for first_idx, second_idx in redundant_stacked[redundant_sorted]:
        if first_idx in included_idxs and second_idx in included_idxs:
            # Default to dropping the second document of any highly similar pair.
            included_idxs.remove(second_idx)
    return list(sorted(included_idxs))
def _get_embeddings_from_stateful_docs(
    embeddings: Embeddings, documents: Sequence[_DocumentWithState]
) -> List[List[float]]:
    if len(documents) and "embedded_doc" in documents[0].state:
        embedded_documents = [doc.state["embedded_doc"] for doc in documents]
    else:
        embedded_documents = embeddings.embed_documents(
            [d.page_content for d in documents]
        )
        for doc, embedding in zip(documents, embedded_documents):
            doc.state["embedded_doc"] = embedding
    return embedded_documents
[docs]class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):
    """Filter that drops redundant documents by comparing their embeddings."""
    embeddings: Embeddings
    """Embeddings to use for embedding document contents."""
    similarity_fn: Callable = cosine_similarity
    """Similarity function for comparing documents. Function expected to take as input
    two matrices (List[List[float]]) and return a matrix of scores where higher values
    indicate greater similarity."""
    similarity_threshold: float = 0.95
    """Threshold for determining when two documents are similar enough
    to be considered redundant."""
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
[docs]    def transform_documents(
        self, documents: Sequence[Document], **kwargs: Any
    ) -> Sequence[Document]:
        """Filter down documents."""
        stateful_documents = get_stateful_documents(documents)
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/document_transformers.html