In this video, you’ll learn how to use LangChain , a powerful framework for building applications powered by large language models (LLMs). Whether you're new to LangChain or looking to deepen your understanding, this tutorial is designed to help you get started.
We'll focus on Chat Models , one of the foundational building blocks of LangChain. You'll learn:
How to send messages to LLMs like OpenAI, Mistral, AWS, Grok, and more using LangChain's chat_model functionality.
The flexibility of switching between different LLM providers without changing your code.
How to securely manage API keys using .env files.
How to structure system and human messages to set context and ask questions effectively.
By the end of this video, you'll be able to:
Load and interact with different LLMs using LangChain.
Send system and human messages to manage conversations with LLMs.
Standardize your code for seamless integration with multiple LLMs.
This video focuses on practical coding examples, including how to invoke chat models, handle responses, and work with system and human messages. Whether you're using Jupyter Notebook or an IDE like IntelliJ, this guide will walk you through the process step-by-step.
Stay tuned for the next video, where we'll dive deeper into Prompt Templates —another key component of LangChain!
Perfect for developers who want to build scalable, flexible AI-powered applications. Don’t forget to subscribe for more tutorials on AI, LangChain, and LLMs! 🚀
0:35 (35s) - Introduction to Video Topics
1:11 (71s) - Loading the Chat Model in LangChain
2:42 (162s) - Getting a Free Mistral API Key
4:36 (276s) - Configuring API Keys in Environment
7:37 (457s) - Sending a Message with the Chat Model
10:55 (655s) - Switching Between LLMs in LangChain
13:49 (829s) - Running Code with Mistral Model
15:43 (943s) - Rate Limiting with Free Mistral API
17:20 (1040s) - System and Human Messages in LangChain
21:52 (1312s) - Benefits of LangChain Standardization