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QA Generation
QA Generation#
This notebook shows how to use the QAGenerationChain to come up with question-answer pairs over a specific document.
This is important because often times you may not have data to evaluate your question-answer system over, so this is a cheap and lightweight way to generate it!
from langchain.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
doc = loader.load()[0]
from langchain.chat_models import ChatOpenAI
from langchain.chains import QAGenerationChain
chain = QAGenerationChain.from_llm(ChatOpenAI(temperature = 0))
qa = chain.run(doc.page_content)
qa[1]
{'question': 'What is the U.S. Department of Justice doing to combat the crimes of Russian oligarchs?',
 'answer': 'The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.'}
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Question Answering Benchmarking: State of the Union Address
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Question Answering
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/evaluation/qa_generation.html