feat: add metric_weights and doc_weights to Scenario schema and dataclass

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

rag_eval/config/loader.py

@@ -62,6 +62,8 @@ def load_scenario(path: str | Path) -> Scenario:
         ),
         source_path=scenario_path,
         optimization_advisor=model.optimization_advisor,
+        metric_weights=dict(model.metric_weights),
+        doc_weights=dict(model.doc_weights),
     )
     # Run cross-field checks after all relative paths have been resolved.
     validate_scenario(scenario)

rag_eval/config/schema.py

@@ -55,6 +55,8 @@ class ScenarioModel(BaseModel):
     output_dir: str
     runtime: RuntimeConfigModel = Field(default_factory=RuntimeConfigModel)
     optimization_advisor: bool = False
+    metric_weights: dict[str, float] = Field(default_factory=dict)
+    doc_weights: dict[str, float] = Field(default_factory=dict)
 
     @field_validator("metrics")
     @classmethod

rag_eval/shared/models.py

@@ -77,6 +77,8 @@ class Scenario:
     app_adapter: AppAdapterConfig | None = None
     source_path: Path | None = None
     optimization_advisor: bool = False
+    metric_weights: dict[str, float] = field(default_factory=dict)
+    doc_weights: dict[str, float] = field(default_factory=dict)
 
     def snapshot(self) -> dict[str, Any]:
         """Serialize the scenario into a reporting-friendly dictionary snapshot."""

tests/test_offline_eval.py

@@ -80,6 +80,64 @@ class ScenarioAndDatasetTests(unittest.TestCase):
         self.assertTrue(scenario.dataset.path.name.endswith(".csv"))
         self.assertTrue(scenario.output_dir.name == "sample-offline-baseline")
 
+    def test_load_scenario_metric_and_doc_weights(self) -> None:
+        """load_scenario passes metric_weights and doc_weights into Scenario."""
+        import os
+        import tempfile
+
+        import yaml
+
+        from rag_eval.config.loader import load_scenario
+
+        payload = {
+            "scenario_name": "w-test",
+            "mode": "offline",
+            "dataset": "nonexistent.csv",
+            "judge_model": "m",
+            "embedding_model": "e",
+            "metrics": ["faithfulness"],
+            "output_dir": "out",
+            "metric_weights": {"faithfulness": 0.7},
+            "doc_weights": {"doc.pdf": 2.0},
+        }
+        with tempfile.NamedTemporaryFile(suffix=".yaml", mode="w", encoding="utf-8", delete=False) as f:
+            yaml.dump(payload, f, allow_unicode=True)
+            tmp_path = f.name
+        try:
+            scenario = load_scenario(tmp_path)
+            assert scenario.metric_weights == {"faithfulness": 0.7}
+            assert scenario.doc_weights == {"doc.pdf": 2.0}
+        finally:
+            os.unlink(tmp_path)
+
+    def test_load_scenario_defaults_to_empty_weights(self) -> None:
+        """load_scenario defaults metric_weights and doc_weights to empty dicts."""
+        import os
+        import tempfile
+
+        import yaml
+
+        from rag_eval.config.loader import load_scenario
+
+        payload = {
+            "scenario_name": "no-w",
+            "mode": "offline",
+            "dataset": "nonexistent.csv",
+            "judge_model": "m",
+            "embedding_model": "e",
+            "metrics": ["faithfulness"],
+            "output_dir": "out",
+        }
+        with tempfile.NamedTemporaryFile(suffix=".yaml", mode="w", encoding="utf-8", delete=False) as f:
+            yaml.dump(payload, f, allow_unicode=True)
+            tmp_path = f.name
+        try:
+            scenario = load_scenario(tmp_path)
+            assert scenario.metric_weights == {}
+            assert scenario.doc_weights == {}
+        finally:
+            os.unlink(tmp_path)
+
     def test_scenario_snapshot_serializes_path_static_kwargs(self) -> None:
         scenario = load_scenario("scenarios/online/sample-pdf-question-bank-online.yaml")
         snapshot = scenario.snapshot()