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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 Ducky?

Ducky is an AI-powered search platform designed to simplify and accelerate product development. It provides a single unified solution for indexing, retrieval, and ranking across all content types. Developers can deploy AI search within minutes using intuitive APIs and SDKs. The platform supports multimodal search across text, images, and PDFs. Automated chunking and multi-stage reranking ensure high-quality results without manual tuning. Metadata filtering enables precise, structured queries for complex use cases. Ducky integrates seamlessly with modern AI agents and language models. Built-in context filtering reduces unnecessary token usage and lowers operational costs. The system improves relevance automatically based on usage patterns. Search results include source attribution for accuracy and trust. Zero infrastructure setup is required. Ducky helps teams ship reliable AI features faster with minimal effort.

Media

Media

Integrations Supported

Airtable
Confluence
Gemini
Gemini Enterprise
Google Drive
Gorgias
Help Scout
HubSpot Customer Platform
Jira
Linear
Llama
Mistral AI
NVIDIA TensorRT
Notion
OpenAI
Python
Qwen
RankGPT
Slack
Zendesk

Integrations Supported

Airtable
Confluence
Gemini
Gemini Enterprise
Google Drive
Gorgias
Help Scout
HubSpot Customer Platform
Jira
Linear
Llama
Mistral AI
NVIDIA TensorRT
Notion
OpenAI
Python
Qwen
RankGPT
Slack
Zendesk

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

Ducky

Company Website

www.ducky.ai/

Categories and Features

Categories and Features

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