def load_data(self, search_embedding: str, top_k: int = 5) -> List[Document]:
        """
        Load data from SingleStore.

        Args:
            search_embedding (str): The embedding to search.
            top_k (int): Number of results to return.

        Returns:
            List[Document]: A list of documents.

        """
        query = f"""
        SELECT {self.content_field}, DOT_PRODUCT_F64({self.vector_field}, JSON_ARRAY_PACK_F64(\'{search_embedding}\')) AS score
        FROM {self.table_name}
        ORDER BY score
        DESC LIMIT {top_k}
        """

        return self.reader.load_data(query=query)

        Load data from SingleStore.

        Args:
            search_embedding (str): The embedding to search.
            top_k (int): Number of results to return.

        Returns:
            List[Document]: A list of documents.

        
llama-index-integrations/readers/llama-index-readers-singlestore/llama_index/readers/singlestore/base.py