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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
Vectara offers LLM-powered search as-a-service. The platform offers a complete ML search process, from extraction and indexing to retrieval and re-ranking as well as calibration. API-addressable for every element of the platform. Developers can embed the most advanced NLP model for site and app search in minutes.
Vectara automatically extracts text form PDF and Office to JSON HTML XML CommonMark, and many other formats. Use cutting-edge zero-shot models that use deep neural networks to understand language to encode at scale. Segment data into any number indexes that store vector encodings optimized to low latency and high recall. Use cutting-edge, zero shot neural network models to recall candidate results from millions upon millions of documents. Cross-attentional neural networks can increase the precision of retrieved answers. They can merge and reorder results. Focus on the likelihood that the retrieved answer is a probable answer to your query.
API Access
Has API
API Access
Has API
Integrations
Datavolo
Gemini
Gemini Enterprise
IBM watsonx.data
Langflow
Llama
Mistral AI
NVIDIA TensorRT
OpenAI
Python
Integrations
Datavolo
Gemini
Gemini Enterprise
IBM watsonx.data
Langflow
Llama
Mistral AI
NVIDIA TensorRT
OpenAI
Python
Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Castorini
Country
Canada
Website
github.com/castorini/rank_llm/
Vendor Details
Company Name
Vectara
Founded
2020
Country
United States
Website
vectara.com
Product Features
Product Features
Enterprise Search
AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery