cls,
        texts: List[str],
        embedding: Any = None,
        metadatas: Optional[List[dict]] = None,
        index_name: str = "",
        url: str = "http://localhost:8882",
        api_key: str = "",
        add_documents_settings: Optional[Dict[str, Any]] = None,
        searchable_attributes: Optional[List[str]] = None,
        page_content_builder: Optional[Callable[[Dict[str, str]], str]] = None,
        index_settings: Optional[Dict[str, Any]] = None,
        verbose: bool = True,
        **kwargs: Any,
    ) -> Marqo:
        """Return Marqo initialized from texts. Note that Marqo does not need
        embeddings, we retain the parameter to adhere to the Liskov
        substitution principle.
        This is a quick way to get started with marqo - simply provide your texts and
        metadatas and this will create an instance of the data store and index the
        provided data.
        To know the ids of your documents with this approach you will need to include
        them in under the key "_id" in your metadatas for each text
        Example:
        .. code-block:: python
                from langchain.vectorstores import Marqo
                datastore = Marqo(texts=['text'], index_name='my-first-index',
                url='http://localhost:8882')
        Args:
            texts (List[str]): A list of texts to index into marqo upon creation.
            embedding (Any, optional): Embeddings (not required). Defaults to None.
            index_name (str, optional): The name of the index to use, if none is
            provided then one will be created with a UUID. Defaults to None.