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 artificial general agents (AGA) powered by AGI 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.
Connect AI applications to multimodal enterprise data with a scalable retrieval augmented generation (RAG) pipeline built on highly performant, industry-leading NIM microservices, for faster PDF data extraction and more accurate information retrieval.
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 generation of text embeddings used for question-answering retrieval.
GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.