fix: 后端 API 修复

- deps.py: 依赖注入优化
- matches.py: 比赛列表查询修复
- predict.py: 预测接口参数校验修复
- orchestrator.py: 多 Agent 编排逻辑修复

src/api/deps.py

@@ -187,8 +187,13 @@ async def rate_limit_predict(request: Request) -> None:
     client_ip = get_client_ip(request)
 
     if not _predict_limiter.is_allowed(client_ip):
-        logger.warning("rate limit exceeded for %s", client_ip)
+        logger.warning("rate limit exceeded for %s", ip)
         raise HTTPException(
             status_code=429,
             detail="请求过于频繁,请稍后再试(每分钟最多 10 次)",
         )
+
+
+def get_predict_rate_limit_remaining(request: Request) -> int:
+    """查询当前 IP 剩余的预测配额(用于响应中提示前端)。"""
+    return _predict_limiter.remaining(get_client_ip(request))

src/api/routes/matches.py

@@ -72,7 +72,13 @@ async def list_matches(
             d = datetime.strptime(date, "%Y-%m-%d")
         except ValueError:
             raise HTTPException(400, "date 格式应为 YYYY-MM-DD")
-        q = q.where(Match.match_date >= d, Match.match_date < d + timedelta(days=1))
+        # date 是用户本地日期(默认北京 UTC+8);match_date 存 UTC,需转换:
+        # 本地 00:00 (UTC+8) = UTC 前一天 16:00;本地 24:00 = UTC 当天 16:00
+        from datetime import timezone as tz_mod
+        tz_cn = tz_mod(timedelta(hours=8))
+        local_start = d.replace(tzinfo=tz_cn)
+        local_end = local_start + timedelta(days=1)
+        q = q.where(Match.match_date >= local_start, Match.match_date < local_end)
 
     # 未开赛按日期正序(最近的排最前,便于预测);其余按日期倒序(最新赛果在前)
     if status == "scheduled":

src/api/routes/predict.py

@@ -8,11 +8,11 @@ from __future__ import annotations
 
 import logging
 
-from fastapi import APIRouter, Depends, HTTPException, Query
+from fastapi import APIRouter, Depends, HTTPException, Query, Request
 from sqlalchemy import select
 from sqlalchemy.orm import selectinload
 
-from src.api.deps import rate_limit_predict, require_admin
+from src.api.deps import get_predict_rate_limit_remaining, rate_limit_predict, require_admin
 from src.api.schemas import PredictOut, PredictRequest, PredictionOut
 from src.db.base import AsyncSession, get_db_read, short_read
 from src.db.models import Match, Prediction
@@ -24,8 +24,8 @@ router = APIRouter(prefix="/api/v1", tags=["predict"])
 
 
 @router.post("/predict", response_model=PredictOut, dependencies=[Depends(rate_limit_predict)])
-async def predict(req: PredictRequest):
+async def predict(req: PredictRequest, request: Request):
-    """对一场比赛调 LLM 预测。mode=multi(默认,5专家+终裁)或 single。
+    """对一场比赛调 LLM 预测。mode=multi(默认,5专家+终裁)、single 或 baseline。
 
     公开接口,仅做限流保护(不要求登录)。
 
@@ -100,7 +100,7 @@ async def predict(req: PredictRequest):
         latency_ms=result.get("latency_ms", 0) if result_dict else result.latency_ms,
         prompt_tokens=result.get("prompt_tokens") if result_dict else getattr(result, "prompt_tokens", None),
         completion_tokens=result.get("completion_tokens") if result_dict else getattr(result, "completion_tokens", None),
-        rate_limit_remaining=_predict_limiter.remaining(get_client_ip(request)),
+        rate_limit_remaining=get_predict_rate_limit_remaining(request),
     )
 
 

src/llm/agents/orchestrator.py

@@ -95,10 +95,10 @@ class MultiPredictResult:
     pred_1x2: str | None
     subjective_confidence: float | None
     reasoning: str | None
+    context: str
     agent_outputs: list[dict]
     agent_weights: dict | None
     status: str = "success"
-    context: str
     latency_ms: int | None = None
     prompt_tokens: int | None = None
     completion_tokens: int | None = None