feat: ScenarioInfo exposes metric_weights and doc_weights from YAML

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

webapp/models.py

@@ -74,6 +74,18 @@ class ReportData(BaseModel):
     lowest_samples: list[SampleScore] = Field(default_factory=list)
     summary_markdown: str = ""
     advice_markdown: str = ""  # optimization_advice.md content (empty if not generated)
+    weighted_score_mean: float | None = Field(
+        default=None,
+        description="加权综合得分均值（metric_weights × doc_weights 共同作用）。",
+    )
+    metric_weights: dict[str, float] = Field(
+        default_factory=dict,
+        description="该次运行使用的指标权重配置（来自 scenario.snapshot.yaml）。",
+    )
+    doc_weights: dict[str, float] = Field(
+        default_factory=dict,
+        description="该次运行使用的文档权重配置（来自 scenario.snapshot.yaml）。",
+    )
 
 
 class RunDetail(BaseModel):
@@ -93,6 +105,14 @@ class ScenarioInfo(BaseModel):
     judge_model: str = ""
     metrics: list[str] = Field(default_factory=list)
     error: str = ""
+    metric_weights: dict[str, float] = Field(
+        default_factory=dict,
+        description="从场景 YAML 读取的指标权重配置，供前端权重面板预填。",
+    )
+    doc_weights: dict[str, float] = Field(
+        default_factory=dict,
+        description="从场景 YAML 读取的文档权重配置，供前端权重面板预填。",
+    )
 
 
 class TaskStatus(BaseModel):

webapp/services/scenario_scanner.py

@@ -37,6 +37,16 @@ def _summarize_scenario(path: Path) -> ScenarioInfo:
 
     metrics = payload.get("metrics")
     metric_list = [str(item) for item in metrics] if isinstance(metrics, list) else []
+    raw_metric_weights = payload.get("metric_weights") or {}
+    raw_doc_weights = payload.get("doc_weights") or {}
+    metric_weights = {
+        str(k): float(v) for k, v in raw_metric_weights.items()
+        if isinstance(v, (int, float))
+    }
+    doc_weights = {
+        str(k): float(v) for k, v in raw_doc_weights.items()
+        if isinstance(v, (int, float))
+    }
 
     return ScenarioInfo(
         path=relative,
@@ -45,6 +55,8 @@ def _summarize_scenario(path: Path) -> ScenarioInfo:
         dataset=str(payload.get("dataset", "")),
         judge_model=str(payload.get("judge_model", "")),
         metrics=metric_list,
+        metric_weights=metric_weights,
+        doc_weights=doc_weights,
     )