Installation Guide

This guide provides detailed instructions for installing the atlas-rag package and its dependencies.

Requirements

  • Python: 3.9 or higher

  • Operating Systems: Linux, macOS, Windows

Prerequisites

Before installing atlas-rag, you must install PyTorch and FAISS manually to ensure hardware compatibility.

1. Install PyTorch

Please visit the official PyTorch website to get the installation command appropriate for your system.

Example for Linux:

# For CPU-only systems
pip install torch torchvision torchaudio

# For systems with NVIDIA GPUs (adjust CUDA version as needed)
pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu126

2. Install FAISS

You need to install either the CPU or GPU version of FAISS: (Recommend to install faiss with CUDA-12.6)

# For CPU-only systems
pip install faiss-cpu

# For systems with NVIDIA GPUs (adjust CUDA version as needed)
conda install -c pytorch -c rapidsai -c rapidsai-nightly -c conda-forge -c nvidia pytorch/label/nightly::faiss-gpu-cuvs 'cuda-version=12.6'

Basic Installation

Install from PyPI

The simplest way to install atlas-rag is via pip:

pip install atlas-rag

This will install the core package with all required dependencies.

Optional Dependencies

NV-Embed-v2 Support

If you need support for NVIDIA’s NV-embed-v2 model, install the package with the nvembed extra:

pip install atlas-rag[nvembed]

This installs compatible versions of transformers (>=4.42.4, <=4.47.1) and sentence-transformers (2.7.0) required for NV-embed-v2.

Verification

After installation, verify that the package is installed correctly:

import atlas_rag
print(atlas_rag.__version__)

You can also verify the installation of key components:

from atlas_rag.kg_construction.triple_extraction import KnowledgeGraphExtractor
from atlas_rag.llm_generator import LLMGenerator
from atlas_rag.kg_construction.triple_config import ProcessingConfig
print("Installation successful!")

Development Installation

If you want to contribute to the project or install from source:

  1. Clone the repository:

git clone https://github.com/HKUST-KnowComp/AutoSchemaKG.git
git checkout release/v0.0.5 # checkout to your desired branch
cd AutoSchemaKG

  1. Install in development mode:

pip install -e .

Next Steps

After successful installation:

  1. Check out the Quick Start Guide to begin using atlas-rag

  2. Explore Examples for advance use cases

Support

If you encounter any installation issues:

  • Check the GitHub Issues

  • Contact the maintainers:

    • Dennis Hong Ting TSANG: httsangaj@connect.ust.hk

    • Jiaxin Bai: jbai@connect.ust.hk

    • Haoyu Huang: haoyuhuang@link.cuhk.edu.hk