In this tutorial, I will teach you LangChain as efficiently as possible by breaking down the framework into seven key components you need to understand to start developing more advanced LLM applications.
LangChain is an open-source framework that simplifies the development of applications using large language models (LLMs). It provides a unified interface for interacting with various LLMs, making building chatbots, question-answering systems, and other AI-powered applications easier.
In this tutorial, I'll teach you LangChain in 7 easy steps. You will learn the core features of the framework and we will cover:
Why you want to learn LangChain
Composing chains with prompt templates and loaders
The runnable protocol and LangChain expression language
Creating vectorstore-backed retrievers
Building RAG chains
Using tools with chains
Building agents with access to tools
🚀
By the end of the tutorial, you will have a solid foundation for diving deeper into the LangChain ecosystem and start testing, deploying, and building advanced Multi Agent systems with LangGraph.
Colab Notebook & Interactive Map
This post is for subscribers only
Sign up now to read the post and get access to the full library of posts for subscribers only.
