Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Additional keyword arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict
classmethod from_llm(llm: BaseLanguageModel, criteria: Optional[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple]] = None, *, prompt: Optional[BasePromptTemplate] = None, **kwargs: Any) → CriteriaEvalChain[source]¶
Create a CriteriaEvalChain instance from an llm and criteria.
Parameters
llm (BaseLanguageModel) – The language model to use for evaluation.
criteria (CRITERIA_TYPE - default=None for "helpfulness") – 
The criteria to evaluate the runs against. It can be:
a mapping of a criterion name to its description
a single criterion name present in one of the default criteria
a single ConstitutionalPrinciple instance
prompt (Optional[BasePromptTemplate], default=None) – The prompt template to use for generating prompts. If not provided,
a default prompt template will be used.
**kwargs (Any) – Additional keyword arguments to pass to the LLMChain
constructor.
Returns
An instance of the CriteriaEvalChain class.
Return type
CriteriaEvalChain
Examples
>>> from langchain.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,