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61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
"""Evaluation operators."""
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import asyncio
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from typing import Any, List, Optional
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from dbgpt.core import Chunk
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from dbgpt.core.awel import JoinOperator
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from dbgpt.core.interface.evaluation import EvaluationMetric, EvaluationResult
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from dbgpt.core.interface.llm import LLMClient
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class RetrieverEvaluatorOperator(JoinOperator[List[EvaluationResult]]):
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"""Evaluator for retriever."""
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def __init__(
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self,
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evaluation_metrics: List[EvaluationMetric],
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llm_client: Optional[LLMClient] = None,
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**kwargs,
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):
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"""Create a new RetrieverEvaluatorOperator."""
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self.llm_client = llm_client
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self.evaluation_metrics = evaluation_metrics
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super().__init__(combine_function=self._do_evaluation, **kwargs)
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async def _do_evaluation(
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self,
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query: str,
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prediction: List[Chunk],
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contexts: List[str],
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raw_dataset: Any = None,
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) -> List[EvaluationResult]:
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"""Run evaluation.
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Args:
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query(str): The query string.
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prediction(List[Chunk]): The retrieved chunks from the retriever.
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contexts(List[str]): The contexts from dataset.
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raw_dataset(Any): The raw data(single row) from dataset.
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"""
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if isinstance(contexts, str):
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contexts = [contexts]
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prediction_strs = [chunk.content for chunk in prediction]
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tasks = []
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for metric in self.evaluation_metrics:
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tasks.append(metric.compute(prediction_strs, contexts))
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task_results = await asyncio.gather(*tasks)
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results = []
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for result, metric in zip(task_results, self.evaluation_metrics):
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results.append(
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EvaluationResult(
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query=query,
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prediction=prediction,
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score=result.score,
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contexts=contexts,
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passing=result.passing,
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raw_dataset=raw_dataset,
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metric_name=metric.name,
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)
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)
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return results
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