ho$dZddlmZmZmZgdZy)aCriteria or rubric based evaluators. These evaluators are useful for evaluating the output of a language model or chain against specified criteria or rubric. Classes ------- CriteriaEvalChain : Evaluates the output of a language model or chain against specified criteria. Examples -------- Using a predefined criterion: >>> from langchain_community.llms import OpenAI >>> from langchain.evaluation.criteria import CriteriaEvalChain >>> llm = OpenAI() >>> criteria = "conciseness" >>> chain = CriteriaEvalChain.from_llm(llm=llm, criteria=criteria) >>> chain.evaluate_strings( prediction="The answer is 42.", reference="42", input="What is the answer to life, the universe, and everything?", ) Using a custom criterion: >>> from langchain_community.llms import OpenAI >>> from langchain.evaluation.criteria import LabeledCriteriaEvalChain >>> llm = OpenAI() >>> criteria = { "hallucination": ( "Does this submission contain information" " not present in the input or reference?" ), } >>> chain = LabeledCriteriaEvalChain.from_llm( llm=llm, criteria=criteria, ) >>> chain.evaluate_strings( prediction="The answer to life is 42.", reference="It's commonly known that the answer to life is 42.", input="Please summarize the following: The answer to life, the universe, and everything is unknowable.", ) )CriteriaCriteriaEvalChainLabeledCriteriaEvalChainN)__doc__(langchain.evaluation.criteria.eval_chainrrr__all__d/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/langchain/evaluation/criteria/__init__.pyr s/b Hr