4 Summarizing text using LangChain
This chapter covers
- Summarization of large documents exceeding the LLM’s context window
- Summarization across documents
- Summarization of structured data
In chapter 1, we discussed the three main solutions for most LLM needs: the summarization (or query) engine, the chatbot, and the autonomous agent. This chapter lays the groundwork for a summarization engine, which we'll develop further in the next chapter. Subsequent chapters will focus on chatbots and knowledge agents.
Summarization engines come in handy when you need to automate summarization tasks for numerous documents regularly. Doing this manually, even with tools like ChatGPT, isn't practical for large volumes.
A summarization engine serves as a straightforward yet practical starting point to delve into LLM application development. It provides foundational elements for more complex applications and offers an opportunity to showcase LangChain functionalities we'll use in upcoming chapters.
Before diving into building a summarization engine, it's essential to understand various summarization techniques. We'll explore methods tailored to different scenarios, considering factors like document size, content consolidation, and handling structured data such as table rows.
In the LangChain introduction in section 1.7.2, you've already summarized a small document using a PromptTemplate, so we can skip this scenario.