if "uuids" in kwargs:
                    _id = kwargs["uuids"][i]
                elif "ids" in kwargs:
                    _id = kwargs["ids"][i]
                batch.add_data_object(
                    data_object=data_properties,
                    class_name=self._index_name,
                    uuid=_id,
                    vector=embeddings[i] if embeddings else None,
                )
                ids.append(_id)
        return ids
[docs]    def similarity_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the query.
        """
        if self._by_text:
            return self.similarity_search_by_text(query, k, **kwargs)
        else:
            if self._embedding is None:
                raise ValueError(
                    "_embedding cannot be None for similarity_search when "
                    "_by_text=False"
                )
            embedding = self._embedding.embed_query(query)
            return self.similarity_search_by_vector(embedding, k, **kwargs)
[docs]    def similarity_search_by_text(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the query.
        """
        content: Dict[str, Any] = {"concepts": [query]}