distance_func (DistanceFunction): how to compute distance between two
                vectors in Rockset.
            k (int, optional): Top K neighbors to retrieve. Defaults to 4.
            where_str (Optional[str], optional): Metadata filters supplied as a
                SQL `where` condition string. Defaults to None.
                eg. "price<=70.0 AND brand='Nintendo'"
            NOTE: Please do not let end-user to fill this and always be aware
                  of SQL injection.
        Returns:
            List[Tuple[Document, float]]: List of documents with their relevance score
        """
        return self.similarity_search_by_vector_with_relevance_scores(
            self._embeddings.embed_query(query),
            k,
            distance_func,
            where_str,
            **kwargs,
        )
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        distance_func: DistanceFunction = DistanceFunction.COSINE_SIM,
        where_str: Optional[str] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Same as `similarity_search_with_relevance_scores` but
        doesn't return the scores.
        """
        return self.similarity_search_by_vector(
            self._embeddings.embed_query(query),
            k,
            distance_func,
            where_str,
            **kwargs,
        )
[docs]    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        distance_func: DistanceFunction = DistanceFunction.COSINE_SIM,
        where_str: Optional[str] = None,
        **kwargs: Any,
    ) -> List[Document]: