LangChain is an AI-first framework designed to enable developers to create context-aware reasoning applications by linking powerful Large Language Models with external data sources.

We just published a course on the YouTube channel that will teach you all about LangChain. The course will equip you with the cutting-edge skills needed to build a highly knowledgeable chatbot using LangChain Expression Language.

Tom Chant is a popular instructor at Scrimba. In this course, Tom will take you on a journey from the basics of LangChain.js to advanced concepts. You'll delve into an array of topics including embeddings, app flow diagrams, Supabase vector store, text splitting, and much more. The course is structured to make learning LangChain.js approachable and enjoyable, with a focus on practical applications.

The course even includes an introduction to LangChain from Jacob Lee, the lead maintainer of LangChain.js.

In this course, you will learn about:

  • Splitting with a LangChain textSplitter tool
  • Vectorising text chunks
  • Using embeddings models
  • Supabase vector store
  • Templates with input_variables
  • Prompts from templates
  • LangChain Expression Language
  • Basic chains with the .Pipe() method
  • Retrieval from a vector store
  • Complex chains with RunnableSequence()
  • The StringOutputParser() class
  • Troubleshooting performance issues

In this course, you'll learn how to use LangChain.js to build a chatbot that can answer questions on a specific text you give it.

In the first part of the project, you'll learn about using LangChain to split text into chunks, convert the chunks to vectors using an OpenAI embeddings model, and store them together in a Supabase vector store.

Next, you'll learn about chains, which are the building blocks of LangChain. And we do this using LangChain Expression Language. This makes the process of coding in LangChain much smoother and easier to grasp.

Finally, you'll learn about retrieval: using vector matching to select the text chunks from our vector store which are most likely to hold the answer to a user’s query. This enables the chatbot to answer questions specific to your data - a critical skill when working with AI and one of the most common use-cases for AI in web dev.

Watch the full course on the YouTube channel (2-hour watch).