Andrew and Jacob Lee teach JavaScript developers how to build LLM apps with LangChain.js. The course covers core building blocks and a hands-on RAG workflow, including load/search/synthesize pipelines, streaming, error handling, and the LangChain expression language.
Welcome. According to GitHub's recent report on the state of open source software, JavaScript was once again the most popular programming language. As generative AI applications move from exploration to application, many developers, including many web and mobile developers, will want to build generative AI apps in JavaScript. LangChain is an important orchestration framework for generative AI and is used by many teams for coordinating workflows using large language models. Happily, it supports JavaScript. I'm delighted to introduce our new short course Build ALM Apps with LangChain js. The instructor for this course, Jacob Lee, is a founding engineer at LangChain and also lead maintainer for LangChain JS. He's collaborated daily with JavaScript developers help them integrate alms into many web applications.
Thanks Andrew, and it's been a pleasure working with you. I'm looking forward to showing you how to incorporate large language models into your applications. We have seen increasing interest in using JavaScript to build with LLMs, and we're really at a unique moment in time where technology that was once the domain of a small number of machine learning experts has now become accessible to the broader developer community. I'm excited specifically for the new opportunities for web devs to leverage their unique skills and creativity to build great things. LangChain JS is seeing about a million downloads a month and after less than a year is approaching 10,000 GitHub stars.
In this course you practice using the fundamental building blocks of LangChain to build a very popular class of LLM application retrieval, augmented generation or rag. This is frequently useful for a lot of business use cases where you might have a lot of text data. For example, let's say you have a long detailed PDF of public company financial filings and you'd like to make that data more easily queryable for market research. The steps in an LLM workflow will typically include loading the document, searching for the most relevant passages, and then prompting the LLM to synthesize its data in an informative and human readable way. LangChain JS can help you implement all this much more efficiently using LangChain modules such as data loaders, prompts, models, vector stores and parsers.
Absolutely, and our goal in designing this course was to take you through the basics of building LLM based applications here using LangChain js. Along the way you'll explore the building blocks Andrew mentioned, as well as how to use LangChain expression language to tie them together. This makes writing applications quick and intuitive, as well as supporting streaming logging, error handling and running things in parallel out of the box.
So lots of exciting things to learn. I hope you have a good reaction to this course, and if you're a Java Shift developer, I know that you enjoy this.
