Welcome to R2R
R2R, short for RAG to Riches, provides the fastest and most efficient way to provide high quality RAG to end users. The framework is built around customizable pipelines and a feature-rich FastAPI implementation.
Key Features
- 🚀 Deploy: Instantly launch production-ready RAG pipelines with streaming capabilities.
- 🧩 Customize: Tailor your pipeline with intuitive configuration files.
- 🔌 Extend: Enhance your pipeline with custom code integrations.
- 🤖 OSS: Benefit from a framework developed by the open-source community, designed to simplify RAG deployment.
- ⚖️ Autoscale: Scale your pipeline effortlessly in the cloud using SciPhi (opens in a new tab).
Why did we build this framework?
R2R was conceived to bridge the gap between local LLM experimentation and scalable production solutions. R2R is to LangChain/LlamaIndex what NextJS is to React.
Demo(s)
Using the cloud application to deploy the pre-built basic pipeline:
Note - the example above uses SciPhi Cloud (opens in a new tab) to pair with the R2R framework for deployment and observability. SciPhi is working to launch a self-hosted version of their cloud platform as R2R matures.
Getting Started
To get started with R2R, we recommend setting up the framework and following an initial example.
- Quick Install: Quick setup using
pip. - Basic example: A basic example to get you started with R2R with a RAG pipeline in a server and a client to interact with it.
- Configure Your Pipeline: A quick overview of how to configure your pipeline with
config.json. - Customize Your Pipeline: A brief introduction of ways to customize your RAG pipeline in code.
Community
Joining our Discord server (opens in a new tab) is the fastest way to get support and connect with both the R2R team and other developers in the community. Whether you're encountering issues, looking for advice on best practices, or just want to share your experiences, we're here to help.