"NFC Championship Game 2023: Philadelphia Eagles 31, San Francisco 49ers 7",    },]QA_PROMPT = "Answer the question based on the  context\nContext:{context}\nQuestion:{question}\nAnswer:"template = PromptTemplate(input_variables=["context", "question"], template=QA_PROMPT)qa_chain = LLMChain(llm=llm, prompt=template)predictions = qa_chain.apply(context_examples)predictions    [{'text': 'You are 30 years old.'},     {'text': ' The Philadelphia Eagles won the NFC championship game in 2023.'}]from langchain.evaluation.qa import ContextQAEvalChaineval_chain = ContextQAEvalChain.from_llm(llm)graded_outputs = eval_chain.evaluate(    context_examples, predictions, question_key="question", prediction_key="text")graded_outputs    [{'text': ' CORRECT'}, {'text': ' CORRECT'}]Comparing to other evaluation metricsâ€‹We can compare the evaluation results we get to other common evaluation metrics. To do this, let's load some evaluation metrics from HuggingFace's evaluate package.# Some data munging to get the examples in the right formatfor i, eg in enumerate(examples):    eg["id"] = str(i)    eg["answers"] = {"text": [eg["answer"]], "answer_start": [0]}    predictions[i]["id"] = str(i)    predictions[i]["prediction_text"] = predictions[i]["text"]for p in predictions:    del p["text"]new_examples = examples.copy()for eg in new_examples:    del eg["question"]    del eg["answer"]from evaluate import loadsquad_metric = load("squad")results = squad_metric.compute(    references=new_examples,