langchain.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter

class langchain.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter[source]

Bases: BaseDocumentTransformer, BaseModel

Perform K-means clustering on document vectors. Returns an arbitrary number of documents closest to center.

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 embeddings: langchain_core.embeddings.Embeddings [Required]

Embeddings to use for embedding document contents.

param num_closest: int = 1

The number of closest vectors to return for each cluster center.

param num_clusters: int = 5

Number of clusters. Groups of documents with similar meaning.

param random_state: int = 42

Controls the random number generator used to initialize the cluster centroids. If you set the random_state parameter to None, the KMeans algorithm will use a random number generator that is seeded with the current time. This means that the results of the KMeans algorithm will be different each time you run it.

param remove_duplicates: bool = False

By default duplicated results are skipped and replaced by the next closest vector in the cluster. If remove_duplicates is true no replacement will be done: This could dramatically reduce results when there is a lot of overlap between clusters.

param sorted: bool = False

By default results are re-ordered “grouping” them by cluster, if sorted is true result will be ordered by the original position from the retriever

async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document]

Asynchronously transform a list of documents.

Parameters

documents – A sequence of Documents to be transformed.

Returns

A list of transformed Documents.

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

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
classmethod parse_obj(obj: Any) → Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]

Filter down documents.

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model

Examples using EmbeddingsClusteringFilter