# rerankers: Unified API for Reranking and Cross-Encoder Models

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rerankers is a lightweight, low-dependency Python library that provides a unified API for various reranking and cross-encoder models. It simplifies the integration of different reranking approaches into retrieval architectures, offering a consistent interface for diverse models like cross-encoders, RankGPT, T5, and API-based rerankers. This library aims to make reranking more accessible and easier to implement for developers.

GitHub: https://github.com/AnswerDotAI/rerankers
OSRepos URL: https://osrepos.com/repo/answerdotai-rerankers

## Summary

rerankers is a lightweight, low-dependency Python library that provides a unified API for various reranking and cross-encoder models. It simplifies the integration of different reranking approaches into retrieval architectures, offering a consistent interface for diverse models like cross-encoders, RankGPT, T5, and API-based rerankers. This library aims to make reranking more accessible and easier to implement for developers.

## Topics

- Python
- Reranking
- NLP
- Information Retrieval
- Machine Learning
- API
- Cross-Encoder
- LLM

## Repository Information

Last analyzed by OSRepos: Sat Jul 04 2026 12:31:06 GMT+0100 (Western European Summer Time)
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## Content

## Introduction

`rerankers` is a lightweight, low-dependency Python library developed by Answer.AI that offers a unified API for integrating various reranking and cross-encoder models into your applications. Its primary goal is to simplify the use of diverse reranking approaches, providing a consistent interface regardless of the underlying model architecture. This makes it easier for developers to experiment with and deploy different rerankers in their retrieval pipelines.

## Installation

The core `rerankers` package is designed to be dependency-free by default, avoiding conflicts with your existing environment. You can then install specific dependencies based on the models you intend to use.

sh
# Core package only, will require other dependencies already installed
pip install rerankers

# All transformers-based approaches (cross-encoders, t5, colbert)
pip install "rerankers[transformers]"

# RankGPT
pip install "rerankers[gpt]"

# API-based rerankers (Cohere, Jina, MixedBread, Pinecone, Isaacus)
pip install "rerankers[api]"

# FlashRank rerankers (ONNX-optimised, very fast on CPU)
pip install "rerankers[flashrank]"

# RankLLM rerankers (better RankGPT + support for local models such as RankZephyr and RankVicuna)
# Note: RankLLM is only supported on Python 3.10+! This will not work with Python 3.9
pip install "rerankers[rankllm]"

# To support Multi-Modal rerankers such as MonoQwen2-VL and other MonoVLM models, which require flash-attention, peft, accelerate, and recent versions of `transformers`
pip install "rerankers[monovlm]"

# To support LLM-Layerwise rerankers (which need flash-attention installed)
pip install "rerankers[llmlayerwise]"

# All of the above
pip install "rerankers[all]"


## Examples

Using `rerankers` is straightforward. You can load any supported reranker with a single line of code and then use its `rank` method to reorder documents based on a query.

python
from rerankers import Reranker, Document

# Load a default cross-encoder
ranker = Reranker('cross-encoder')

# Load a specific cross-encoder model
ranker = Reranker('mixedbread-ai/mxbai-rerank-large-v1', model_type='cross-encoder')

# Load an API-based reranker (e.g., Cohere)
# ranker = Reranker("cohere", lang='en', api_key = "YOUR_API_KEY")

# Load RankGPT
# ranker = Reranker("rankgpt", api_key = "YOUR_API_KEY")

# Define your query and documents
query = "I love you"
docs = [
    Document(text="I really like you", doc_id=0, metadata={'source': 'twitter'}),
    Document(text="I hate you", doc_id=1, metadata={'source': 'reddit'})
]

# Rank the documents
results = ranker.rank(query=query, docs=docs)

# Print the ranked results
print(results)
# Example output:
# RankedResults(results=[Result(document=Document(text='I really like you', doc_id=0, metadata={'source': 'twitter'}), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=1, metadata={'source': 'reddit'}), score=-4.14453125, rank=2)], query='I love you', has_scores=True)

# Access top k results
top_result = results.top_k(1)[0]
print(top_result.text) # 'I really like you'
print(top_result.document.metadata) # {'source': 'twitter'}


## Why use rerankers?

Rerankers are a crucial component in modern retrieval architectures, yet their implementation can often be complex and fragmented. Different reranking methods, from traditional cross-encoders to advanced LLM-based approaches like RankGPT, often reside in separate libraries with inconsistent APIs and varying levels of documentation. This creates a significant barrier to entry for developers and makes it challenging to compare and integrate different models.

`rerankers` addresses these issues by providing a simple, unified API. It aims to be:

*   **Lightweight**: Ships with only essential dependencies.
*   **Easy-to-understand**: Offers a minimal set of calls to learn, enabling access to a wide range of models.
*   **Easy-to-integrate**: Designed to fit seamlessly into existing pipelines with minimal code changes.
*   **Easy-to-expand**: New reranking models can be added with little effort, requiring only a class with a `rank()` function.

By centralizing access to various reranking models, `rerankers` empowers developers to efficiently build and optimize their information retrieval systems.

## Links

*   **GitHub Repository**: [AnswerDotAI/rerankers](https://github.com/AnswerDotAI/rerankers)
*   **arXiv Paper**: [rerankers: A Lightweight Python Library to Unify Ranking Methods](https://arxiv.org/abs/2408.17344)