LangChain Unleashed 🚀 Build Powerful LLM Apps Using Open-Source Models (Full Tutorial)

Unlock the full power of LangChain and learn how to build real AI applications using open-source LLMs like Llama 2, Mistral 7B, and Hugging Face models.
This video is your complete beginner-friendly guide to understanding LangChain fundamentals, building your first chatbot, adding memory, using RAG (Retrieval-Augmented Generation), and working with agents and tools—without using the OpenAI API.

In just over an hour, you will learn how modern AI apps are built using the same frameworks used by professionals. Whether you’re a Python beginner or an AI enthusiast, this tutorial will give you the foundation needed to build scalable, production-ready LLM applications.

🔥 What You’ll Learn (Highlights)

What LangChain is and how it works

How to generate text using open-source LLMs

LangChain Expression Language (LCEL)

Prompting techniques & one-shot prompting

How to build a chatbot with memory

How RAG works and why it’s essential

Creating embeddings & storing them in ChromaDB

Loading Mistral / Gemma / Llama models

Building agents: XML Agents & ReAct Agents

Running your own LLM server

Practical coding walkthroughs & real demos

This course is perfect for developers, AI builders, data science students, and anyone who wants to work with open-source LLMs instead of paid APIs.

Recommended Placement for Links

🔗 Download course resources

🔗 GitHub project code

🔗 HuggingFace model links

🔗 Kaggle notebook links

Suggested Timestamps

00:00 Introduction

00:45 What is LangChain?

02:00 Setting Up Python Environment

05:00 Generating Text with an LLM

10:00 LangChain Basics

15:00 LCEL Explained

20:00 Creating a Chatbot

25:00 Adding Memory

30:00 What is RAG?

35:00 Creating ChromaDB Database

40:00 Agents & Tools

50:00 Running Mistral/Gemma

60:00 Final Notes

Hashtags

#LangChain #LLM #OpenSourceAI #Mistral7B #Llama2 #HuggingFace #RAG #AIEngineering #PythonAI #AIDevelopment