R2R – Open-source framework for production-grade RAG
Hello HN, I'm Owen from SciPhi (https://www.sciphi.ai/), a startup working on simplifying˛Retrieval-Augmented Generation (RAG). Today we’re excited to share R2R (https://github.com/SciPhi-AI/R2R), an open-source framework that makes it simpler to develop and deploy production-grade RAG systems. Just a quick reminder: RAG helps Large Language Models (LLMs) use current information and specific knowledge. For example, it allows a programming assistant to use your latest documents to answer questions. The idea is to gather all the relevant information…
In plain words
R2R is an open-source framework for building and deploying production-grade Retrieval-Augmented Generation (RAG) systems. It simplifies RAG development by addressing common deployment challenges that typically require complex abstractions. The framework enables large language models to answer questions using current documents and specific knowledge bases, making it suitable for developers building applications like intelligent programming assistants that need access to up-to-date information.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hello HN, I'm Owen from SciPhi (https://www.sciphi.ai/), a startup working on simplifying˛Retrieval-Augmented Generation (RAG). Today we’re excited to share R2R (https://github.com/SciPhi-AI/R2R), an open-source framework that makes it simpler to develop and deploy production-grade RAG systems. Just a quick reminder: RAG helps Large Language Models (LLMs) use current information and specific knowledge. For example, it allows a programming assistant to use your latest documents to answer questions. The idea is to gather all the relevant information ("retrieval") and present it to the LLM with a question ("augmentation"). This way, the LLM can provide answers (“generation”) as though it was trained directly on your data. The R2R framework is a powerful tool for addressing key challenges in deploying RAG systems, avoiding the complex abstractions common in other projects. Through conversations with numerous developers, we discovered that many were independently developing similar solutions. R2R distinguishes itself by adopting a straightforward approach to streamline the setup, monitoring, and upgrading of RAG systems. Specifically, it focuses on reducing unnecessary complexity and enhancing the visibility and tracking of system performance. The key parts of R2R include: an Ingestion Pipeline that transforms different data types (like json, txt, pdf, html) into 'Documents' ready for embedding. Next, the Embedding Pipeline takes text and turns it into vector embeddings through various processes (such as extracting text, transforming it, chunking, and embedding). Finally, the RAG Pipeline follows the steps of the embedding pipeline but adds an LLM provider to create text completions. R2R is currently in use at several companies building applications from B2B lead generation to educational tools for consumers. Our GitHub repo (https://github.com/SciPhi-AI/R2R) includes basic examples for application deployment and standalone use, demonstrating the framework's adaptability in a simple way. We’d love for you to give R2R a try, and welcome your feedback and comments as we refine and develop it further!
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, February 2024
the whole month →
MarketingIdeas.com▲1,230Never run out of marketing ideas ever again
Growth · 2024 · marketingideas.com
