change schema -> dataset_schema (#442)

# What does this PR do?

- `schema` should not a field w/ pydantic warnings
- change `schema` to `dataset_schema`

<img width="855" alt="image"
src="https://github.com/user-attachments/assets/47cb6bb9-4be0-46a5-8701-24d24e2eaabd">


## Test Plan

```
pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio eval/test_eval.py
```


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

llama_stack/apis/datasets/datasets.py

@@ -17,7 +17,7 @@ from llama_stack.apis.resource import Resource, ResourceType
 
 
 class CommonDatasetFields(BaseModel):
-    schema: Dict[str, ParamType]
+    dataset_schema: Dict[str, ParamType]
     url: URL
     metadata: Dict[str, Any] = Field(
         default_factory=dict,

llama_stack/distribution/routers/routing_tables.py

@@ -332,7 +332,7 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
             identifier=dataset_id,
             provider_resource_id=provider_dataset_id,
             provider_id=provider_id,
-            schema=schema,
+            dataset_schema=schema,
             url=url,
             metadata=metadata,
         )

llama_stack/providers/inline/datasetio/localfs/datasetio.py

@@ -60,9 +60,9 @@ class PandasDataframeDataset(BaseDataset):
 
     def _validate_dataset_schema(self, df) -> pandas.DataFrame:
         # note that we will drop any columns in dataset that are not in the schema
-        df = df[self.dataset_def.schema.keys()]
+        df = df[self.dataset_def.dataset_schema.keys()]
         # check all columns in dataset schema are present
-        assert len(df.columns) == len(self.dataset_def.schema)
+        assert len(df.columns) == len(self.dataset_def.dataset_schema)
         # TODO: type checking against column types in dataset schema
         return df
 

llama_stack/providers/inline/eval/meta_reference/eval.py

@@ -58,7 +58,7 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
 
     async def validate_eval_input_dataset_schema(self, dataset_id: str) -> None:
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        if not dataset_def.schema or len(dataset_def.schema) == 0:
+        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
             raise ValueError(f"Dataset {dataset_id} does not have a schema defined.")
 
         expected_schemas = [
@@ -74,7 +74,7 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
             },
         ]
 
-        if dataset_def.schema not in expected_schemas:
+        if dataset_def.dataset_schema not in expected_schemas:
             raise ValueError(
                 f"Dataset {dataset_id} does not have a correct input schema in {expected_schemas}"
             )

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

@@ -60,17 +60,17 @@ class BasicScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
 
     async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        if not dataset_def.schema or len(dataset_def.schema) == 0:
+        if not dataset_def.dataset_schema or len(dataset_def.dataset_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.schema:
+            if required_column not in dataset_def.dataset_schema:
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column."
                 )
-            if dataset_def.schema[required_column].type != "string":
+            if dataset_def.dataset_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

@@ -64,17 +64,17 @@ 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_id=dataset_id)
-        if not dataset_def.schema or len(dataset_def.schema) == 0:
+        if not dataset_def.dataset_schema or len(dataset_def.dataset_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.schema:
+            if required_column not in dataset_def.dataset_schema:
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column."
                 )
-            if dataset_def.schema[required_column].type != "string":
+            if dataset_def.dataset_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/llm_as_judge/scoring.py

@@ -67,17 +67,17 @@ class LlmAsJudgeScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
 
     async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        if not dataset_def.schema or len(dataset_def.schema) == 0:
+        if not dataset_def.dataset_schema or len(dataset_def.dataset_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.schema:
+            if required_column not in dataset_def.dataset_schema:
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column."
                 )
-            if dataset_def.schema[required_column].type != "string":
+            if dataset_def.dataset_schema[required_column].type != "string":
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
                 )