🎓 Learn RAG implementation with hands-on examples and step-by-step guidance
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!
| 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 |
- Python 3.8+
- Basic understanding of LLMs
- Notebooks familiarity
- OpenAI API key (optional, you can also use local models)
- 📦 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
MIT Licensed. See LICENSE for details.
Happy RAG and ROLL! 🎸