EvaluatorType

class langchain.evaluation.schema.EvaluatorType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

The types of the evaluators.

QA = 'qa'

Question answering evaluator, which grades answers to questions directly using an LLM.

COT_QA = 'cot_qa'

Chain of thought question answering evaluator, which grades answers to questions using chain of thought ‘reasoning’.

CONTEXT_QA = 'context_qa'

Question answering evaluator that incorporates ‘context’ in the response.

PAIRWISE_STRING = 'pairwise_string'

The pairwise string evaluator, which predicts the preferred prediction from between two models.

SCORE_STRING = 'score_string'

The scored string evaluator, which gives a score between 1 and 10 to a prediction.

LABELED_PAIRWISE_STRING = 'labeled_pairwise_string'

The labeled pairwise string evaluator, which predicts the preferred prediction from between two models based on a ground truth reference label.

LABELED_SCORE_STRING = 'labeled_score_string'

The labeled scored string evaluator, which gives a score between 1 and 10 to a prediction based on a ground truth reference label.

AGENT_TRAJECTORY = 'trajectory'

The agent trajectory evaluator, which grades the agent’s intermediate steps.

CRITERIA = 'criteria'

The criteria evaluator, which evaluates a model based on a custom set of criteria without any reference labels.

LABELED_CRITERIA = 'labeled_criteria'

The labeled criteria evaluator, which evaluates a model based on a custom set of criteria, with a reference label.

STRING_DISTANCE = 'string_distance'

Compare predictions to a reference answer using string edit distances.

EXACT_MATCH = 'exact_match'

Compare predictions to a reference answer using exact matching.

REGEX_MATCH = 'regex_match'

Compare predictions to a reference answer using regular expressions.

PAIRWISE_STRING_DISTANCE = 'pairwise_string_distance'

Compare predictions based on string edit distances.

EMBEDDING_DISTANCE = 'embedding_distance'

Compare a prediction to a reference label using embedding distance.

PAIRWISE_EMBEDDING_DISTANCE = 'pairwise_embedding_distance'

Compare two predictions using embedding distance.

JSON_VALIDITY = 'json_validity'

Check if a prediction is valid JSON.

JSON_EQUALITY = 'json_equality'

Check if a prediction is equal to a reference JSON.

JSON_EDIT_DISTANCE = 'json_edit_distance'

Compute the edit distance between two JSON strings after canonicalization.

JSON_SCHEMA_VALIDATION = 'json_schema_validation'

Check if a prediction is valid JSON according to a JSON schema.