Elevate Shopping Experiences Online and In Stores.
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
Multimodal question-answer retrieval representing user queries as text and documents as images.
Build advanced AI agents within the biomedical domain using the AI-Q Blueprint and the BioNeMo Virtual Screening Blueprint
Build a custom deep researcher powered by state-of-the-art models that continuously process and synthesize multimodal enterprise data, enabling reasoning, planning, and refinement to generate comprehensive reports.
The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.
Continuously extract, embed, and index multimodal data for fast, accurate semantic search. Built on world-class NeMo Retriever models, the RAG blueprint connects AI applications to multimodal enterprise data wherever it resides.
Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
Fine-tuned reranking model for multilingual, cross-lingual text question-answering retrieval, with long context support.
Create intelligent virtual assistants for customer service across every industry
Cutting-edge vision-language model exceling in high-quality reasoning from images.
Cutting-edge vision-Language model exceling in high-quality reasoning from images.
English text embedding model for question-answering retrieval.
Multilingual text question-answering retrieval, transforming textual information into dense vector representations.
GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.
