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The most advanced AI retrieval system. Containerized, Retrieval-Augmented Generation (RAG) with a RESTful API.

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r2r

Containerized, state of the art Retrieval-Augmented Generation (RAG) with a RESTful API

About

R2R (RAG to Riches) is the most advanced AI retrieval system, supporting Retrieval-Augmented Generation (RAG) with production-ready features. Built around a containerized RESTful API, R2R offers multimodal content ingestion, hybrid search functionality, configurable GraphRAG, and comprehensive user and document management.

For a more complete view of R2R, check out the full documentation.

Key Features

  • Release 3.3.0Ā Ā Ā Ā December 3, 2024Ā Ā Ā Ā 

    Warning: These changes are breaking!

Install with pip

The recommended way to get started with R2R is by using our CLI.

pip install r2r

You may run R2R directly from the python package, but additional dependencies like Postgres+pgvector must be configured and the full R2R core is required:

# export OPENAI_API_KEY=sk-...
# export POSTGRES...
pip install 'r2r[core,ingestion-bundle]'
r2r --config-name=default serve

Alternatively, R2R can be launched alongside its requirements inside Docker:

# export OPENAI_API_KEY=sk-...
r2r serve --docker --full

The command above will install the full installation which includes Hatchet for orchestration and Unstructured.io for parsing.

Getting Started

  • Installation: Quick installation of R2R using Docker or pip
  • Quickstart: A quick introduction to R2R's core features
  • Setup: Learn how to setup and configure R2R
  • API & SDKs: API reference and Python/JS SDKs for interacting with R2R

Cookbooks

Community

Join our Discord server 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.

Contributing

We welcome contributions of all sizes! Here's how you can help:

Our Contributors