Bases: EvalConfig
Configuration for a string distance evaluator.
Parameters
distance (Optional[StringDistanceEnum]) – The string distance metric to use.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param distance: Optional[langchain.evaluation.string_distance.base.StringDistance] = None¶
The string distance metric to use.
damerau_levenshtein: The Damerau-Levenshtein distance.
levenshtein: The Levenshtein distance.
jaro: The Jaro distance.
jaro_winkler: The Jaro-Winkler distance.
param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.STRING_DISTANCE¶
param normalize_score: bool = True¶
Whether to normalize the distance to between 0 and 1.
Applies only to the Levenshtein and Damerau-Levenshtein distances.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include