# \[ANN\] FlashRank.jl: Fast and Efficient Document Ranking

**URL:** https://discourse.julialang.org/t/ann-flashrank-jl-fast-and-efficient-document-ranking/116756
**Category:** Package Announcements
**Tags:** announcement, ranking, genai, rag
**Created:** [July 8, 2024, 8:00am UTC](https://discourse.julialang.org/t/ann-flashrank-jl-fast-and-efficient-document-ranking/116756 "2024-07-08T08:00:17Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![svilupp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/svilupp/32/34933_2.png) [@svilupp](https://discourse.julialang.org/u/svilupp)
#### Post date: [July 8, 2024, 8:00am UTC](https://discourse.julialang.org/t/ann-flashrank-jl-fast-and-efficient-document-ranking/116756/1 "2024-07-08T08:00:18Z")

</div>

**Introducing [FlashRank.jl](https://github.com/svilupp/FlashRank.jl): Fast and Efficient Document Ranking**

We are excited to announce the release of [FlashRank.jl](https://github.com/svilupp/FlashRank.jl), a Julia package that provides a fast and efficient way to rank documents relevant to a given query without requiring GPUs or large dependencies. It’s heavily inspired by [Python’s FlashRank](https://github.com/PrithivirajDamodaran/FlashRank).

**Key Features**

- Four ranking models with varying sizes and accuracy
- Lightweight dependencies for ease of integration
- Fast ranking speeds, with the smallest model capable of ranking 100 documents in ~0.1 seconds on a laptop

**Getting Started**

To get started with [FlashRank.jl](https://github.com/svilupp/FlashRank.jl), simply add it to your Julia environment using `Pkg.add("FlashRank")`. Import the package and you can use the `RankerModel` to rank documents for a given query.

**Example Usage**

```julia
using FlashRank

ranker = RankerModel()
query = "How to speedup LLMs?"
passages = ["...", "...", "..."]
result = rank(ranker, query, passages)

```

**Integrated with PromptingTools.jl**

FlashRank.jl is designed to work seamlessly with PromptingTools.jl, allowing you to easily integrate it into your Retrieval Augmented Generation (RAG) pipelines. See our documentation for more details.

**Try it out!**

Give [FlashRank.jl](https://github.com/svilupp/FlashRank.jl) a try today and experience fast and efficient document ranking for your RAG pipelines!