namespace: Optional[str] = None,
        batch_size: int = 32,
        embedding_chunk_size: int = 1000,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Upsert optimization is done by chunking the embeddings and upserting them.
        This is done to avoid memory issues and optimize using HTTP based embeddings.
        For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index,
        embedding_chunk_size>1000 and batch_size~64 for best performance.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            ids: Optional list of ids to associate with the texts.
            namespace: Optional pinecone namespace to add the texts to.
            batch_size: Batch size to use when adding the texts to the vectorstore.
            embedding_chunk_size: Chunk size to use when embedding the texts.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        if namespace is None:
            namespace = self._namespace
        texts = list(texts)
        ids = ids or [str(uuid.uuid4()) for _ in texts]
        metadatas = metadatas or [{} for _ in texts]
        for metadata, text in zip(metadatas, texts):
            metadata[self._text_key] = text
        # For loops to avoid memory issues and optimize when using HTTP based embeddings
        # The first loop runs the embeddings, it benefits when using OpenAI embeddings
        # The second loops runs the pinecone upsert asynchoronously.
        for i in range(0, len(texts), embedding_chunk_size):