R2R is a production-ready, agentic retrieval-augmented generation (RAG) system designed for AI developers and researchers. It provides a RESTful API for seamless integration into applications, enabling state-of-the-art retrieval capabilities to enhance large language models (LLMs).

How It Works

R2R employs an agentic approach, allowing the retrieval system to dynamically decide what information to fetch and when, rather than relying on static queries. This is achieved through a sophisticated pipeline that orchestrates LLM-driven decision-making with efficient vector database lookups and data processing. The system is built for scalability and performance, optimizing the retrieval process for complex, real-world data.

Quick Start & Requirements

• Install via pip: pip install r2r
• Requires Python 3.10+ and a PostgreSQL database with the pgvector extension.
• Docker Compose is recommended for easy setup and management of dependencies.
• Official documentation: https://r2r.readthedocs.io/en/latest/

Highlighted Details

• State-of-the-art RAG performance with agentic capabilities.
• Production-ready with a focus on scalability and reliability.
• RESTful API for easy integration.
• Supports various data sources and embedding models.

Maintenance & Community

• Actively maintained by SciPhi AI.
• Community support available via Discord: https://discord.gg/sciphi-ai

Licensing & Compatibility

• Apache 2.0 License.
• Permissive license allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The system is designed for advanced users and requires familiarity with RAG concepts, LLMs, and database management. While production-ready, extensive customization may be needed for highly specialized use cases.