forked from phoenix-oss/llama-stack-mirror
34ab7a3b6c
Lint check in main branch is failing. This fixes the lint check after we moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We need to move to a `ruff.toml` file as well as fixing and ignoring some additional checks. Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
82 lines
2.4 KiB
Python
82 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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dialog = "dialog"
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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(f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}")
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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(f"Input row {input_row} does not match any of the expected schemas in {expected_schemas}")
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