Unlock the Power of LangChain to Summarize Text

A Simple Guide to Summarize Lengthy PDFs, Docs, and URLs with LangChain

Explore the latest in the LangChain series! Dive into seamless techniques for integrating LangChain to extract and summarize content from diverse formats like PDFs, DOCs, plain text, or even directly from a URL.

How LangChain Makes Large Language Models More Powerful: Part 1

How LangChain Makes Large Language Models More Powerful: Part 2

Vector Database: Empowering Next-Gen Applications

Imagine you are researching a complex topic. You find a number of relevant articles and books, but you don’t have time to read them all. You think LLMs would be a quick help, but then token limits and outdated training data get in the way!!!

But wait…with LangChain, it’s possible!!!

LangChain can help resolve the issue of limited token size or summarizing content from the web URL that the LLM is not trained on.

LangChain is a powerful text summarization library that uses large language models (LLMs) to generate summaries of text in a variety of formats, including PDFs, Docs, and web pages.

source: https://python.langchain.com/docs/use_cases/summarization

With LangChain and LLMs, you can instantly summarize any text, regardless of its length or complexity, even if the LLM is not trained on that data.

so, how to pass large PDFs or documents or URLs into the LLM’s context window?

Stuff

One way to summarize multiple documents with LangChain is to “stuff” or “fill” all of the document contents into a single prompt in case the document content fits within the size of the prompt.

This is done using the load_summarize_chain() function with the chain_type="stuff" parameter. This function takes a list of documents or text as input, inserts all of the…