“数据库样例打印到后端日志”，用于快速理解数据内容与结构。
+    """
+    print_db_sample_to_logs(limit=limit)
+    return {"ok": True, "printed": True, "limit": limit}
+

backend/app/routes/metrics.py

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+from __future__ import annotations
+
+from datetime import datetime, timedelta
+
+import pandas as pd
+from fastapi import APIRouter, HTTPException, Query
+from sqlalchemy import text
+
+from ..db import get_engine
+from ..services.schema_discovery import discover_schema
+from ..services.timeseries import normalize_timeseries
+
+
+router = APIRouter()
+
+
+@router.get("/overview")
+def overview():
+    """
+    返回 BI 顶部卡片核心指标（尽量从现有表自动推断）。
+    """
+    engine = get_engine()
+    schema = discover_schema(engine)
+    if not schema.sales_table:
+        raise HTTPException(status_code=422, detail="未发现可用销量/订单明细表（需要至少包含 product_id + 时间 + 数量/金额）")
+
+    q = text(schema.overview_sql)
+    with engine.connect() as conn:
+        row = conn.execute(q).mappings().first()
+    return {"schema": schema.model_dump(), "metrics": dict(row) if row else {}}
+
+
+@router.get("/sales/timeseries")
+def sales_timeseries(
+    product_id: str = Query(..., min_length=1),
+    days: int = Query(30, ge=1, le=365),
+):
+    engine = get_engine()
+    schema = discover_schema(engine)
+    if not schema.sales_table:
+        raise HTTPException(status_code=422, detail="未发现可用销量/订单明细表")
+
+    since = datetime.utcnow() - timedelta(days=days)
+    q = text(schema.timeseries_sql)
+    with engine.connect() as conn:
+        df = pd.read_sql(
+            q,
+            conn,
+            params={"product_id": product_id, "since": since},
+        )
+    if df.empty:
+        return {"product_id": product_id, "points": []}
+
+    points = normalize_timeseries(df, ts_col="ds", value_cols=["units", "gmv"])
+    return {"product_id": product_id, "points": points}
+

backend/app/routes/trend.py

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+from __future__ import annotations
+
+import math
+from datetime import datetime, timedelta
+
+import pandas as pd
+from fastapi import APIRouter, HTTPException, Query
+from sqlalchemy import text
+
+from ..db import get_engine
+from ..services.forecast import forecast_next_n
+from ..services.schema_discovery import discover_schema
+from ..services.trend_engine import compute_trend_scores
+
+
+router = APIRouter()
+
+
+@router.get("/potential-winners")
+def potential_winners(days: int = Query(14, ge=3, le=60), limit: int = Query(50, ge=1, le=200)):
+    engine = get_engine()
+    schema = discover_schema(engine)
+    if not schema.sales_table:
+        raise HTTPException(status_code=422, detail="未发现可用销量/订单明细表")
+
+    since = datetime.utcnow() - timedelta(days=days)
+    q = text(schema.trend_candidates_sql)
+    with engine.connect() as conn:
+        df = pd.read_sql(q, conn, params={"since": since, "limit": limit * 5})
+    if df.empty:
+        return {"items": []}
+
+    scored = compute_trend_scores(df)
+    scored = scored.sort_values("potential_score", ascending=False).head(limit)
+    return {"items": scored.to_dict(orient="records")}
+
+
+@router.get("/forecast")
+def forecast(
+    product_id: str = Query(..., min_length=1),
+    days: int = Query(30, ge=7, le=180),
+    horizon: int = Query(14, ge=1, le=60),
+):
+    engine = get_engine()
+    schema = discover_schema(engine)
+    if not schema.sales_table:
+        raise HTTPException(status_code=422, detail="未发现可用销量/订单明细表")
+
+    since = datetime.utcnow() - timedelta(days=days)
+    q = text(schema.timeseries_sql)
+    with engine.connect() as conn:
+        df = pd.read_sql(q, conn, params={"product_id": product_id, "since": since})
+    if df.empty:
+        return {"product_id": product_id, "forecast": []}
+
+    df = df.sort_values("ds")
+    y = df["units"].astype(float).fillna(0.0).values
+    yhat = forecast_next_n(y, n=horizon)
+    start = pd.to_datetime(df["ds"]).max()
+    out = []
+    for i, v in enumerate(yhat, start=1):
+        out.append({"ds": (start + pd.Timedelta(days=i)).to_pydatetime().isoformat(), "units_hat": float(max(0.0, v))})
+    return {"product_id": product_id, "forecast": out}
+