Introduction
Build, scale, and manage user-facing Retrieval-Augmented Generation applications.
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. It’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 management, and observability / analytics features.
Key Features
- 📁 Multimodal Ingestion: Parse
.txt,.pdf,.json,.png,.mp3, and more. - 🔍 Hybrid Search: Combine semantic and keyword search with reciprocal rank fusion for enhanced relevancy.
- 🔗 Graph RAG: Automatically extract relationships and build knowledge graphs.
- 🗂️ App Management: Efficiently manage documents and users with full authentication.
- 🔭 Observability: Observe and analyze your RAG engine performance.
- 🧩 Configurable: Provision your application using intuitive configuration files.
- 🖥️ Dashboard: An open-source React+Next.js app with optional authentication, to interact with R2R via GUI.
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
- 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
Cookbooks
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Advanced RAG Pipelines
- RAG Agent: R2R’s powerful RAG agent
- Hybrid Search: Introduction to hybrid search
- Advanced RAG: Advanced RAG features
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Knowledge Graphs
- GraphRAG: Walkthrough of GraphRAG
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Orchestration
- GraphRAG: R2R event orchestration
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Auth & Admin Features
- Web Development: Building webapps using R2R
- User Auth: Authenticating users
- Collections: Document collections
- Analytics & Observability: End-to-end logging and analytics
- Web Application: Connecting with the R2R Application