forked from phoenix-oss/llama-stack-mirror
3d9331840e
# What does this PR do? - add some docs to OpenAPI for agents/eval/scoring/datasetio [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan - read [//]: # (## Documentation)
78 lines
2.2 KiB
Python
78 lines
2.2 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 typing import Any, Dict, List, Optional, Protocol, runtime_checkable
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from pydantic import BaseModel
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from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
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from llama_stack.schema_utils import json_schema_type, webmethod
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# mapping of metric to value
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ScoringResultRow = Dict[str, Any]
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@json_schema_type
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class ScoringResult(BaseModel):
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"""
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A scoring result for a single row.
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:param score_rows: The scoring result for each row. Each row is a map of column name to value.
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:param aggregated_results: Map of metric name to aggregated value
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"""
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score_rows: List[ScoringResultRow]
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# aggregated metrics to value
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aggregated_results: Dict[str, Any]
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@json_schema_type
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class ScoreBatchResponse(BaseModel):
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dataset_id: Optional[str] = None
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results: Dict[str, ScoringResult]
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@json_schema_type
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class ScoreResponse(BaseModel):
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"""
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The response from scoring.
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:param results: A map of scoring function name to ScoringResult.
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"""
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# each key in the dict is a scoring function name
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results: Dict[str, ScoringResult]
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class ScoringFunctionStore(Protocol):
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def get_scoring_function(self, scoring_fn_id: str) -> ScoringFn: ...
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@runtime_checkable
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class Scoring(Protocol):
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scoring_function_store: ScoringFunctionStore
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@webmethod(route="/scoring/score-batch", method="POST")
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async def score_batch(
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self,
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dataset_id: str,
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scoring_functions: Dict[str, Optional[ScoringFnParams]],
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save_results_dataset: bool = False,
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) -> ScoreBatchResponse: ...
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@webmethod(route="/scoring/score", method="POST")
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async def score(
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self,
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input_rows: List[Dict[str, Any]],
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scoring_functions: Dict[str, Optional[ScoringFnParams]],
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) -> ScoreResponse:
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"""Score a list of rows.
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:param input_rows: The rows to score.
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:param scoring_functions: The scoring functions to use for the scoring.
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:return: ScoreResponse object containing rows and aggregated results
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"""
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...
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