'The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and joining with European allies to find and seize their yachts, luxury apartments, and private jets.'},     {'query': 'How much direct assistance is the United States providing to Ukraine?',      'answer': 'The United States is providing more than $1 Billion in direct assistance to Ukraine.'}]# Combine examplesexamples += new_examplesEvaluateâ€‹Now that we have examples, we can use the question answering evaluator to evaluate our question answering chain.from langchain.evaluation.qa import QAEvalChainpredictions = qa.apply(examples)llm = OpenAI(temperature=0)eval_chain = QAEvalChain.from_llm(llm)graded_outputs = eval_chain.evaluate(examples, predictions)for i, eg in enumerate(examples):    print(f"Example {i}:")    print("Question: " + predictions[i]["query"])    print("Real Answer: " + predictions[i]["answer"])    print("Predicted Answer: " + predictions[i]["result"])    print("Predicted Grade: " + graded_outputs[i]["text"])    print()    Example 0:    Question: What did the president say about Ketanji Brown Jackson    Real Answer: He praised her legal ability and said he nominated her for the supreme court.    Predicted Answer:  The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by both Democrats and Republicans.    Predicted Grade: