is not relevant to the original query. The third part of the query is asking for a minimum price of 0, which is not relevant to the original query. The fourth part of the query is asking for a maximum price of null, which is not relevant to the original query. Therefore, the Predicted Query does not semantically match the original query and is not likely to produce the same answer. Final Grade: F',     ' The original query is asking for cameras with a maximum price of 300. The predicted query is asking for cameras with a maximum price of 500. This means that the predicted query is likely to return more results than the original query, which may include cameras that are not within the budget range. Therefore, the predicted query is not semantically the same as the original query and does not answer the original question. Final Grade: F']import refrom typing import List# Parse the evaluation chain responses into a rubricdef parse_eval_results(results: List[str]) -> List[float]:    rubric = {"A": 1.0, "B": 0.75, "C": 0.5, "D": 0.25, "F": 0}    return [rubric[re.search(r"Final Grade: (\w+)", res).group(1)] for res in results]parsed_results = parse_eval_results(request_eval_results)# Collect the scores for a final evaluation tablescores["request_synthesizer"].extend(parsed_results)Evaluate the Response Chainâ€‹The second component translated the structured API response to a natural language response.