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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 Pinecone Rerank v0?

Pinecone Rerank V0 is a specialized cross-encoder model aimed at boosting accuracy in reranking tasks, which significantly benefits enterprise search and retrieval-augmented generation (RAG) systems. By processing queries and documents concurrently, this model evaluates detailed relevance and provides a relevance score on a scale of 0 to 1 for each combination of query and document. It supports a maximum context length of 512 tokens, ensuring consistent ranking quality. In tests utilizing the BEIR benchmark, Pinecone Rerank V0 excelled by achieving the top average NDCG@10 score, outpacing rival models across 6 out of 12 datasets. Remarkably, it demonstrated a 60% performance increase on the Fever dataset when compared to Google Semantic Ranker, as well as over 40% enhancement on the Climate-Fever dataset when evaluated against models like cohere-v3-multilingual and voyageai-rerank-2. Currently, users can access this model through Pinecone Inference in a public preview, enabling extensive experimentation and feedback gathering. This innovative design underscores a commitment to advancing search technology and positions Pinecone Rerank V0 as a crucial asset for organizations striving to improve their information retrieval systems. Its unique capabilities not only refine search outcomes but also adapt to various user needs, enhancing overall usability.

Media

Media

Integrations Supported

Llama
OpenAI
Airbyte
Cohere
Gemini Enterprise
GitHub Copilot
Haystack
HoneyHive
Instill
Jina AI
Matillion
Mistral AI
NVIDIA TensorRT
Nuclia
Qwen
RankGPT
Snowflake
Traceloop
TwelveLabs
Vercel

Integrations Supported

Llama
OpenAI
Airbyte
Cohere
Gemini Enterprise
GitHub Copilot
Haystack
HoneyHive
Instill
Jina AI
Matillion
Mistral AI
NVIDIA TensorRT
Nuclia
Qwen
RankGPT
Snowflake
Traceloop
TwelveLabs
Vercel

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

$25 per month
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

Pinecone

Date Founded

2019

Company Location

United States

Company Website

www.pinecone.io/blog/pinecone-rerank-v0-announcement/

Categories and Features

Categories and Features

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