Bases: EvalConfig
Configuration for an embedding distance evaluator.
Parameters
embeddings (Optional[Embeddings]) – The embeddings to use for computing the distance.
distance_metric (Optional[EmbeddingDistanceEnum]) – The distance metric to use for computing the distance.
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_metric: Optional[langchain.evaluation.embedding_distance.base.EmbeddingDistance] = None¶
param embeddings: Optional[langchain.schema.embeddings.Embeddings] = None¶
param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.EMBEDDING_DISTANCE¶
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
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance