Project Title

R2R — State-of-the-Art AI Retrieval System with Agentic RAG and RESTful API

Overview

R2R is a cutting-edge AI retrieval system that employs Retrieval-Augmented Generation (RAG) and provides a RESTful API for seamless integration. It stands out for its multimodal content ingestion, hybrid search capabilities, knowledge graph support, and comprehensive document management. R2R also includes a Deep Research API, enhancing the system's ability to fetch relevant data and deliver context-aware answers for complex queries.

Key Features

Multimodal Ingestion: Supports parsing of various file formats including .txt, .pdf, .json, .png, .mp3, and more.
Hybrid Search: Combines semantic and keyword search with reciprocal rank fusion for effective retrieval.
Knowledge Graphs: Automatically extracts entities and relationships to enhance data understanding.
Agentic RAG: Integrates a reasoning agent for more sophisticated query handling and response generation.

Use Cases

Enterprise Search: Companies can use R2R to manage and retrieve information from large document databases efficiently.
Research and Development: Researchers can leverage R2R's Deep Research API to gather comprehensive data for complex analyses.
Customer Support: R2R can be employed to provide context-aware answers to customer queries, improving service quality.

Advantages

Advanced Retrieval Techniques: Utilizes the latest in AI technology for retrieval, including RAG.
RESTful API: Facilitates easy integration with existing systems and applications.
Comprehensive Document Management: Offers robust tools for document ingestion, storage, and retrieval.

Limitations / Considerations

Complexity: The system's advanced features may require a steeper learning curve for some users.
Customization: While highly configurable, specific use cases may require additional development work.

Haystack: An open-source NLP framework for building search systems, differing in its focus on simplicity and modularity.
Elasticsearch: A widely-used search and analytics engine, offering a different approach with a focus on scalability and real-time data.
Qdrant: A vector database designed for building semantic search solutions, differing in its emphasis on vector search capabilities.

Basic Information

GitHub: R2R
Stars: 7,438
License: Unknown
Last Commit: 2025-11-13

📊 Project Information

Project Name: R2R
GitHub URL: https://github.com/SciPhi-AI/R2R
Programming Language: Python
⭐ Stars: 7,438
🍴 Forks: 614
📅 Created: 2024-02-12
🔄 Last Updated: 2025-11-13

🏷️ Project Topics

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📚 Documentation

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R2R

Project Description