🎸 RAG and ROLL with LlamaIndex - Hands-on Tutorial

🎓 Learn RAG implementation with hands-on examples and step-by-step guidance

📚 What is RAG?

Retrieval-Augmented Generation (RAG) supercharges LLMs by combining them with external knowledge retrieval. Think of it as giving your AI a searchable knowledge base to reference before responding!

🎯 What You'll Master

Topic	Description
Setup	Configure LlamaIndex environment
Data Processing	Build document loaders and indexes
Retrieval	Create efficient search systems
Query Engines	Implement smart query processing
More	...
Real Examples	Practical use cases

📋 Prerequisites

• Python 3.8+
• Basic understanding of LLMs
• Notebooks familiarity
• OpenAI API key (optional, you can also use local models)

🚀 Getting Started

• 📦 Lesson 01: Learn how to load and ingest data, and create your first index.
• 📦 Lesson 02: Discover how to filter and rerank chunks, and build your first chat engine.
• 📦 ... doing

📄 License

MIT Licensed. See LICENSE for details.

Happy RAG and ROLL! 🎸