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Description

RankLLM is a comprehensive Python toolkit designed to enhance reproducibility in information retrieval research, particularly focusing on listwise reranking techniques. This toolkit provides an extensive array of rerankers, including pointwise models such as MonoT5, pairwise models like DuoT5, and listwise models that work seamlessly with platforms like vLLM, SGLang, or TensorRT-LLM. Furthermore, it features specialized variants like RankGPT and RankGemini, which are proprietary listwise rerankers tailored for enhanced performance. The toolkit comprises essential modules for retrieval, reranking, evaluation, and response analysis, thereby enabling streamlined end-to-end workflows. RankLLM's integration with Pyserini allows for efficient retrieval processes and ensures integrated evaluation for complex multi-stage pipelines. Additionally, it offers a dedicated module for in-depth analysis of input prompts and LLM responses, which mitigates reliability issues associated with LLM APIs and the unpredictable nature of Mixture-of-Experts (MoE) models. Supporting a variety of backends, including SGLang and TensorRT-LLM, it ensures compatibility with an extensive range of LLMs, making it a versatile choice for researchers in the field. This flexibility allows researchers to experiment with different model configurations and methodologies, ultimately advancing the capabilities of information retrieval systems.

Description

troveGEN serves as a platform for retrieval-augmented generation (RAG) and document search, specifically designed for teams developing AI products and organizations that require controlled access to their internal data. The system can process a variety of inputs such as files, scanned documents, and web pages, after which it efficiently parses, applies optical character recognition (OCR), chunks, indexes, and retrieves pertinent excerpts using hybrid search methods, reranking, filters, and access controls. Users can leverage its REST API, TypeScript SDK, or Model Context Protocol server for searching content and obtaining results that are linked to their sources; it also offers optional answer generation that provides cited responses. Customers have the flexibility to choose between managed hosting or integrating their own vector databases, models, and content sources, even supporting air-gapped deployments for enhanced security. Potential applications encompass internal knowledge searches, customer support solutions, management of technical documentation, and streamlining document workflows across various sectors including finance, healthcare, legal, and government. Overall, troveGEN enhances information retrieval and management while ensuring compliance and governance for sensitive data.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

No images available

Integrations

OpenAI Yes 
Amazon S3 No 
Azure Blob Storage No 
Cohere No 
Gemini Yes 
Gemini Enterprise Yes 
Google Drive No 
Llama Yes 
Microsoft SharePoint No 
Mistral AI Yes 
Model Context Protocol (MCP) No 
NVIDIA TensorRT Yes 
Pinecone No 
Python Yes 
Qdrant No 
Qwen Yes 
RankGPT Yes 
pgvector No 

Integrations

OpenAI Yes 
Amazon S3 Yes 
Azure Blob Storage Yes 
Cohere Yes 
Gemini No 
Gemini Enterprise No 
Google Drive Yes 
Llama No 
Microsoft SharePoint Yes 
Mistral AI No 
Model Context Protocol (MCP) Yes 
NVIDIA TensorRT No 
Pinecone Yes 
Python No 
Qdrant Yes 
Qwen No 
RankGPT No 
pgvector Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

$25/month
troveGEN-Z Launch costs $25/month and includes 1,500 pages, 8,000 searches, 1,500 answers, and two projects. Annual billing costs 10 times the monthly price. The one-time free trial includes 500 pages, 2,000 searches, and 200 answers. Sovereign deployments are quoted.
Free Trial Yes 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Castorini

Country

Canada

Website

github.com/castorini/rank_llm/

Vendor Details

Company Name

Intellara Technologies

Founded

2025

Country

India

Website

troveGEN.com

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