langchain.prompts import PromptTemplatetemplate = """You are trying to answer the following question by querying an API:> Question: {question}The query you know you should be executing against the API is:> Query: {truth_query}Is the following predicted query semantically the same (eg likely to produce the same answer)?> Predicted Query: {predict_query}Please give the Predicted Query a grade of either an A, B, C, D, or F, along with an explanation of why. End the evaluation with 'Final Grade: <the letter>'> Explanation: Let's think step by step."""prompt = PromptTemplate.from_template(template)eval_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)request_eval_results = []for question, predict_query, truth_query in list(    zip(questions, predicted_queries, truth_queries)):    eval_output = eval_chain.run(        question=question,        truth_query=truth_query,        predict_query=predict_query,    )    request_eval_results.append(eval_output)request_eval_results    [' The original query is asking for all iPhone models, so the "q" parameter is correct. The "max_price" parameter is also correct, as it is set to null, meaning that no maximum price is set. The predicted query adds two additional parameters, "size" and "min_price". The "size" parameter is not necessary, as it is not relevant to the question being asked. The "min_price" parameter is also not necessary, as it is not relevant to the question being asked and it is set to 0, which is the default value. Therefore, the predicted query is not semantically the same as the original query and is not likely to produce the same answer. Final Grade: D',