LLM-RAG Tutorial This repository contains a comprehensive tutorial on building a Retrieval-Augmented Generation (RAG) pipeline using Large Language Models (LLMs). The tutorial provides code examples, detailed explanations, and a streamlined approach to integrate LLMs with a retrieval system for improved response generation.
📚 Contents • notebooks/: Jupyter notebooks with step-by-step explanations. • data/: Example datasets used in the tutorial. • src/: Python modules for custom retrieval and generation components. • config/: Configuration files for model parameters and settings.
🚀 Getting Started Prerequisites • Python 3.10+ • transformers library • faiss or chromadb for the retrieval component Install dependencies using: pip install -r requirements.txt
Example The RAG pipeline combines a dense retriever (e.g., FAISS) with a generative model (e.g., OpenAI GPT). The example notebooks walk through:
- Indexing documents using FAISS/ChromaDB.
- Querying the indexed documents.
- Generating responses with an LLM using the retrieved context.