"""
        return self.similarity_search_by_vector(
            self.embedding_function.embed_query(query), k, where_str, **kwargs
        )
[docs]    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        where_str: Optional[str] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Perform a similarity search with StarRocks by vectors
        Args:
            query (str): query string
            k (int, optional): Top K neighbors to retrieve. Defaults to 4.
            where_str (Optional[str], optional): where condition string.
                                                 Defaults to None.
            NOTE: Please do not let end-user to fill this and always be aware
                  of SQL injection. When dealing with metadatas, remember to
                  use `{self.metadata_column}.attribute` instead of `attribute`
                  alone. The default name for it is `metadata`.
        Returns:
            List[Document]: List of (Document, similarity)
        """
        q_str = self._build_query_sql(embedding, k, where_str)
        try:
            return [
                Document(
                    page_content=r[self.config.column_map["document"]],
                    metadata=json.loads(r[self.config.column_map["metadata"]]),
                )
                for r in get_named_result(self.connection, q_str)
            ]
        except Exception as e:
            logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
            return []
[docs]    def similarity_search_with_relevance_scores(