Introduction

Build, scale, and manage user-facing Retrieval-Augmented Generation applications.

r2r

R2R (RAG to Riches), the Elasticsearch for RAG, bridges the gap between experimenting with and deploying state of the art Retrieval-Augmented Generation (RAG) applications. Itโ€™s a complete platform that helps you quickly build and launch scalable RAG solutions. Built around a containerized RESTful API, R2R offers multimodal ingestion support, hybrid search, GraphRAG, user & document management, and observability / analytics features.

Key Features

Getting Started

  • Installation: Quick installation of R2R using Docker or pip
  • Quickstart: A quick introduction to R2Rโ€™s core features
  • Setup: Learn how to setup and configure R2R

API & SDKs

  • SDK: API reference and Python/JS SDKs for interacting with R2R
  • API: API reference and Python/JS SDKs for interacting with R2R
  • Configuration: A guide on how to configure your R2R system
  • SciPhi Website: Explore a managed AI solution powered by R2R.
  • Contact Us: Get in touch with our team to discuss your specific needs.

Cookbooks

Community

Join our Discord server to get support and connect with both the R2R team and other developers in the community. Whether youโ€™re encountering issues, looking for advice on best practices, or just want to share your experiences, weโ€™re here to help.