Raudaschl/rag-fusion

Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.

A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥. Our toolkit integrates 40 pre-retrieved benchmark datasets and supports 7+ retrieval techniques, 24+ state-of-the-art Reranking models, and multiple RAG methods.

⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)

Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models

XRAG: eXamining the Core - Benchmarking Foundational Component Modules in Advanced Retrieval-Augmented Generation

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.