"""Create an Astra DB vectorstore from raw texts.
        Args:
            texts (List[str]): the texts to insert.
            embedding (Embeddings): the embedding function to use in the store.
            metadatas (Optional[List[dict]]): metadata dicts for the texts.
            ids (Optional[List[str]]): ids to associate to the texts.
            *Additional arguments*: you can pass any argument that you would
                to 'add_texts' and/or to the 'AstraDB' class constructor
                (see these methods for details). These arguments will be
                routed to the respective methods as they are.
        Returns:
            an `AstraDb` vectorstore.
        """
        known_kwargs = {
            "collection_name",
            "token",
            "api_endpoint",
            "astra_db_client",
            "namespace",
            "metric",
            "batch_size",
            "bulk_insert_batch_concurrency",
            "bulk_insert_overwrite_concurrency",
            "bulk_delete_concurrency",
            "batch_concurrency",
            "overwrite_concurrency",
        }
        if kwargs:
            unknown_kwargs = set(kwargs.keys()) - known_kwargs
            if unknown_kwargs:
                warnings.warn(
                    "Method 'from_texts' of AstraDB vector store invoked with "
                    f"unsupported arguments ({', '.join(sorted(unknown_kwargs))}), "
                    "which will be ignored."
                )
        collection_name: str = kwargs["collection_name"]
        token = kwargs.get("token")
        api_endpoint = kwargs.get("api_endpoint")
        astra_db_client = kwargs.get("astra_db_client")
        namespace = kwargs.get("namespace")
        metric = kwargs.get("metric")
        astra_db_store = cls(
            embedding=embedding,