

# <p align="center"><img src="./images/rankify-crop.png" width="300" style ="border-radius: 50;"/></p>
### <div align="center">🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥<div>


<div align="center">
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_A modular and efficient retrieval, reranking  and RAG  framework designed to work with state-of-the-art models for retrieval, ranking and rag tasks._

_Rankify is a Python toolkit designed for unified retrieval, re-ranking, and retrieval-augmented generation (RAG) research. Our toolkit integrates 40 pre-retrieved benchmark datasets and supports 7 retrieval techniques, 24 state-of-the-art re-ranking models, and multiple RAG methods. Rankify provides a modular and extensible framework, enabling seamless experimentation and benchmarking across retrieval pipelines. Comprehensive documentation, open-source implementation, and pre-built evaluation tools make Rankify a powerful resource for researchers and practitioners in the field._

<p align="center">
<img src="images/overview.png" width="700" height="900" >
</p>

## :sparkles: Features

- **Comprehensive Retrieval & Reranking Framework**: Rankify unifies retrieval, re-ranking, and retrieval-augmented generation (RAG) into a single modular Python toolkit, enabling seamless experimentation and benchmarking.  

- **Extensive Dataset Support**: Includes **40 benchmark datasets** with **pre-retrieved documents**, covering diverse domains such as **question answering, dialogue, entity linking, and fact verification**.  

- **Diverse Retriever Integration**: Supports **7 retrieval techniques**, including **BM25, DPR, ANCE, BPR, ColBERT, BGE, and Contriever**, providing flexibility for various retrieval strategies.  

- **Advanced Re-ranking Models**: Implements **24 primary re-ranking models** with **41 sub-methods**, covering **pointwise, pairwise, and listwise** re-ranking approaches for enhanced ranking performance.  

- **Prebuilt Retrieval Indices**: Provides **precomputed Wikipedia and MS MARCO corpora** for multiple retrieval models, eliminating indexing overhead and accelerating experiments.  

- **Seamless RAG Integration**: Bridges retrieval and generative models (e.g., **GPT, LLAMA, T5**), enabling retrieval-augmented generation with **zero-shot**, **Fusion-in-Decoder (FiD)**, and **in-context learning** strategies.  

- **Modular & Extensible Design**: Easily integrates custom datasets, retrievers, re-rankers, and generation models using Rankify’s structured Python API.  

- **Comprehensive Evaluation Suite**: Offers **automated performance evaluation** with **retrieval, ranking, and RAG metrics**, ensuring reproducible benchmarking.  

- **User-Friendly Documentation**: Detailed **[📖 online documentation](http://rankify.readthedocs.io/)**, example notebooks, and tutorials for easy adoption.  

## 🔍 Roadmap  

**Rankify** is still under development, and this is our first release (**v0.1.0**). While it already supports a wide range of retrieval, re-ranking, and RAG techniques, we are actively enhancing its capabilities by adding more retrievers, rankers, datasets, and features.  

### 🚀 Planned Improvements  

- **Retrievers**  
  - [x] Support for **BM25, DPR, ANCE, BPR, ColBERT, BGE, and Contriever**  
  - [ ] Add missing retrievers: **Spar, MSS, MSS-DPR**  
  - [ ] Enable **custom index loading** and support for user-defined retrieval corpora  

- **Re-Rankers**  
  - [x] 24 primary re-ranking models with 41 sub-methods  
  - [ ] Expand the list by adding **more advanced ranking models** 

- **Datasets**  
  - [x] 40 benchmark datasets for retrieval, ranking, and RAG  
  - [ ] Add **more datasets**  
  - [ ] Support for **custom dataset integration**  

- **Retrieval-Augmented Generation (RAG)**  
  - [x] Integration with **GPT, LLAMA, and T5**  
  - [ ] Extend support for **more generative models**   

- **Evaluation & Usability**  
  - [x] Standard retrieval and ranking evaluation metrics (Top-K, EM, Recall, ...)
  - [ ] Add **advanced evaluation metrics** (NDCG, MAP for retriever )  

- **Pipeline Integration**  
  - [ ] **Add a pipeline module** for streamlined retrieval, re-ranking, and RAG workflows 

## 🔧 Installation  

#### Set up the virtual environment
First, create and activate a conda environment with Python 3.10:

```bash
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](https://pytorch.org/get-started/previous-versions/) 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
```bash
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+):  
```base
pip install rankify
```

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

#### Recommended Installation  

For full functionality, we **recommend installing Rankify with all dependencies**:
```bash
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:
```bash
# Install dependencies for retrieval only (BM25, DPR, ANCE, etc.)
pip install "rankify[retriever]"

# Install dependencies for base re-ranking only (excluding vLLM)
pip install "rankify[base]"

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

# Install dependencies for retrieval-augmented generation (RAG)
pip install "rankify[rag]"
```

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


```bash
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 dependencies for base re-ranking only (excluding vLLM)
pip install -e ".[base]"
# Install base re-ranking with vLLM support for `FirstModelReranker`, `LiT5ScoreReranker`, `LiT5DistillReranker`, `VicunaReranker`, and `ZephyrReranker'.
pip install -e ".[reranking]"
# Install dependencies for retrieval-augmented generation (RAG)
pip install -e ".[rag]"
```



#### Using ColBERT Retriever  

If you want to use **ColBERT Retriever**, follow these additional setup steps:
```bash
# 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
```
```bash
# 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

### **1️⃣. Pre-retrieved Datasets**  

We provide **1,000 pre-retrieved documents per dataset**, which you can download from:  

🔗 **[Hugging Face Dataset Repository](https://huggingface.co/datasets/abdoelsayed/reranking-datasets-light)**  

#### **Dataset Format**  

The pre-retrieved documents are structured as follows:
```json
[
    {
        "question": "...",
        "answers": ["...", "...", ...],
        "ctxs": [
            {
                "id": "...",         // Passage ID from database TSV file
                "score": "...",      // Retriever score
                "has_answer": true|false  // Whether the passage contains the answer
            }
        ]
    }
]
```


#### **Access Datasets in Rankify**  

You can **easily download and use pre-retrieved datasets** through **Rankify**.  

#### **List Available Datasets**  

To see all available datasets:
```python
from rankify.dataset.dataset import Dataset 

# Display available datasets
Dataset.avaiable_dataset()
```

**BM25 Retriever**
```python
from rankify.dataset.dataset import Dataset
# Download BM25-retrieved documents for nq-dev
dataset = Dataset(retriever="bm25", dataset_name="nq-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for 2wikimultihopqa-dev
dataset = Dataset(retriever="bm25", dataset_name="2wikimultihopqa-train", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for archivialqa-dev
dataset = Dataset(retriever="bm25", dataset_name="archivialqa-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for archivialqa-test
dataset = Dataset(retriever="bm25", dataset_name="archivialqa-test", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for chroniclingamericaqa-test
dataset = Dataset(retriever="bm25", dataset_name="chroniclingamericaqa-test", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for chroniclingamericaqa-dev
dataset = Dataset(retriever="bm25", dataset_name="chroniclingamericaqa-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for entityquestions-test
dataset = Dataset(retriever="bm25", dataset_name="entityquestions-test", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for ambig_qa-dev
dataset = Dataset(retriever="bm25", dataset_name="ambig_qa-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for ambig_qa-train
dataset = Dataset(retriever="bm25", dataset_name="ambig_qa-train", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for arc-test
dataset = Dataset(retriever="bm25", dataset_name="arc-test", n_docs=100)
documents = dataset.download(force_download=False)
# Download BM25-retrieved documents for arc-dev
dataset = Dataset(retriever="bm25", dataset_name="arc-dev", n_docs=100)
documents = dataset.download(force_download=False)
```

**BGE Retriever**
```python
from rankify.dataset.dataset import Dataset
# Download BGE-retrieved documents for nq-dev
dataset = Dataset(retriever="bge", dataset_name="nq-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download BGE-retrieved documents for 2wikimultihopqa-dev
dataset = Dataset(retriever="bge", dataset_name="2wikimultihopqa-train", n_docs=100)
documents = dataset.download(force_download=False)
# Download BGE-retrieved documents for archivialqa-dev
dataset = Dataset(retriever="bge", dataset_name="archivialqa-dev", n_docs=100)
documents = dataset.download(force_download=False)
```

**ColBERT Retriever**


```python
from rankify.dataset.dataset import Dataset
# Download ColBERT-retrieved documents for nq-dev
dataset = Dataset(retriever="colbert", dataset_name="nq-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download ColBERT-retrieved documents for 2wikimultihopqa-dev
dataset = Dataset(retriever="colbert", dataset_name="2wikimultihopqa-train", n_docs=100)
documents = dataset.download(force_download=False)
# Download ColBERT-retrieved documents for archivialqa-dev
dataset = Dataset(retriever="colbert", dataset_name="archivialqa-dev", n_docs=100)
documents = dataset.download(force_download=False)
```

**MSS-DPR Retriever**


```python
from rankify.dataset.dataset import Dataset
# Download MSS-DPR-retrieved documents for nq-dev
dataset = Dataset(retriever="mss-dpr", dataset_name="nq-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download MSS-DPR-retrieved documents for 2wikimultihopqa-dev
dataset = Dataset(retriever="mss-dpr", dataset_name="2wikimultihopqa-train", n_docs=100)
documents = dataset.download(force_download=False)
# Download MSS-DPR-retrieved documents for archivialqa-dev
dataset = Dataset(retriever="mss-dpr", dataset_name="archivialqa-dev", n_docs=100)
documents = dataset.download(force_download=False)
```

**MSS Retriever**

```python
from rankify.dataset.dataset import Dataset
# Download MSS-retrieved documents for nq-dev
dataset = Dataset(retriever="mss", dataset_name="nq-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download MSS-retrieved documents for 2wikimultihopqa-dev
dataset = Dataset(retriever="mss", dataset_name="2wikimultihopqa-train", n_docs=100)
documents = dataset.download(force_download=False)
# Download MSS-retrieved documents for archivialqa-dev
dataset = Dataset(retriever="mss", dataset_name="archivialqa-dev", n_docs=100)
documents = dataset.download(force_download=False)
```

**Contriever Retriever**

```python
from rankify.dataset.dataset import Dataset
# Download MSS-retrieved documents for nq-dev
dataset = Dataset(retriever="contriever", dataset_name="nq-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download MSS-retrieved documents for 2wikimultihopqa-dev
dataset = Dataset(retriever="contriever", dataset_name="2wikimultihopqa-train", n_docs=100)
documents = dataset.download(force_download=False)
# Download MSS-retrieved documents for archivialqa-dev
dataset = Dataset(retriever="contriever", dataset_name="archivialqa-dev", n_docs=100)
documents = dataset.download(force_download=False)
```


**ANCE Retriever**

```python
from rankify.dataset.dataset import Dataset
# Download ANCE-retrieved documents for nq-dev
dataset = Dataset(retriever="ance", dataset_name="nq-dev", n_docs=100)
documents = dataset.download(force_download=False)
# Download ANCE-retrieved documents for 2wikimultihopqa-dev
dataset = Dataset(retriever="ance", dataset_name="2wikimultihopqa-train", n_docs=100)
documents = dataset.download(force_download=False)
# Download ANCE-retrieved documents for archivialqa-dev
dataset = Dataset(retriever="ance", dataset_name="archivialqa-dev", n_docs=100)
documents = dataset.download(force_download=False)
```

**Load Pre-retrieved Dataset from File**  

If you have already downloaded a dataset, you can load it directly:
```python
from rankify.dataset.dataset import Dataset

# Load pre-downloaded BM25 dataset for WebQuestions
documents = Dataset.load_dataset('./tests/out-datasets/bm25/web_questions/test.json', 100)
```
Now, you can integrate **retrieved documents** with **re-ranking** and **RAG** workflows! 🚀  

---

### 2️⃣. Running Retrieval
To perform retrieval using **Rankify**, you can choose from various retrieval methods such as **BM25, DPR, ANCE, Contriever, ColBERT, and BGE**.  

**Example: Running Retrieval on Sample Queries**  
```python
from rankify.dataset.dataset import Document, Question, Answer, Context
from rankify.retrievers.retriever import Retriever

# Sample Documents
documents = [
    Document(question=Question("the cast of a good day to die hard?"), answers=Answer([
            "Jai Courtney",
            "Sebastian Koch",
            "Radivoje Bukvić",
            "Yuliya Snigir",
            "Sergei Kolesnikov",
            "Mary Elizabeth Winstead",
            "Bruce Willis"
        ]), contexts=[]),
    Document(question=Question("Who wrote Hamlet?"), answers=Answer(["Shakespeare"]), contexts=[])
]
```

```python
# BM25 retrieval on Wikipedia
bm25_retriever_wiki = Retriever(method="bm25", n_docs=5, index_type="wiki")

# BM25 retrieval on MS MARCO
bm25_retriever_msmacro = Retriever(method="bm25", n_docs=5, index_type="msmarco")


# DPR (multi-encoder) retrieval on Wikipedia
dpr_retriever_wiki = Retriever(method="dpr", model="dpr-multi", n_docs=5, index_type="wiki")

# DPR (multi-encoder) retrieval on MS MARCO
dpr_retriever_msmacro = Retriever(method="dpr", model="dpr-multi", n_docs=5, index_type="msmarco")

# DPR (single-encoder) retrieval on Wikipedia
dpr_retriever_wiki = Retriever(method="dpr", model="dpr-single", n_docs=5, index_type="wiki")

# DPR (single-encoder) retrieval on MS MARCO
dpr_retriever_msmacro = Retriever(method="dpr", model="dpr-single", n_docs=5, index_type="msmarco")

# ANCE retrieval on Wikipedia
ance_retriever_wiki = Retriever(method="ance", model="ance-multi", n_docs=5, index_type="wiki")

# ANCE retrieval on MS MARCO
ance_retriever_msmacro = Retriever(method="ance", model="ance-multi", n_docs=5, index_type="msmarco")


# Contriever retrieval on Wikipedia
contriever_retriever_wiki = Retriever(method="contriever", model="facebook/contriever-msmarco", n_docs=5, index_type="wiki")

# Contriever retrieval on MS MARCO
contriever_retriever_msmacro = Retriever(method="contriever", model="facebook/contriever-msmarco", n_docs=5, index_type="msmarco")


# ColBERT retrieval on Wikipedia
colbert_retriever_wiki = Retriever(method="colbert", model="colbert-ir/colbertv2.0", n_docs=5, index_type="wiki")

# ColBERT retrieval on MS MARCO
colbert_retriever_msmacro = Retriever(method="colbert", model="colbert-ir/colbertv2.0", n_docs=5, index_type="msmarco")


# BGE retrieval on Wikipedia
bge_retriever_wiki = Retriever(method="bge", model="BAAI/bge-large-en-v1.5", n_docs=5, index_type="wiki")

# BGE retrieval on MS MARCO
bge_retriever_msmacro = Retriever(method="bge", model="BAAI/bge-large-en-v1.5", n_docs=5, index_type="msmarco")
```

**Running Retrieval**

After defining the retriever, you can retrieve documents using:
```python
retrieved_documents = bm25_retriever_wiki.retrieve(documents)

for i, doc in enumerate(retrieved_documents):
    print(f"\nDocument {i+1}:")
    print(doc)
```

---
## 3️⃣. Running Reranking
Rankify provides support for multiple reranking models. Below are examples of how to use each model.  

** Example: Reranking a Document**  
```python
from rankify.dataset.dataset import Document, Question, Answer, Context
from rankify.models.reranking import Reranking

# Sample document setup
question = Question("When did Thomas Edison invent the light bulb?")
answers = Answer(["1879"])
contexts = [
    Context(text="Lightning strike at Seoul National University", id=1),
    Context(text="Thomas Edison tried to invent a device for cars but failed", id=2),
    Context(text="Coffee is good for diet", id=3),
    Context(text="Thomas Edison invented the light bulb in 1879", id=4),
    Context(text="Thomas Edison worked with electricity", id=5),
]
document = Document(question=question, answers=answers, contexts=contexts)

# Initialize the reranker
reranker = Reranking(method="monot5", model_name="monot5-base-msmarco")

# Apply reranking
reranker.rank([document])

# Print reordered contexts
for context in document.reorder_contexts:
    print(f"  - {context.text}")
```


**Examples of Using Different Reranking Models**  
```python
# UPR
model = Reranking(method='upr', model_name='t5-base')

# API-Based Rerankers
model = Reranking(method='apiranker', model_name='voyage', api_key='your-api-key')
model = Reranking(method='apiranker', model_name='jina', api_key='your-api-key')
model = Reranking(method='apiranker', model_name='mixedbread.ai', api_key='your-api-key')

# Blender Reranker
model = Reranking(method='blender_reranker', model_name='PairRM')

# ColBERT Reranker
model = Reranking(method='colbert_ranker', model_name='Colbert')

# EchoRank
model = Reranking(method='echorank', model_name='flan-t5-large')

# First Ranker
model = Reranking(method='first_ranker', model_name='base')

# FlashRank
model = Reranking(method='flashrank', model_name='ms-marco-TinyBERT-L-2-v2')

# InContext Reranker
Reranking(method='incontext_reranker', model_name='llamav3.1-8b')

# InRanker
model = Reranking(method='inranker', model_name='inranker-small')

# ListT5
model = Reranking(method='listt5', model_name='listt5-base')

# LiT5 Distill
model = Reranking(method='lit5distill', model_name='LiT5-Distill-base')

# LiT5 Score
model = Reranking(method='lit5score', model_name='LiT5-Distill-base')

# LLM Layerwise Ranker
model = Reranking(method='llm_layerwise_ranker', model_name='bge-multilingual-gemma2')

# LLM2Vec
model = Reranking(method='llm2vec', model_name='Meta-Llama-31-8B')

# MonoBERT
model = Reranking(method='monobert', model_name='monobert-large')

# MonoT5
Reranking(method='monot5', model_name='monot5-base-msmarco')

# RankGPT
model = Reranking(method='rankgpt', model_name='llamav3.1-8b')

# RankGPT API
model = Reranking(method='rankgpt-api', model_name='gpt-3.5', api_key="gpt-api-key")
model = Reranking(method='rankgpt-api', model_name='gpt-4', api_key="gpt-api-key")
model = Reranking(method='rankgpt-api', model_name='llamav3.1-8b', api_key="together-api-key")
model = Reranking(method='rankgpt-api', model_name='claude-3-5', api_key="claude-api-key")

# RankT5
model = Reranking(method='rankt5', model_name='rankt5-base')

# Sentence Transformer Reranker
model = Reranking(method='sentence_transformer_reranker', model_name='all-MiniLM-L6-v2')
model = Reranking(method='sentence_transformer_reranker', model_name='gtr-t5-base')
model = Reranking(method='sentence_transformer_reranker', model_name='sentence-t5-base')
model = Reranking(method='sentence_transformer_reranker', model_name='distilbert-multilingual-nli-stsb-quora-ranking')
model = Reranking(method='sentence_transformer_reranker', model_name='msmarco-bert-co-condensor')

# SPLADE
model = Reranking(method='splade', model_name='splade-cocondenser')

# Transformer Ranker
model = Reranking(method='transformer_ranker', model_name='mxbai-rerank-xsmall')
model = Reranking(method='transformer_ranker', model_name='bge-reranker-base')
model = Reranking(method='transformer_ranker', model_name='bce-reranker-base')
model = Reranking(method='transformer_ranker', model_name='jina-reranker-tiny')
model = Reranking(method='transformer_ranker', model_name='gte-multilingual-reranker-base')
model = Reranking(method='transformer_ranker', model_name='nli-deberta-v3-large')
model = Reranking(method='transformer_ranker', model_name='ms-marco-TinyBERT-L-6')
model = Reranking(method='transformer_ranker', model_name='msmarco-MiniLM-L12-en-de-v1')

# TwoLAR
model = Reranking(method='twolar', model_name='twolar-xl')

# Vicuna Reranker
model = Reranking(method='vicuna_reranker', model_name='rank_vicuna_7b_v1')

# Zephyr Reranker
model = Reranking(method='zephyr_reranker', model_name='rank_zephyr_7b_v1_full')
```
---

## 4️⃣. Using Generator Module
Rankify provides a **Generator Module** to facilitate **retrieval-augmented generation (RAG)** by integrating retrieved documents into generative models for producing answers. Below is an example of how to use different generator methods.  

```python
from rankify.dataset.dataset import Document, Question, Answer, Context
from rankify.generator.generator import Generator

# Define question and answer
question = Question("What is the capital of France?")
answers = Answer(["Paris"])
contexts = [
    Context(id=1, title="France", text="The capital of France is Paris.", score=0.9),
    Context(id=2, title="Germany", text="Berlin is the capital of Germany.", score=0.5)
]

# Construct document
doc = Document(question=question, answers=answers, contexts=contexts)

# Initialize Generator (e.g., Meta Llama)
generator = Generator(method="in-context-ralm", model_name='meta-llama/Llama-3.1-8B')

# Generate answer
generated_answers = generator.generate([doc])
print(generated_answers)  # Output: ["Paris"]
```

---
## 5️⃣ Evaluating with Metrics  

Rankify provides built-in **evaluation metrics** for **retrieval, re-ranking, and retrieval-augmented generation (RAG)**. These metrics help assess the quality of retrieved documents, the effectiveness of ranking models, and the accuracy of generated answers.  

**Evaluating Generated Answers**  

You can evaluate the quality of **retrieval-augmented generation (RAG) results** by comparing generated answers with ground-truth answers.
```python
from rankify.metrics.metrics import Metrics
from rankify.dataset.dataset import Dataset

# Load dataset
dataset = Dataset('bm25', 'nq-test', 100)
documents = dataset.download(force_download=False)

# Initialize Generator
generator = Generator(method="in-context-ralm", model_name='meta-llama/Llama-3.1-8B')

# Generate answers
generated_answers = generator.generate(documents)

# Evaluate generated answers
metrics = Metrics(documents)
print(metrics.calculate_generation_metrics(generated_answers))
```

**Evaluating Retrieval Performance**  

```python
# Calculate retrieval metrics before reranking
metrics = Metrics(documents)
before_ranking_metrics = metrics.calculate_retrieval_metrics(ks=[1, 5, 10, 20, 50, 100], use_reordered=False)

print(before_ranking_metrics)
```

**Evaluating Reranked Results**  
```python
# Calculate retrieval metrics after reranking
after_ranking_metrics = metrics.calculate_retrieval_metrics(ks=[1, 5, 10, 20, 50, 100], use_reordered=True)
print(after_ranking_metrics)
```

## 📜 Supported Models


### **1️⃣ Retrievers**  
- ✅ **BM25**
- ✅ **DPR** 
- ✅ **ColBERT**   
- ✅ **ANCE**
- ✅ **BGE** 
- ✅ **Contriever** 
- ✅ **BPR** 
---

### **2️⃣ Rerankers**  

- ✅ **Cross-Encoders** 
- ✅ **RankGPT**
- ✅ **RankGPT-API** 
- ✅ **MonoT5**
- ✅ **MonoBert**
- ✅ **RankT5** 
- ✅ **ListT5** 
- ✅ **LiT5Score**
- ✅ **LiT5Dist**
- ✅ **Vicuna Reranker**
- ✅ **Zephyr Reranker**
- ✅ **Sentence Transformer-based** 
- ✅ **FlashRank Models**  
- ✅ **API-Based Rerankers**  
- ✅ **ColBERT Reranker**
- ✅ **LLM Layerwise Ranker** 
- ✅ **Splade Reranker**
- ✅ **UPR Reranker**
- ✅ **Inranker Reranker**
- ✅ **Transformer Reranker**
- ✅ **FIRST Reranker**
- ✅ **Blender Reranker**
- ✅ **LLM2VEC Reranker**
- ✅ **ECHO Reranker**
- ✅ **Incontext Reranker**
-  📍 **DynRank**
-  📍 **ASRank**
---

### **3️⃣ Generators**  
- ✅ **Fusion-in-Decoder (FiD) with T5**
- ✅ **In-Context Learning RLAM** 
---

## 📖 Documentation

For full API documentation, visit the [Rankify Docs](http://rankify.readthedocs.io/).

---

## 💡 Contributing


Follow these steps to get involved:

1. **Fork this repository** to your GitHub account.

2. **Create a new branch** for your feature or fix:

   ```bash
   git checkout -b feature/YourFeatureName
   ```

3. **Make your changes** and **commit them**:

   ```bash
   git commit -m "Add YourFeatureName"
   ```

4. **Push the changes** to your branch:

   ```bash
   git push origin feature/YourFeatureName
   ```

5. **Submit a Pull Request** to propose your changes.

Thank you for helping make this project better!

---


## :bookmark: License

Rankify is licensed under the Apache-2.0 License - see the [LICENSE](https://opensource.org/license/apache-2-0) file for details.

## 🙏 Acknowledgments  

We would like to express our gratitude to the following libraries, which have greatly contributed to the development of **Rankify**:  

- **Rerankers** – A powerful Python library for integrating various reranking methods.  
  🔗 [GitHub Repository](https://github.com/AnswerDotAI/rerankers/tree/main)  

- **Pyserini** – A toolkit for supporting BM25-based retrieval and integration with sparse/dense retrievers.  
  🔗 [GitHub Repository](https://github.com/castorini/pyserini)  

- **FlashRAG** – A modular framework for Retrieval-Augmented Generation (RAG) research.  
  🔗 [GitHub Repository](https://github.com/RUC-NLPIR/FlashRAG)  




## :star2: Citation

Please kindly cite our paper if helps your research:

```BibTex
@article{abdallah2025rankify,
  title={Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation},
  author={Abdallah, Abdelrahman and Mozafari, Jamshid and Piryani, Bhawna and Ali, Mohammed and Jatowt, Adam},
  journal={arXiv preprint arXiv:2502.02464},
  year={2025}
}
```