for items in zip(*[res[0][field] for field in output_fields]):
            meta = {field: value for field, value in zip(output_fields, items)}
            doc = Document(page_content=meta.pop(self._text_field), metadata=meta)
            logger.debug(
                f"[similarity_search_with_score_by_vector] "
                f"res[0][score_col]:{res[0][score_col]}"
            )
            score = res[0][score_col][count]
            count += 1
            ret.append((doc, score))
        return ret
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        table_name: str = "test",
        database_name: str = "default",
        connection_args: Dict[str, Any] = DEFAULT_HIPPO_CONNECTION,
        index_params: Optional[Dict[Any, Any]] = None,
        search_params: Optional[Dict[str, Any]] = None,
        drop_old: bool = False,
        **kwargs: Any,
    ) -> "Hippo":
        """
        Creates an instance of the VST class from the given texts.
        Args:
            texts (List[str]): List of texts to be added.
            embedding (Embeddings): Embedding model for the texts.
            metadatas (List[dict], optional):
            List of metadata dictionaries for each text.Defaults to None.
            table_name (str): Name of the table. Defaults to "test".
            database_name (str): Name of the database. Defaults to "default".
            connection_args (dict[str, Any]): Connection parameters.
            Defaults to DEFAULT_HIPPO_CONNECTION.