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First Steps With LangChain (Summary)

Martin Breuss
First Steps With LangChain Martin Breuss 02:04
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You’ve successfully designed, built, and extended LangChain chains. This introduction prepared you to continue exploring LangChain and building your LLM-powered apps. Make sure to check out the written step-by-step tutorial this course was based on to learn more techniques.

In this video course, you’ve learned how to:

  • Use LangChain to build LLM-powered applications
  • Create reusable instructions with prompt templates
  • Create and extend LangChain chains
  • Debug what happens when a chain executes

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00:00 Congrats for making it to the end of this course. I hope you had a fun time exploring LangChain and interacting with large language models.

00:08 In this course, you learned how to use chat models with LangChain, how to work with prompts and prompt templates, which was one of the central points of this course is how you can make your prompts reusable and maintainable.

00:21 And the second central point was how you can build chains using LangChain, which allows you to build flexible workflows. You’ve also seen how you can dive into what’s going on behind the scenes when LangChain calls one of these chains, and how you can inspect the inputs and outputs using the debug mode.

00:40 With this, you’re set up well with the basics of how to work with LangChain. Note that there’s a lot more that you can learn about this library, and I encourage you to keep going and explore all the available options that you have. For this, I have a couple of additional resources for you that you can check out.

00:57 The first one is about “Embeddings and Vector Databases With ChromaDB. This walks you through an example of how you can use a vector database to gather the context that you then pass to any sort of calls to your language model.

01:11 Next, there’s a tutorial about how to build a retrieval augmented generation chatbot using LangChain. This starts off with some of the topics that you’ve covered in this course, but goes a lot deeper and shows you how you can combine your knowledge of LangChain and add on top of it also the retrieval from a vector database, as well as the setting up agents that can make decisions on which functions to call, and brings it all together to build a hospital chatbot.

01:39 And finally, there’s also LangGraph, which allows you to build stateful AI agents using Python and LangGraph builds on top of LangChain. So these are a bunch of resources where you can go very deep, and you can learn more about how to interact with language models using Python’s LangChain Library.

01:57 I hope you had fun and learned something. Thanks for watching. My name is Martin, and hope to see you around at Real Python.

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