2. Creates a new index for the embeddings in the Weaviate instance.
            3. Adds the documents to the newly created Weaviate index.
        This is intended to be a quick way to get started.
        Args:
            texts: Texts to add to vector store.
            embedding: Text embedding model to use.
            metadatas: Metadata associated with each text.
            client: weaviate.Client to use.
            weaviate_url: The Weaviate URL. If using Weaviate Cloud Services get it
                from the ``Details`` tab. Can be passed in as a named param or by
                setting the environment variable ``WEAVIATE_URL``. Should not be
                specified if client is provided.
            weaviate_api_key: The Weaviate API key. If enabled and using Weaviate Cloud
                Services, get it from ``Details`` tab. Can be passed in as a named param
                or by setting the environment variable ``WEAVIATE_API_KEY``. Should
                not be specified if client is provided.
            batch_size: Size of batch operations.
            index_name: Index name.
            text_key: Key to use for uploading/retrieving text to/from vectorstore.
            by_text: Whether to search by text or by embedding.
            relevance_score_fn: Function for converting whatever distance function the
                vector store uses to a relevance score, which is a normalized similarity
                score (0 means dissimilar, 1 means similar).
            **kwargs: Additional named parameters to pass to ``Weaviate.__init__()``.
        Example:
            .. code-block:: python
                from langchain_community.embeddings import OpenAIEmbeddings
                from langchain_community.vectorstores import Weaviate
                embeddings = OpenAIEmbeddings()