def METHOD_NAME(filters: FilterRequest = None):
    """
    This endpoint returns basic accuracy metrics based on user feedback,
    e.g., the ratio of correct answers or correctly identified documents.
    You can filter the output by document or label.
    Example:
    `curl --location --request POST 'http://127.0.0.1:8000/eval-doc-qa-feedback' \
     --header 'Content-Type: application/json' \
     --data-raw '{ "filters": {"document_id": ["XRR3xnEBCYVTkbTystOB"]} }'`
    """
    if filters:
        filters_content = filters.filters or {}
        filters_content["origin"] = ["user-feedback"]
    else:
        filters_content = {"origin": ["user-feedback"]}
    labels = DOCUMENT_STORE.get_all_labels(filters=filters_content)
    res: Dict[str, Optional[Union[float, int]]]
    if len(labels) > 0:
        answer_feedback = [1 if l.is_correct_answer else 0 for l in labels]
        doc_feedback = [1 if l.is_correct_document else 0 for l in labels]
        answer_accuracy = sum(answer_feedback) / len(answer_feedback)
        doc_accuracy = sum(doc_feedback) / len(doc_feedback)
        res = {"answer_accuracy": answer_accuracy, "document_accuracy": doc_accuracy, "n_feedback": len(labels)}
    else:
        res = {"answer_accuracy": None, "document_accuracy": None, "n_feedback": 0}
    return res