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Installation

🔧 Installation

Set up the virtual environment

First, create and activate a conda environment with Python 3.10:

conda create -n rankify python=3.10
conda activate rankify

Install PyTorch 2.5.1

We recommend installing Rankify with PyTorch 2.5.1 for Rankify. Refer to the PyTorch installation page for platform-specific installation commands.

If you have access to GPUs, it's recommended to install the CUDA version 12.4 or 12.6 of PyTorch, as many of the evaluation metrics are optimized for GPU use.

To install Pytorch 2.5.1 you can install it from the following command: 

pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124

Basic Installation

To install Rankify, simply use pip (requires Python 3.10+):

pip install rankify
This will install the base functionality required for retrieval, re-ranking, and retrieval-augmented generation (RAG).

For full functionality, we recommend installing Rankify with all dependencies: 

pip install "rankify[all]"
This ensures you have all necessary modules, including retrieval, re-ranking, and RAG support.

Optional Dependencies

If you prefer to install only specific components, choose from the following: 

# Install dependencies for retrieval only (BM25, DPR, ANCE, etc.)
pip install "rankify[retriever]"

# Install base re-ranking with vLLM support for `FirstModelReranker`, `LiT5ScoreReranker`, `LiT5DistillReranker`, `VicunaReranker`, and `ZephyrReranker'.
pip install "rankify[reranking]"

Or, to install from GitHub for the latest development version:

git clone https://github.com/DataScienceUIBK/rankify.git
cd rankify
pip install -e .
# For full functionality we recommend installing Rankify with all dependencies:
pip install -e ".[all]"
# Install dependencies for retrieval only (BM25, DPR, ANCE, etc.)
pip install -e ".[retriever]"
# Install base re-ranking with vLLM support for `FirstModelReranker`, `LiT5ScoreReranker`, `LiT5DistillReranker`, `VicunaReranker`, and `ZephyrReranker'.
pip install -e ".[reranking]"

Using ColBERT Retriever

If you want to use ColBERT Retriever, follow these additional setup steps: 

# Install GCC and required libraries
conda install -c conda-forge gcc=9.4.0 gxx=9.4.0
conda install -c conda-forge libstdcxx-ng

# Export necessary environment variables
export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
export CC=gcc
export CXX=g++
export PATH=$CONDA_PREFIX/bin:$PATH

# Clear cached torch extensions
rm -rf ~/.cache/torch_extensions/*