3 Summarizing text using LangChain
This chapter covers
- Summarization of large documents exceeding the LLM’s context window
- Summarization across multiple documents
- Summarization of structured data
In Chapter 1, you explored three major LLM application types: summarization engines, chatbots, and autonomous agents. In this chapter, you'll begin building practical summarization chains using LangChain, with a particular focus on the LangChain Expression Language (LCEL) to handle various real-world scenarios. A chain is a sequence of connected operations where the output of one step becomes the input for the next—ideal for automating tasks like summarization. This work lays the foundation for constructing a more advanced summarization engine in the next chapter.
Summarization engines are essential for automating the summarization of large document volumes, a task that would be impractical and costly to handle manually, even with tools like ChatGPT. Starting with a summarization engine is a practical entry point for developing LLM applications, providing a solid base for more complex projects and showcasing LangChain’s capabilities, which we’ll further explore in later chapters.