def load_data(
        self,
        query: np.ndarray,
        id_to_text_map: Dict[str, str],
        k: int = 4,
        separate_documents: bool = True,
    ) -> List[Document]:
        """
        Load data from txtai index.

        Args:
            query (np.ndarray): A 2D numpy array of query vectors.
            id_to_text_map (Dict[str, str]): A map from ID's to text.
            k (int): Number of nearest neighbors to retrieve. Defaults to 4.
            separate_documents (Optional[bool]): Whether to return separate
                documents. Defaults to True.

        Returns:
            List[Document]: A list of documents.

        """
        search_result = self._index.search(query, k)
        documents = []
        for query_result in search_result:
            for doc_id, _ in query_result:
                doc_id = str(doc_id)
                if doc_id not in id_to_text_map:
                    raise ValueError(
                        f"Document ID {doc_id} not found in id_to_text_map."
                    )
                text = id_to_text_map[doc_id]
                documents.append(Document(text=text))

        if not separate_documents:
            # join all documents into one
            text_list = [doc.get_content() for doc in documents]
            text = "\n\n".join(text_list)
            documents = [Document(text=text)]

        return documents

        Load data from txtai index.

        Args:
            query (np.ndarray): A 2D numpy array of query vectors.
            id_to_text_map (Dict[str, str]): A map from ID's to text.
            k (int): Number of nearest neighbors to retrieve. Defaults to 4.
            separate_documents (Optional[bool]): Whether to return separate
                documents. Defaults to True.

        Returns:
            List[Document]: A list of documents.

        
llama-index-integrations/readers/llama-index-readers-txtai/llama_index/readers/txtai/base.py