ids: Optional[List[str]] = None,
        batch_size: int = 32,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore
                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.
            batch_size: Send documents in batches to rockset.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        batch: list[dict] = []
        stored_ids = []
        for i, text in enumerate(texts):
            if len(batch) == batch_size:
                stored_ids += self._write_documents_to_rockset(batch)
                batch = []
            doc = {}
            if metadatas and len(metadatas) > i:
                doc = metadatas[i]
            if ids and len(ids) > i:
                doc["_id"] = ids[i]
            doc[self._text_key] = text
            doc[self._embedding_key] = self._embeddings.embed_query(text)
            batch.append(doc)
        if len(batch) > 0:
            stored_ids += self._write_documents_to_rockset(batch)
            batch = []
        return stored_ids
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        client: Any = None,
        collection_name: str = "",
        text_key: str = "",
        embedding_key: str = "",
        ids: Optional[List[str]] = None,