forked from Archives/langchain
Update run eval imports in init (#5858)
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@ -1,20 +1,22 @@
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"""Evaluation classes that interface with traced runs and datasets."""
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from langchain.evaluation.run_evaluators.base import (
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RunEvalInputMapper,
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RunEvaluator,
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RunEvaluatorChain,
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RunEvaluatorInputMapper,
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RunEvaluatorOutputParser,
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)
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from langchain.evaluation.run_evaluators.implementations import (
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ChoicesOutputParser,
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StringRunEvaluatorInputMapper,
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get_criteria_evaluator,
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get_qa_evaluator,
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)
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__all__ = [
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"RunEvaluator",
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"RunEvalInputMapper",
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"RunEvaluatorChain",
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"RunEvaluatorInputMapper",
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"RunEvaluatorOutputParser",
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"get_qa_evaluator",
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"get_criteria_evaluator",
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"StringRunEvaluatorInputMapper",
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"ChoicesOutputParser",
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]
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@ -15,7 +15,7 @@ from langchain.chains.llm import LLMChain
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from langchain.schema import RUN_KEY, BaseOutputParser
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class RunEvalInputMapper:
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class RunEvaluatorInputMapper:
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"""Map the inputs of a run to the inputs of an evaluation."""
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@abstractmethod
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@ -37,7 +37,7 @@ class RunEvaluatorOutputParser(BaseOutputParser[EvaluationResult]):
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class RunEvaluatorChain(Chain, RunEvaluator):
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"""Evaluate Run and optional examples."""
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input_mapper: RunEvalInputMapper
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input_mapper: RunEvaluatorInputMapper
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"""Maps the Run and Optional example to a dictionary for the eval chain."""
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eval_chain: LLMChain
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"""The evaluation chain."""
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@ -10,8 +10,8 @@ from langchain.evaluation.qa.eval_chain import QAEvalChain
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from langchain.evaluation.qa.eval_prompt import PROMPT as QA_DEFAULT_PROMPT
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from langchain.evaluation.qa.eval_prompt import SQL_PROMPT
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from langchain.evaluation.run_evaluators.base import (
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RunEvalInputMapper,
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RunEvaluatorChain,
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RunEvaluatorInputMapper,
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RunEvaluatorOutputParser,
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)
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from langchain.evaluation.run_evaluators.criteria_prompt import (
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@ -25,7 +25,7 @@ _QA_PROMPTS = {
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}
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class StringRunEvalInputMapper(RunEvalInputMapper, BaseModel):
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class StringRunEvaluatorInputMapper(RunEvaluatorInputMapper, BaseModel):
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"""Maps the Run and Optional[Example] to a dictionary."""
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prediction_map: Mapping[str, str]
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@ -97,7 +97,7 @@ def get_qa_evaluator(
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eval_chain = QAEvalChain.from_llm(llm=llm, prompt=prompt, **kwargs)
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input_mapper = kwargs.pop(
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"input_mapper",
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StringRunEvalInputMapper(
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StringRunEvaluatorInputMapper(
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input_map={input_key: "query"},
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prediction_map={prediction_key: "result"},
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answer_map={answer_key: "answer"},
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@ -179,7 +179,7 @@ def get_criteria_evaluator(
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prompt_ = prompt.partial(criteria=criteria_str)
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input_mapper = kwargs.pop(
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"input_mapper",
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StringRunEvalInputMapper(
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StringRunEvaluatorInputMapper(
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input_map={input_key: "input"},
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prediction_map={prediction_key: "output"},
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),
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