🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥

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If you like our Framework, don't hesitate to ⭐ star this repository ⭐. This helps us to make the Framework more better and scalable to different models and methods 🤗.

A modular and efficient retrieval, reranking and RAG framework designed to work with state-of-the-art models for retrieval, ranking and rag tasks.

🚀 Demo

To run the demo locally:

# Make sure Rankify is installed
pip install streamlit

# Then run the demo
streamlit run demo.py

https://github.com/user-attachments/assets/13184943-55db-4f0c-b509-fde920b809bc

🎉News

[2026-02-16] Huge thanks to @JamieHoldcroft for integrating 15+ new dense retrievers, including SOTA LLM-based bi-encoders (SFR, E5, GritLM) and reasoning-augmented models (RaDeR, ReasonIR, ReasonEmbed, BGE-Reasoner).
[2025-10-14] Updated installation with optional extras: retriever, reranking, rag, and all.
[2025-10-14] New CLI (rankify-index) syntax & examples for BM25, DPR, ANCE, Contriever, ColBERT, BGE.
[2025-06-11] Many thanks to @tobias124 for implementing Indexing for Custom Dataset.
[2025-06-01] Many thanks to @aherzinger for implementing and refactoring the Generator and RAG models.
[2025-05-30] Huge thanks to @baraayusry for implementing the Online Retriever using CrawAI and ReACT.
[2025-02-10] Released reranking-datasets and reranking-datasets-light on Hugging Face.
[2025-02-04] Our paper is released on arXiv.

🔧 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 cmd

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

Recommended Installation

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:

# Retrieval stack (BM25, dense retrievers, web tools)
pip install "rankify[retriever]"

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

# RAG endpoints (OpenAI, LiteLLM, vLLM clients)
pip install "rankify[rag]"

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]"
# RAG endpoints (OpenAI, LiteLLM, vLLM clients)
pip install -e ".[rag]"

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/*

:rocket: Quick Start

🚀 One-Line Pipeline API (Recommended)

The simplest way to use Rankify - HuggingFace-style one-line interface:

from rankify import pipeline

# Create a RAG pipeline with intelligent defaults
rag = pipeline("rag")
answers = rag("What is machine learning?", documents)

# Or customize your configuration
rag = pipeline(
    "rag",
    retriever="bge",           # State-of-the-art dense retriever
    reranker="flashrank",      # Ultra-fast reranker
    generator="basic-rag"
)

Available Pipeline Types:

pipeline("search") - Document retrieval only
pipeline("rerank") - Retrieve + rerank
pipeline("rag") - Full RAG pipeline (retrieve + rerank + generate)

📖 Pipeline API Documentation

🤖 RankifyAgent - AI-Powered Model Selection

Let AI help you choose the best models for your use case:

from

Rankify

Install / Use

README