self,
        embedding: List[float],
        k: int = 4,
        filters: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filters: Filters to apply to the query. Defaults to None.
        Returns:
            List of Documents most similar to the embedding.
        """
        docs_with_score = self.similarity_search_by_vector_with_score(
            embedding=embedding, k=k, filters=filters, **kwargs
        )
        return [doc for doc, _ in docs_with_score]
[docs]    def similarity_search_by_vector_with_score(
        self,
        embedding: List[float],
        k: int = 4,
        filters: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to embedding vector, along with scores.
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filters: Filters to apply to the query. Defaults to None.
        Returns:
            List of Documents most similar to the embedding and score for each.
        """
        if self._is_databricks_managed_embeddings():
            raise ValueError(
                "`similarity_search_by_vector` is not supported for index with "
                "Databricks-managed embeddings."
            )
        search_resp = self.index.similarity_search(
            columns=self.columns,
            query_vector=embedding,
            filters=filters,
            num_results=k,
        )