Summary

This chapter explores document summarization techniques using LangChain, building upon the previous chapter’s discussion of text processing and context windows. It covers various summarization methods, including the stuff method for short documents, loaders for different data sources, and MapReduce patterns for handling long documents. The chapter also provides guidance on prompt engineering and discusses the use of multimodal models, such as Gemini 1.5, for summarizing multimodal content. Finally, it examines the future relevance of MapReduce patterns considering advancements in LLMs with large context windows. We also provided many examples to help you get started – check out our GitHub!

After witnessing the power of Q&A in context, you might wonder whether MapReduce patterns for summarization will still be relevant in the future. We believe that the answer is “yes,” but for more specific use cases. Specifically, when working with (less...