rename meta-reference -> basic

llama_stack/providers/inline/scoring/basic/scoring.py

@@ -70,18 +70,18 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
         raise NotImplementedError("Register scoring function not implemented yet")
 
     async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
-        dataset_def = await self.datasets_api.get_dataset(dataset_identifier=dataset_id)
-        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
+        if not dataset_def.schema or len(dataset_def.schema) == 0:
             raise ValueError(
                 f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
             )
 
         for required_column in ["generated_answer", "expected_answer", "input_query"]:
-            if required_column not in dataset_def.dataset_schema:
+            if required_column not in dataset_def.schema:
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column."
                 )
-            if dataset_def.dataset_schema[required_column].type != "string":
+            if dataset_def.schema[required_column].type != "string":
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
                 )

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -63,18 +63,19 @@ class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
         )
 
     async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
-        dataset_def = await self.datasets_api.get_dataset(dataset_identifier=dataset_id)
-        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
+        print(dataset_def)
+        if not dataset_def.schema or len(dataset_def.schema) == 0:
             raise ValueError(
                 f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
             )
 
         for required_column in ["generated_answer", "expected_answer", "input_query"]:
-            if required_column not in dataset_def.dataset_schema:
+            if required_column not in dataset_def.schema:
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column."
                 )
-            if dataset_def.dataset_schema[required_column].type != "string":
+            if dataset_def.schema[required_column].type != "string":
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
                 )

llama_stack/providers/registry/scoring.py

@@ -15,8 +15,8 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.scoring,
             provider_type="meta-reference",
             pip_packages=[],
-            module="llama_stack.providers.inline.scoring.meta_reference",
-            config_class="llama_stack.providers.inline.scoring.meta_reference.MetaReferenceScoringConfig",
+            module="llama_stack.providers.inline.scoring.basic",
+            config_class="llama_stack.providers.inline.scoring.basic.MetaReferenceScoringConfig",
             api_dependencies=[
                 Api.datasetio,
                 Api.datasets,