def load_documents(
        self, embedding: List[float], k: int, **kwargs: Any
    ) -> List[Document]:
        """
        Query index to load top k most similar documents.

        Args:
            embedding: a list of floats
            k: topK number
            kwargs:  may contain 'where', 'metadata_fields', 'args', 'fetch_k'

        """
        return cast(
            List[Document],
            self.similarity_search_with_score(embedding, k=k, form="doc", **kwargs),
        )

        Query index to load top k most similar documents.

        Args:
            embedding: a list of floats
            k: topK number
            kwargs:  may contain 'where', 'metadata_fields', 'args', 'fetch_k'

        
llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py