forked from phoenix-oss/llama-stack-mirror
9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
103 lines
3.2 KiB
Python
103 lines
3.2 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
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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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function = "function"
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language = "language"
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id = "id"
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ground_truth = "ground_truth"
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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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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.function.value: StringType(),
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ColumnName.language.value: StringType(),
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ColumnName.id.value: StringType(),
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ColumnName.ground_truth.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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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.function.value: StringType(),
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ColumnName.language.value: StringType(),
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ColumnName.id.value: StringType(),
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ColumnName.ground_truth.value: StringType(),
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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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