https://github.com/NirDiamant/RAG_Techniques.git

Advanced RAG Techniques: Comprehensive Research Guide

Based on my deep research of the NirDiamant/RAG_Techniques repository, I've compiled a comprehensive guide covering 36 advanced RAG techniques. This repository is one of the most extensive RAG resources available, with over 22,600 stars on GitHub.

Overview

The repository contains 36 distinct RAG techniques organized into 9 categories, ranging from foundational approaches to cutting-edge advanced architectures. Each technique includes implementation notebooks (primarily using LangChain, with some LlamaIndex support) that can be run directly in Google Colab.

🌱 Foundational RAG Techniques (5 Techniques)

1. Simple RAG

Explanation: The most basic RAG implementation that introduces core concepts. It splits documents into chunks, creates embeddings, stores them in a vector database, and retrieves relevant chunks based on user queries to generate responses.

Code Links:

• LangChain: simple_rag.ipynb
• LlamaIndex: Available (check repository)

When to Use:

• Starting your RAG journey and learning the basics
• Building simple Q&A systems
• Rapid prototyping
• Educational purposes and demonstrations
• When you need a quick proof-of-concept

Key Benefits: Easy implementation, minimal setup requirements, fast to deploy, excellent learning foundation.

2. RAG with CSV Files

Explanation: Specialized RAG implementation designed to work with structured tabular data from CSV files. It creates a retrieval system that can query and answer questions about data in spreadsheet format.