```python with open("../../state_of_the_union.txt") as f: state_of_the_union = f.read() ``` ## Summarize Let's take a look at it in action below, using it summarize a long document. ```python from langchain import OpenAI from langchain.chains.summarize import load_summarize_chain llm = OpenAI(temperature=0) summary_chain = load_summarize_chain(llm, chain_type="map_reduce") ``` ```python from langchain.chains import AnalyzeDocumentChain ``` ```python summarize_document_chain = AnalyzeDocumentChain(combine_docs_chain=summary_chain) ``` ```python summarize_document_chain.run(state_of_the_union) ``` ``` " In this speech, President Biden addresses the American people and the world, discussing the recent aggression of Russia's Vladimir Putin in Ukraine and the US response. He outlines economic sanctions and other measures taken to hold Putin accountable, and announces the US Department of Justice's task force to go after the crimes of Russian oligarchs. He also announces plans to fight inflation and lower costs for families, invest in American manufacturing, and provide military, economic, and humanitarian assistance to Ukraine. He calls for immigration reform, protecting the rights of women, and advancing the rights of LGBTQ+ Americans, and pays tribute to military families. He concludes with optimism for the future of America." ``` ## Question Answering Let's take a look at this using a question answering chain. ```python from langchain.chains.question_answering import load_qa_chain ``` ```python qa_chain = load_qa_chain(llm, chain_type="map_reduce") ``` ```python qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain) ``` ```python qa_document_chain.run(input_document=state_of_the_union, question="what did the president say about justice breyer?") ``` ``` ' The president thanked Justice Breyer for his service.' ```