jaro_winkler: The Jaro-Winkler distance.
param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.STRING_DISTANCE¶
param input_key: Optional[str] = None¶
The key from the traced run’s inputs dictionary to use to represent the
input. If not provided, it will be inferred automatically.
param normalize_score: bool = True¶
Whether to normalize the distance to between 0 and 1.
Applies only to the Levenshtein and Damerau-Levenshtein distances.
param prediction_key: Optional[str] = None¶
The key from the traced run’s outputs dictionary to use to
represent the prediction. If not provided, it will be inferred
automatically.
param reference_key: Optional[str] = None¶
The key in the dataset run to use as the reference string.
If not provided, we will attempt to infer automatically.
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
update – values to change/add in the new model. Note: the data is not validated before creating