>>> ds = datasets.load_dataset('crime_and_punish', split='train')
        >>> # load index
        >>> ds.load_faiss_index('embeddings', 'my_index.faiss')
        >>> # query
        >>> scores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)
        ```
        """
        with self.formatted_as(type="numpy", columns=[column], dtype=dtype):
            super().add_faiss_index(
                column=column,
                index_name=index_name,
                device=device,
                string_factory=string_factory,
                metric_type=metric_type,
                custom_index=custom_index,
                batch_size=batch_size,
                train_size=train_size,
                faiss_verbose=faiss_verbose,
            )
        return self