forked from phoenix-oss/llama-stack-mirror
change schema -> dataset_schema for register_dataset api (#443)
# What does this PR do? - API updates: change schema to dataset_schema for register_dataset for resolving pydantic naming conflict - Note: this OpenAPI update will be synced with llama-stack-client-python SDK. cc @dineshyv ## Test Plan ``` pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio eval/test_eval.py ``` ## Sources Please link relevant resources if necessary. ## 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.
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d5b1202c83
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6 changed files with 90 additions and 90 deletions
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@ -49,7 +49,7 @@ class Datasets(Protocol):
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async def register_dataset(
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self,
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dataset_id: str,
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schema: Dict[str, ParamType],
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dataset_schema: Dict[str, ParamType],
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url: URL,
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provider_dataset_id: Optional[str] = None,
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provider_id: Optional[str] = None,
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@ -310,7 +310,7 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
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async def register_dataset(
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self,
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dataset_id: str,
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schema: Dict[str, ParamType],
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dataset_schema: Dict[str, ParamType],
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url: URL,
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provider_dataset_id: Optional[str] = None,
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provider_id: Optional[str] = None,
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@ -332,7 +332,7 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
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identifier=dataset_id,
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provider_resource_id=provider_dataset_id,
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provider_id=provider_id,
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dataset_schema=schema,
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dataset_schema=dataset_schema,
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url=url,
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metadata=metadata,
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)
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@ -57,7 +57,7 @@ async def register_dataset(
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await datasets_impl.register_dataset(
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dataset_id=dataset_id,
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schema=dataset_schema,
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dataset_schema=dataset_schema,
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url=URL(uri=test_url),
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)
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@ -163,7 +163,7 @@ class Testeval:
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await datasets_impl.register_dataset(
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dataset_id="mmlu",
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schema={
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dataset_schema={
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"input_query": StringType(),
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"expected_answer": StringType(),
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"chat_completion_input": ChatCompletionInputType(),
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