astra_db_store = cls(
            embedding=embedding,
            collection_name=collection_name,
            token=token,
            api_endpoint=api_endpoint,
            astra_db_client=astra_db_client,
            namespace=namespace,
            metric=metric,
            batch_size=kwargs.get("batch_size"),
            bulk_insert_batch_concurrency=kwargs.get("bulk_insert_batch_concurrency"),
            bulk_insert_overwrite_concurrency=kwargs.get(
                "bulk_insert_overwrite_concurrency"
            ),
            bulk_delete_concurrency=kwargs.get("bulk_delete_concurrency"),
        )
        astra_db_store.add_texts(
            texts=texts,
            metadatas=metadatas,
            ids=ids,
            batch_size=kwargs.get("batch_size"),
            batch_concurrency=kwargs.get("batch_concurrency"),
            overwrite_concurrency=kwargs.get("overwrite_concurrency"),
        )
        return astra_db_store
[docs]    @classmethod
    def from_documents(
        cls: Type[ADBVST],
        documents: List[Document],
        embedding: Embeddings,
        **kwargs: Any,
    ) -> ADBVST:
        """Create an Astra DB vectorstore from a document list.
        Utility method that defers to 'from_texts' (see that one).
        Args: see 'from_texts', except here you have to supply 'documents'
            in place of 'texts' and 'metadatas'.
        Returns:
            an `AstraDB` vectorstore.
        """
        return super().from_documents(documents, embedding, **kwargs)