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What is Shaped?

Discover the fastest pathway to personalized suggestions and search capabilities that enhance user engagement, boost conversion rates, and increase overall revenue through a dynamic system that adapts instantly to your requirements. Our platform is designed to guide users in finding precisely what they seek by showcasing products or content that closely match their preferences. In addition, we focus on your business objectives, making sure that every element of your platform or marketplace is optimally aligned. At its foundation, Shaped includes a sophisticated four-stage recommendation engine that utilizes advanced data and machine-learning technology to analyze your information and effectively meet your discovery needs at scale. The integration process with your existing data sources is both efficient and rapid, facilitating the real-time ingestion and re-ranking of information based on user interactions. You also have the opportunity to refine large language models and neural ranking systems to attain top-tier performance. Moreover, our platform allows you to design and test various ranking and retrieval mechanisms tailored to specific applications, ensuring users receive the most pertinent results for their queries. This adaptability guarantees a user experience that is not only relevant but also consistently engaging.

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.

Media

Media

Integrations Supported

AccessOwl
Amazon Kinesis
Amazon Redshift
Amazon S3
Gemini
Google Analytics
Google Cloud BigQuery
Google Cloud Firestore
Llama
Mistral AI
Mixpanel
MySQL
NVIDIA TensorRT
OpenAI
PostgreSQL
Python
Qwen
RankGPT
Segment
Snowflake

Integrations Supported

AccessOwl
Amazon Kinesis
Amazon Redshift
Amazon S3
Gemini
Google Analytics
Google Cloud BigQuery
Google Cloud Firestore
Llama
Mistral AI
Mixpanel
MySQL
NVIDIA TensorRT
OpenAI
PostgreSQL
Python
Qwen
RankGPT
Segment
Snowflake

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
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

Shaped

Company Location

United States

Company Website

www.shaped.ai/

Company Facts

Organization Name

Castorini

Company Location

Canada

Company Website

github.com/castorini/rank_llm/

Categories and Features

Categories and Features

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