texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        index_name: Optional[str] = None,
        index_schema: Optional[Union[Dict[str, str], str, os.PathLike]] = None,
        vector_schema: Optional[Dict[str, Union[str, int]]] = None,
        **kwargs: Any,
    ) -> Redis:
        """Create a Redis vectorstore from a list of texts.
        This is a user-friendly interface that:
            1. Embeds documents.
            2. Creates a new Redis index if it doesn't already exist
            3. Adds the documents to the newly created Redis index.
        This method will generate schema based on the metadata passed in
        if the `index_schema` is not defined. If the `index_schema` is defined,
        it will compare against the generated schema and warn if there are
        differences. If you are purposefully defining the schema for the
        metadata, then you can ignore that warning.
        To examine the schema options, initialize an instance of this class
        and print out the schema using the `Redis.schema`` property. This
        will include the content and content_vector classes which are
        always present in the langchain schema.
        Example:
            .. code-block:: python
                from langchain.vectorstores import Redis
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                redisearch = RediSearch.from_texts(
                    texts,
                    embeddings,
                    redis_url="redis://username:password@localhost:6379"
                )
        Args:
            texts (List[str]): List of texts to add to the vectorstore.