EmbeddingsClusteringFilter#
- class langchain_community.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter[source]#
Bases:
BaseDocumentTransformer,BaseModelPerform 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: 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
Examples using EmbeddingsClusteringFilter