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What is NVIDIA NeMo Retriever?
NVIDIA NeMo Retriever comprises a collection of microservices tailored for the development of high-precision multimodal extraction, reranking, and embedding workflows, all while prioritizing data privacy. It facilitates quick and context-aware responses for various AI applications, including advanced retrieval-augmented generation (RAG) and agentic AI functions. Within the NVIDIA NeMo ecosystem and leveraging NVIDIA NIM, NeMo Retriever equips developers with the ability to effortlessly integrate these microservices, linking AI applications to vast enterprise datasets, no matter their storage location, and providing options for specific customizations to suit distinct requirements. This comprehensive toolkit offers vital elements for building data extraction and information retrieval pipelines, proficiently gathering both structured and unstructured data—ranging from text to charts and tables—transforming them into text formats, and efficiently eliminating duplicates. Additionally, the embedding NIM within NeMo Retriever processes these data segments into embeddings, storing them in a highly efficient vector database, which is optimized by NVIDIA cuVS, thus ensuring superior performance and indexing capabilities. As a result, the overall user experience and operational efficiency are significantly enhanced, enabling organizations to fully leverage their data assets while upholding a strong commitment to privacy and accuracy in their processes. By employing this innovative solution, businesses can navigate the complexities of data management with greater ease and effectiveness.
What is Klee?
Unlock the potential of a secure and localized AI experience right from your desktop, delivering comprehensive insights while ensuring total data privacy and security. Our cutting-edge application designed for macOS merges efficiency, privacy, and intelligence through advanced AI capabilities. The RAG (Retrieval-Augmented Generation) system enhances the large language model's functionality by leveraging data from a local knowledge base, enabling you to safeguard sensitive information while elevating the quality of the model's responses. To configure RAG on your local system, you start by segmenting documents into smaller pieces, converting these segments into vectors, and storing them in a vector database for easy retrieval. This vectorized data is essential during the retrieval phase. When users present a query, the system retrieves the most relevant segments from the local knowledge base and integrates them with the initial query to generate a precise response using the LLM. Furthermore, we are excited to provide individual users with lifetime free access to our application, reinforcing our commitment to user privacy and data security, which distinguishes our solution in a competitive landscape. In addition to these features, users can expect regular updates that will continually enhance the application’s functionality and user experience.
Integrations Supported
Codestral
Codestral Mamba
Hugging Face
LangChain
Llama 2
Llama 3.3
Meta Pixel
Ministral 8B
Mistral 7B
Mistral AI
API Availability
Has API
API Availability
Pricing Information
Pricing not provided
Pricing Information
Pricing not provided
Free Version
Supported Platforms
SaaS
Supported Platforms
Mac
Customer Service / Support
Standard Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Company Facts
Organization Name
NVIDIA
Date Founded
1993
Company Location
United States
Company Website
developer.nvidia.com/nemo-retriever
Company Facts
Organization Name
Klee
Company Website
kleedesktop.com
Categories and Features
Embedding Models
Not specified
Reranking Models
Not specified
Categories and Features
AI Tools
Not specified
Artificial Intelligence
Not specified
Retrieval-Augmented Generation (RAG)
Not specified