context_examples = [
    {
        "question": "How old am I?",
        "context": "I am 30 years old. I live in New York and take the train to work everyday.",
    },
    {
        "question": 'Who won the NFC championship game in 2023?"',
        "context": "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 ContextQAEvalChain
eval_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 metrics#
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 format
for i, eg in enumerate(examples):
    eg['id'] = str(i)
    eg['answers'] = {"text": [eg['answer']], "answer_start": [0]}
    predictions[i]['id'] = str(i)
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/evaluation/question_answering.html