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change schema -> dataset_schema

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This commit is contained in:
Xi Yan 2024-11-13 10:52:16 -05:00
parent c29fa56dde
commit d1f758abf6
7 changed files with 15 additions and 15 deletions
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6
llama_stack/providers/inline/scoring/basic/scoring.py
View file

@ -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'."
)

6
llama_stack/providers/inline/scoring/braintrust/braintrust.py
View file

@ -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'."
)

6
llama_stack/providers/inline/scoring/llm_as_judge/scoring.py
View file

@ -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'."
)