R2R offers production-ready RAG systems with a semi-opinionated framework that prioritizes simplicity and practicality. It aims to set a new industry benchmark for ease of use and effectiveness in deploying, adapting, and maintaining RAG pipelines. The project includes basic examples for application deployment and interaction, as well as a web application for visual intelligence. Key features include rapid deployment, flexible standardization, easy modification, versioning, extensibility, OSS support, and deployment assistance. Core abstractions revolve around the Ingestion Pipeline, Embedding Pipeline, and RAG Pipeline, each incorporating a logging database for operation tracking and observability. Configuration involves setting up cloud provider secrets and environment variables for a smooth setup.
https://github.com/SciPhi-AI/R2R