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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.
What is BGE?
BGE, or BAAI General Embedding, functions as a comprehensive toolkit designed to enhance search performance and support Retrieval-Augmented Generation (RAG) applications. It includes features for model inference, evaluation, and fine-tuning of both embedding models and rerankers, facilitating the development of advanced information retrieval systems. Among its key components are embedders and rerankers, which can seamlessly integrate into RAG workflows, leading to marked improvements in the relevance and accuracy of search outputs. BGE supports a range of retrieval strategies, such as dense retrieval, multi-vector retrieval, and sparse retrieval, which enables it to adjust to various data types and retrieval scenarios. Users can conveniently access these models through platforms like Hugging Face, and the toolkit provides an array of tutorials and APIs for efficient implementation and customization of retrieval systems. By leveraging BGE, developers can create resilient and high-performance search solutions tailored to their specific needs, ultimately enhancing the overall user experience and satisfaction. Additionally, the inherent flexibility of BGE guarantees its capability to adapt to new technologies and methodologies as they emerge within the data retrieval field, ensuring its continued relevance and effectiveness. This adaptability not only meets current demands but also anticipates future trends in information retrieval.
Integrations Supported
Hugging Face
Baseten
ChatGPT
Claude
Cohere
GPT-3.5
GPT-4
LiteLLM
Llama 2
NVIDIA TensorRT
Integrations Supported
Hugging Face
Baseten
ChatGPT
Claude
Cohere
GPT-3.5
GPT-4
LiteLLM
Llama 2
NVIDIA TensorRT
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
Weiwei Sun
Company Location
United States
Company Website
github.com/sunnweiwei/RankGPT
Company Facts
Organization Name
BGE
Date Founded
2025
Company Location
United States
Company Website
bge-model.com/Introduction/index.html