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🎸 RAG and ROLL with LlamaIndex - Hands-on Tutorial

Python 3.8+ LlamaIndex License: MIT

🎓 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! 🎸

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🎸 Hands-on tutorial for building RAG applications with LlamaIndex

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