LangChain Tutorial: Showcasing LangChain Use Cases
102_use_cases This tutorial dives straight into implementing LangChain use case using a step-by-step approach, and hopefully inspire you to build. Summarization ¶ Langchain summarization is one of the most common usage of LangChain and LLMs. It can summarize any amount of text or documentations, including the ones that exceeds the token limit (currently set at 4096 tokens for gpt-35-turbo ), which roughly equates to 3000 words . The technique used is similar to the concept of sliding window , which a fixed size window, usually under the max context window limit, is used to chunk the the long document into smaller pieces, then summarize the content recursively to produce the final summary using mapreduce. You can use it to summarize not only text, books, documents, audios, social media threads, and etc. Some use cases including: articles , research pagers , legal and financial documents , transcripts , chat history...