def load_data(
        self,
        embedding: Optional[List[float]] = None,
        k: int = 10,
        metadata_fields: Optional[List[str]] = None,
        where: Optional[str] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """
        Load data from the jaguar vector store.

        Args:
            embedding: list of float number for vector. If this
                       is given, it returns topk similar documents.
            k: Number of results to return.
            where: "a = '100' or ( b > 100 and c < 200 )"
                   If embedding is not given, it finds values
                   of columns in metadata_fields, and the text value.
            metadata_fields: Optional[List[str]] a list of metadata fields to load
                       in addition to the text document

        Returns:
            List of documents

        """
        if embedding is not None:
            return self._load_similar_data(
                embedding=embedding,
                k=k,
                metadata_fields=metadata_fields,
                where=where,
                **kwargs,
            )
        else:
            return self._load_store_data(
                k=k, metadata_fields=metadata_fields, where=where, **kwargs
            )

        Load data from the jaguar vector store.

        Args:
            embedding: list of float number for vector. If this
                       is given, it returns topk similar documents.
            k: Number of results to return.
            where: "a = '100' or ( b > 100 and c < 200 )"
                   If embedding is not given, it finds values
                   of columns in metadata_fields, and the text value.
            metadata_fields: Optional[List[str]] a list of metadata fields to load
                       in addition to the text document

        Returns:
            List of documents

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