index_name = kwargs.get("index_name", self.index_name)
        text_field = kwargs.get("text_field", "text")
        dim = len(embeddings[0])
        engine = kwargs.get("engine", "nmslib")
        space_type = kwargs.get("space_type", "l2")
        ef_search = kwargs.get("ef_search", 512)
        ef_construction = kwargs.get("ef_construction", 512)
        m = kwargs.get("m", 16)
        vector_field = kwargs.get("vector_field", "vector_field")
        max_chunk_bytes = kwargs.get("max_chunk_bytes", 1 * 1024 * 1024)
        _validate_aoss_with_engines(self.is_aoss, engine)
        mapping = _default_text_mapping(
            dim, engine, space_type, ef_search, ef_construction, m, vector_field
        )
        return _bulk_ingest_embeddings(
            self.client,
            index_name,
            embeddings,
            texts,
            metadatas=metadatas,
            ids=ids,
            vector_field=vector_field,
            text_field=text_field,
            mapping=mapping,
            max_chunk_bytes=max_chunk_bytes,
            is_aoss=self.is_aoss,
        )
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        bulk_size: int = 500,
        **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.