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What is RankLLM?

RankLLM is an advanced Python framework aimed at improving reproducibility within the realm of information retrieval research, with a specific emphasis on listwise reranking methods. The toolkit boasts a wide selection of rerankers, such as pointwise models exemplified by MonoT5, pairwise models like DuoT5, and efficient listwise models that are compatible with systems including vLLM, SGLang, or TensorRT-LLM. Additionally, it includes specialized iterations like RankGPT and RankGemini, which are proprietary listwise rerankers engineered for superior performance. The toolkit is equipped with vital components for retrieval processes, reranking activities, evaluation measures, and response analysis, facilitating smooth end-to-end workflows for users. Moreover, RankLLM's synergy with Pyserini enhances retrieval efficiency and guarantees integrated evaluation for intricate multi-stage pipelines, making the research process more cohesive. It also features a dedicated module designed for thorough analysis of input prompts and LLM outputs, addressing reliability challenges that can arise with LLM APIs and the variable behavior of Mixture-of-Experts (MoE) models. The versatility of RankLLM is further highlighted by its support for various backends, including SGLang and TensorRT-LLM, ensuring it works seamlessly with a broad spectrum of LLMs, which makes it an adaptable option for researchers in this domain. This adaptability empowers researchers to explore diverse model setups and strategies, ultimately pushing the boundaries of what information retrieval systems can achieve while encouraging innovative solutions to emerging challenges.

What is RankGPT?

RankGPT is a Python toolkit meticulously designed to explore the utilization of generative Large Language Models (LLMs), such as ChatGPT and GPT-4, to enhance relevance ranking in Information Retrieval (IR) systems. It introduces cutting-edge methods, including instructional permutation generation and a sliding window approach, which enable LLMs to efficiently reorder documents. The toolkit supports a variety of LLMs—including GPT-3.5, GPT-4, Claude, Cohere, and Llama2 via LiteLLM—providing extensive modules for retrieval, reranking, evaluation, and response analysis, which streamline the entire process from start to finish. Additionally, it includes a specialized module for in-depth examination of input prompts and outputs from LLMs, addressing reliability challenges related to LLM APIs and the unpredictable nature of Mixture-of-Experts (MoE) models. Moreover, RankGPT is engineered to function with multiple backends, such as SGLang and TensorRT-LLM, ensuring compatibility with a wide range of LLMs. Among its impressive features, the Model Zoo within RankGPT displays various models, including LiT5 and MonoT5, conveniently hosted on Hugging Face, facilitating easy access and implementation for users in their projects. This toolkit not only empowers researchers and developers but also opens up new avenues for improving the efficiency of information retrieval systems through state-of-the-art LLM techniques. Ultimately, RankGPT stands out as an essential resource for anyone looking to push the boundaries of what is possible in the realm of information retrieval.

Media

Media

Integrations Supported

NVIDIA TensorRT
Python
ChatGPT
Claude
Cohere
GPT-3.5
GPT-4
Gemini
Gemini Enterprise
Hugging Face
LiteLLM
Llama
Llama 2
Mistral AI
OpenAI
Qwen
RankGPT
RankLLM

Integrations Supported

NVIDIA TensorRT
Python
ChatGPT
Claude
Cohere
GPT-3.5
GPT-4
Gemini
Gemini Enterprise
Hugging Face
LiteLLM
Llama
Llama 2
Mistral AI
OpenAI
Qwen
RankGPT
RankLLM

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Castorini

Company Location

Canada

Company Website

github.com/castorini/rank_llm/

Company Facts

Organization Name

Weiwei Sun

Company Location

United States

Company Website

github.com/sunnweiwei/RankGPT

Categories and Features

Categories and Features

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