RankLLMCastorini
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Related Products
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About
BGE (BAAI General Embedding) is a comprehensive retrieval toolkit designed for search and Retrieval-Augmented Generation (RAG) applications. It offers inference, evaluation, and fine-tuning capabilities for embedding models and rerankers, facilitating the development of advanced information retrieval systems. The toolkit includes components such as embedders and rerankers, which can be integrated into RAG pipelines to enhance search relevance and accuracy. BGE supports various retrieval methods, including dense retrieval, multi-vector retrieval, and sparse retrieval, providing flexibility to handle different data types and retrieval scenarios. The models are available through platforms like Hugging Face, and the toolkit provides tutorials and APIs to assist users in implementing and customizing their retrieval systems. By leveraging BGE, developers can build robust and efficient search solutions tailored to their specific needs.
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About
RankLLM is a Python toolkit for reproducible information retrieval research using rerankers, with a focus on listwise reranking. It offers a suite of rerankers, pointwise models like MonoT5, pairwise models like DuoT5, and listwise models compatible with vLLM, SGLang, or TensorRT-LLM. Additionally, it supports RankGPT and RankGemini variants, which are proprietary listwise rerankers. It includes modules for retrieval, reranking, evaluation, and response analysis, facilitating end-to-end workflows. RankLLM integrates with Pyserini for retrieval and provides integrated evaluation for multi-stage pipelines. It also includes a module for detailed analysis of input prompts and LLM responses, addressing reliability concerns with LLM APIs and non-deterministic behavior in Mixture-of-Experts (MoE) models. The toolkit supports various backends, including SGLang and TensorRT-LLM, and is compatible with a wide range of LLMs.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Data scientists and AI researchers looking for a tool to develop sophisticated retrieval systems, offering tools for embedding, reranking, and fine-tuning to enhance search and RAG applications
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Audience
Academic researchers and developers seeking a solution offering tools for implementing and evaluating listwise reranking with large language models
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Supported
In Person
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationBGE
Founded: 2025
United States
bge-model.com/Introduction/index.html
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Company InformationCastorini
Canada
github.com/castorini/rank_llm/
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Categories |
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Integrations
Baseten
Supported
Gemini
Not Supported
Gemini Enterprise
Not Supported
Hugging Face
Supported
Llama
Not Supported
Mistral AI
Not Supported
NVIDIA TensorRT
Not Supported
Nebius Token Factory
Supported
OpenAI
Not Supported
Python
Not Supported
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Integrations
Baseten
Not Supported
Gemini
Supported
Gemini Enterprise
Supported
Hugging Face
Not Supported
Llama
Supported
Mistral AI
Supported
NVIDIA TensorRT
Supported
Nebius Token Factory
Not Supported
OpenAI
Supported
Python
Supported
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