Criteria or rubric based evaluators.
These evaluators are useful for evaluating the output of a language model or chain against specified criteria or rubric.
CriteriaEvalChain : Evaluates the output of a language model or chain against specified criteria.
Using a predefined criterion:
from langchain_openai import OpenAI from langchain_classic.evaluation.criteria import CriteriaEvalChain
model = OpenAI() criteria = "conciseness" chain = CriteriaEvalChain.from_llm(llm=model, 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_openai import OpenAI from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain
model = OpenAI() criteria = { "hallucination": ( "Does this submission contain information" " not present in the input or reference?" ), } chain = LabeledCriteriaEvalChain.from_llm( llm=model, 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.", )
A Criteria to evaluate.
LLM Chain for evaluating runs against criteria.
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.
CriteriaEvalChain
An instance of the CriteriaEvalChain class.
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.
