Short-course introduction (YouTube) to LangChain, presented by host Andrew with Harrison Chase (creator of LangChain). The speakers describe LangChain as an open-source framework that factors common abstractions for building LLM applications—models, prompts and parsers, memory, chains, document question-answering, evaluation, and agents—to reduce glue code and speed development. The video positions the course as practical training for building LLM-powered applications quickly.
Chapters
I'm excited to share with you this new short course on LangChain for LLM Large language model application development. This is built in collaboration with Harrison Chase, creator of LangChain. LLMs have made it possible to use prompting to develop powerful AI applications much faster than ever before. But an application, say, building a question answering system to answer questions about the text document, that might require prompting an LLM multiple times with multiple inputs, pausing the LLM output to then feed it to downstream LLM prompts, and so on. And so there's a lot of glue code needed to build these applications. LangChain, created by Harrison, is a powerful open source software package that makes this process easier. Harrison, thank you for working with us on this short course. Can you say a bit more about LangChain was covered in this course?
Absolutely. And thanks Andrew for having me. So excited to be working on this course with you. LangChain is a framework for building applications with language models. It started after I had a bunch of conversations with folks who were building cutting edge and advanced applications and saw some common abstractions that I factored out into a library to make it easier for others to do so as well. In this course you'll be learning about the core abstractions that you can use to build an LLM powered application. These will cover models, prompts and parsers. These are basic building blocks for interacting with inputs and outputs of language models. Memory, which is how you turn the stateless language model into something that you can hold a conversation with so it remembers previous interactions, chains or how you can string together sequences of these operations when calling a language model. Question answering over documents how you can combine your own data with a language model so that it knows more than just the data that it was trained on. Evaluation how do you evaluate these applications that are now powered by this non deterministic model? And agents which involve using the language model as a reasoning engine when interacting with other sources of data and computation. With these building blocks, there is an incredible amount of applications that you can build, often with a relatively few lines.
Of code and that's super exciting. And so an LLM is a very powerful platform to develop on. But instead of calling LLMs yourself and doing all the work to put the application together kind of from scratch on top of the LLM, LangChain provides higher level abstractions that make building these applications easier. LangChain adoption has been growing rapidly and it is also backed by a large community of of open source developers and I think it is one of the most important tools to learn today. If you want to build applications quickly using LLMs. So I hope you enjoyed this short course and use what you learned from it to build some really cool applications.
