forked from phoenix-oss/llama-stack-mirror
0a3b3d5fb6
* fix after rebase * remove print --------- Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
60 lines
1.6 KiB
Python
60 lines
1.6 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, Protocol, runtime_checkable
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from llama_models.schema_utils import json_schema_type, webmethod
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from pydantic import BaseModel
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.scoring_functions import * # noqa: F403
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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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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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# 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")
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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]] = None,
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save_results_dataset: bool = False,
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) -> ScoreBatchResponse: ...
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@webmethod(route="/scoring/score")
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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]] = None,
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) -> ScoreResponse: ...
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