forked from phoenix-oss/llama-stack-mirror
7a90fc5854
# What does this PR do? - there's no value in keeping data schema validation logic in a DataSchemaValidatorMixin - move into data schema validation logic into standalone utils ## Test Plan ``` pytest -v -s -m llm_as_judge_scoring_together_inference scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct pytest -v -s -m basic_scoring_together_inference scoring/test_scoring.py pytest -v -s -m braintrust_scoring_together_inference scoring/test_scoring.py pytest -v -s -m meta_reference_eval_together_inference eval/test_eval.py 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.
85 lines
2.4 KiB
Python
85 lines
2.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from enum import Enum
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from typing import Any, Dict, List
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from llama_stack.apis.common.type_system import (
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ChatCompletionInputType,
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CompletionInputType,
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StringType,
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)
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from llama_stack.distribution.datatypes import Api
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class ColumnName(Enum):
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input_query = "input_query"
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expected_answer = "expected_answer"
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chat_completion_input = "chat_completion_input"
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completion_input = "completion_input"
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generated_answer = "generated_answer"
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context = "context"
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VALID_SCHEMAS_FOR_SCORING = [
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.generated_answer.value: StringType(),
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},
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.generated_answer.value: StringType(),
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ColumnName.context.value: StringType(),
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},
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]
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VALID_SCHEMAS_FOR_EVAL = [
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.chat_completion_input.value: ChatCompletionInputType(),
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},
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{
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ColumnName.input_query.value: StringType(),
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ColumnName.expected_answer.value: StringType(),
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ColumnName.completion_input.value: CompletionInputType(),
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},
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]
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def get_valid_schemas(api_str: str):
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if api_str == Api.scoring.value:
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return VALID_SCHEMAS_FOR_SCORING
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elif api_str == Api.eval.value:
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return VALID_SCHEMAS_FOR_EVAL
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else:
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raise ValueError(f"Invalid API string: {api_str}")
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def validate_dataset_schema(
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dataset_schema: Dict[str, Any],
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expected_schemas: List[Dict[str, Any]],
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):
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if dataset_schema not in expected_schemas:
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raise ValueError(
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f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}"
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)
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def validate_row_schema(
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input_row: Dict[str, Any],
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expected_schemas: List[Dict[str, Any]],
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):
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for schema in expected_schemas:
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if all(key in input_row for key in schema):
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return
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raise ValueError(
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f"Input row {input_row} does not match any of the expected schemas in {expected_schemas}"
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)
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