LangChain4j for Beginners
A course for building AI applications with LangChain4j and Azure OpenAI GPT-5.2, from basic chat to AI agents.
Table of Contents
- Introduction - Learn the fundamentals of LangChain4j
- Prompt Engineering - Master effective prompt design
- RAG (Retrieval-Augmented Generation) - Build intelligent knowledge-based systems
- Tools - Integrate external tools and simple assistants
- MCP (Model Context Protocol) - Work with the Model Context Protocol (MCP) and Agentic modules
Video Walkthroughs
Each module has a companion live session where we walk through the concepts and code step by step.
| Module | Video |
|---|---|
| 01 - Introduction | Getting Started with LangChain4j |
| 02 - Prompt Engineering | Prompt Engineering with LangChain4j |
| 03 - RAG | RAG with LangChain4j |
| 04 - Tools & 05 - MCP | AI Agents with Tools and MCP |
Learning Path
New to LangChain4j? Check out the Glossary for definitions of key terms and concepts.
Quick Start
- Fork this repository to your GitHub account
- Click Code → Codespaces tab → ... → New with options...
- Use the defaults – this will select the Development container created for this course
- Click Create codespace
- Wait 5-10 minutes for the environment to be ready
- Jump straight to Introduction to get started!
After completing the modules, explore the Testing Guide to see LangChain4j testing concepts in action.
Note: This training uses Azure OpenAI. Get started with a FREE Azure account if you don't have one.
Learning with GitHub Copilot
To quickly start coding, open this project in a GitHub Codespace or your local IDE with the provided devcontainer. The devcontainer used in this course comes pre-configured with GitHub Copilot for AI paired programming.
Each code example includes suggested questions you can ask GitHub Copilot to deepen your understanding. Look for the 💡/🤖 prompts in:
- Java file headers - Questions specific to each example
- Module READMEs - Exploration prompts after code examples
How to use: Open any code file and ask Copilot the suggested questions. It has full context of the codebase and can explain, extend, and suggest alternatives.
Want to learn more? Check out Copilot for AI Paired Programming.