eval_chain

Resolve the criteria to evaluate.

Parameters

criteria : CRITERIA_TYPE 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

Returns:

Dict[str, str] A dictionary mapping criterion names to descriptions.

Examples:

criterion = "relevance" CriteriaEvalChain.resolve_criteria(criteria) {'relevance': 'Is the submission referring to a real quote from the text?'}

Class for a constitutional principle.

A base class for evaluators that use an LLM.

String evaluator interface.

Grade, tag, or otherwise evaluate predictions relative to their inputs and/or reference labels.

A Criteria to evaluate.

A parser for the output of the CriteriaEvalChain.

LLM Chain for evaluating runs against criteria.

Parameters

llm : BaseLanguageModel The language model to use for evaluation. criteria : Union[Mapping[str, str]] The criteria or rubric to evaluate the runs against. It can be a mapping of criterion name to its description, or a single criterion name. prompt : Optional[BasePromptTemplate], default=None The prompt template to use for generating prompts. If not provided, a default prompt template will be used based on the value of requires_reference. requires_reference : bool, default=False Whether the evaluation requires a reference text. If True, the PROMPT_WITH_REFERENCES template will be used, which includes the reference labels in the prompt. Otherwise, the PROMPT template will be used, which is a reference-free prompt. **kwargs : Any Additional keyword arguments to pass to the LLMChain constructor.

Returns:

CriteriaEvalChain An instance of the CriteriaEvalChain class.

Examples:

from langchain_anthropic import ChatAnthropic from langchain_classic.evaluation.criteria import CriteriaEvalChain model = ChatAnthropic(temperature=0) criteria = {"my-custom-criterion": "Is the submission the most amazing ever?"} evaluator = CriteriaEvalChain.from_llm(llm=model, criteria=criteria) evaluator.evaluate_strings( ... prediction="Imagine an ice cream flavor for the color aquamarine", ... input="Tell me an idea", ... ) { 'reasoning': 'Here is my step-by-step reasoning for the given criteria:\n\nThe criterion is: "Is the submission the most amazing ever?" This is a subjective criterion and open to interpretation. The submission suggests an aquamarine-colored ice cream flavor which is creative but may or may not be considered the most amazing idea ever conceived. There are many possible amazing ideas and this one ice cream flavor suggestion may or may not rise to that level for every person. \n\nN', 'value': 'N', 'score': 0, }

from langchain_openai import ChatOpenAI from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain model = ChatOpenAI(model="gpt-4", temperature=0) criteria = "correctness" evaluator = LabeledCriteriaEvalChain.from_llm( ... llm=model, ... criteria=criteria, ... ) evaluator.evaluate_strings( ... prediction="The answer is 4", ... input="How many apples are there?", ... reference="There are 3 apples", ... ) { 'score': 0, 'reasoning': 'The criterion for this task is the correctness of the submission. The submission states that there are 4 apples, but the reference indicates that there are actually 3 apples. Therefore, the submission is not correct, accurate, or factual according to the given criterion.\n\nN', 'value': 'N', }

Criteria evaluation chain that requires references.