Welcome to RAGHub, a living collection of new and emerging frameworks, projects, and resources in the Retrieval-Augmented Generation (RAG) ecosystem. This is a community-driven project for r/RAG, where we aim to catalog the rapid growth of RAG tools and projects that are pushing the boundaries of the field.
Each day, it feels like a new tool or framework emerges, and choosing the right one is becoming more of an art than a science. Is the framework from three months ago still relevant? Or was it just hype, rehashing old concepts with a fresh look? RAGHub exists to help you stay ahead of these changes, providing a platform for the latest innovations in RAG.
If you're looking for proven, mainstream RAG frameworks and techniques, check out the excellent repository by Nir Diamant: RAG Techniques. This repository focuses on more established tools and methods that have already gained traction in the community.
| Framework Name | Description | Website | Reddit Post | Tags |
|---|---|---|---|---|
| LangChain | A framework for building applications with LLMs. | Visit Site | Reddit Discussion | Python, RAG |
| Haystack | A framework for building search engines using neural networks. | Visit Site | Reddit Discussion | Python, RAG, NLP |
| Framework Name | Description | Website | GitHub Link | Tags |
|---|---|---|---|---|
| Trulens | TruLens is a software tool that helps you objectively measure and enhance the quality of LLM-based applications using feedback functions, enabling faster and scalable evaluation for use cases like question answering, summarization, retrieval-augmented generation, and agent-based applications. | Visit Site | Github | Python, RAG |
| ragas | Ragas is a framework for evaluating and quantifying the performance of Retrieval Augmented Generation (RAG) pipelines, which use external data to enhance the LLM’s context. | Visit Site | Github | Python, RAG, NLP |
| Framework Name | Description | GitHub Link | Tags |
|---|---|---|---|
| rag-citation | RAG Citation is an project that combines Retrieval-Augmented Generation (RAG) with automatic citation generation. This tool is designed to enhance the credibility of RAG-generated content by providing relevant citations for the information used in generating responses. | Github | Python, RAG |
| Framework Name | Description | Website | GitHub Link | Tags |
|---|---|---|---|---|
| R2R | R2R (RAG to Riches), the Elasticsearch for RAG is a complete platform that helps you quickly build and launch scalable RAG solutions | Visit Site | Github | Python, RAG, Graph |
| RAGFlow | RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. | Visit Site | Github | Python, RAG |
| Project Name | Description | GitHub Link | Tags |
|---|---|---|---|
| contextual-doc-retrieval-opneai-reranker | Python-based system leveraging OpenAI GPT-4o and Cohere for re-ranking and query expansion, combined with BM25 for accurate document retrieval. It parses PDFs, chunks content contextually, and enhances search precision with AI-powered contextual understanding and re-ranking. | GitHub | Python, RAG |
| Site/Article | Description | Link |
|---|---|---|
| RAG_Techniques | Showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. | Read More |
| GenAI_Agents | Visit Site |
This is a community project, and we welcome contributions from everyone! If you’d like to add a new framework, project, or resource, please check out our Contribution Guidelines for details on how to get started.
This project is licensed under the MIT License. See the LICENSE file for details.
This project is part of the r/RAG community. Have feedback or suggestions? Feel free to open an issue, start a discussion, or join the conversation on our Discord server! We want to make this repository a valuable resource for everyone exploring the RAG ecosystem, and your input is crucial.