UltraRAG is a comprehensive framework for building and optimizing Retrieval-Augmented Generation (RAG) systems, targeting researchers and developers. It offers a one-stop solution for automated knowledge adaptation, simplifying data construction, model fine-tuning, and inference evaluation, with a particular focus on domain-specific RAG applications.

How It Works

UltraRAG employs a modular architecture with three layers: Backend (components like knowledge base, retrieval, generation models), Workflow (standard RAG patterns and proprietary methods like Adaptive-Note, VisRAG), and Function (data synthesis, evaluation, fine-tuning). It supports microservice deployment for key services and provides a user-friendly frontend for resource management and function access. This layered approach allows for flexible customization and integration of cutting-edge RAG techniques.

Quick Start & Requirements

• Deployment: Docker (docker-compose up --build -d) or Conda (conda create -n ultrarag python=3.10, conda activate ultrarag, pip install -r requirements.txt).
• Prerequisites: CUDA 12.2+, Python 3.10+.
• Model Download: python scripts/download_model.py.
• WebUI: Access at http://localhost:8843.
• Demo: streamlit run ultrarag/webui/webui.py --server.fileWatcherType none.
• Documentation: User Guide, UltraRAG Series.

Highlighted Details

• No-code WebUI for full-link RAG setup and optimization, including multimodal RAG (VisRAG).
• One-click data construction and retrieval with proprietary methods like KBAlign and RAG-DDR.
• Multidimensional, multi-stage evaluation using the RAGEval method for robust assessment.
• Research-friendly integration of proprietary methods and support for module-level exploration.

Maintenance & Community

Developed by a collaboration including THUNLP (Tsinghua University) and NEUIR (Northeastern University). New contributors are welcome.

Licensing & Compatibility

• License: Apache-2.0.
• Compatibility: Permissive license suitable for commercial use and closed-source integration.

Limitations & Caveats

The framework is research-oriented and integrates several proprietary methods, which may require understanding their specific implementations for full utilization. Performance claims are based on specific domain evaluations (legal field).