chore(P3): baseline 落库下沉 + MatchPredictPanel 拆分 + 多 worker/CSRF 文档

P3-2 baseline 落库从路由下沉到服务层(predict_baseline 内直接落库),
删除路由层 _persist_baseline,三种模式统一 result.prediction_id,对外 JSON 不变。

P3-1 MatchPredictPanel.PredictionPanel 拆为 OutcomePanel/AgentsPanel/ReasoningPanel
三个子组件,本文件保留 PredictModal/PredictProgress/Spinner,对外导出路径不变。

P3-3 docs 加 ⚠️ 多 worker 陷阱红字 + STRICT_SINGLE_WORKER 环境变量(启动期强制拒绝多 worker)。
P3-4 docs 新增「同站部署 vs 跨站 CSRF」节。

src/llm/baseline.py

@@ -12,7 +12,7 @@ from sqlalchemy import case, func, select
 
 from src.db.base import AsyncSession, AsyncSessionLocal
 from src.db.models import Match
-from src.llm.predict import PredictResult
+from src.llm.predict import PredictResult, _upsert_prediction
 
 logger = logging.getLogger(__name__)
 
@@ -58,19 +58,23 @@ async def predict_baseline(
 
     返回 PredictResult(D2 统一结果类型):
       provider=model="baseline", 不调用 LLM,latency_ms≈0。
-    prediction_id 为占位 0 —— baseline 不在服务层落库,
-    由路由层 _persist_baseline 落库后取得真实 id。
+
+    P3-2:baseline 落库下沉到服务层 —— 直接在服务层完成落库并回填真实
+    prediction_id,路由层不再需要特殊的 _persist_baseline,与 single/multi
+    路径统一(result.prediction_id 即可用)。对外 JSON 不变。
     """
+    from src.db.unit_of_work import get_uow
+
     async with AsyncSessionLocal() as db:
         match = await db.get(Match, match_id)
         if match is None:
             raise ValueError(f"match {match_id} not found")
 
         before = None
-        if backtest and match.match_dt:
+        if backtest and match.match_date:
             from datetime import timedelta
 
-            before = match.match_dt - timedelta(days=1)
+            before = match.match_date - timedelta(days=1)
         elif cutoff_at is not None:
             before = cutoff_at
 
@@ -93,8 +97,38 @@ async def predict_baseline(
     else:
         pred_1x2 = "X"
 
+    values = {
+        "prompt_version": "baseline_v1",
+        "prompt_tokens": 0,
+        "completion_tokens": 0,
+        "latency_ms": 0,
+        "pred_home_goals": float(pred_home),
+        "pred_away_goals": float(pred_away),
+        "pred_1x2": pred_1x2,
+        "subjective_confidence": 0.5,
+        "reasoning": (
+            f"基线估计(非投注建议): 主队主场场均进球 {home_avg:.2f} → 预测 {pred_home}; "
+            f"客队客场场均进球 {away_avg:.2f} → 预测 {pred_away}。"
+        ),
+        "raw_response": {"home_avg": round(home_avg, 2), "away_avg": round(away_avg, 2)},
+        "status": "success",
+    }
+
+    # P3-2:服务层落库,回填真实 prediction_id(与 single/multi 统一)。
+    async with get_uow() as session:
+        pred = await _upsert_prediction(
+            session,
+            match_id=match_id,
+            provider_name="baseline",
+            model="baseline",
+            mode="baseline",
+            run_type="live",
+            values=values,
+        )
+        prediction_id = pred.id
+
     return PredictResult(
-        prediction_id=0,  # 占位:真实 id 由路由层 _persist_baseline 落库后返回
+        prediction_id=prediction_id,
         provider="baseline",
         model="baseline",
         prompt_version="baseline_v1",
@@ -105,14 +139,11 @@ async def predict_baseline(
         alt_pred_away_goals=None,
         pred_1x2=pred_1x2,
         subjective_confidence=0.5,
-        reasoning=(
-            f"基线估计(非投注建议): 主队主场场均进球 {home_avg:.2f} → 预测 {pred_home}; "
-            f"客队客场场均进球 {away_avg:.2f} → 预测 {pred_away}。"
-        ),
+        reasoning=values["reasoning"],
         context="",  # baseline 不构建 LLM 上下文
         status="success",
         latency_ms=0,
         prompt_tokens=0,
         completion_tokens=0,
-        raw={"home_avg": round(home_avg, 2), "away_avg": round(away_avg, 2)},
+        raw=values["raw_response"],
     )