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ease
features
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support

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Description

Cohere's Embed stands out as a premier multimodal embedding platform that effectively converts text, images, or a blend of both into high-quality vector representations. These vector embeddings are specifically tailored for various applications such as semantic search, retrieval-augmented generation, classification, clustering, and agentic AI. The newest version, embed-v4.0, introduces the capability to handle mixed-modality inputs, permitting users to create a unified embedding from both text and images. It features Matryoshka embeddings that can be adjusted in dimensions of 256, 512, 1024, or 1536, providing users with the flexibility to optimize performance against resource usage. With a context length that accommodates up to 128,000 tokens, embed-v4.0 excels in managing extensive documents and intricate data formats. Moreover, it supports various compressed embedding types such as float, int8, uint8, binary, and ubinary, which contributes to efficient storage solutions and expedites retrieval in vector databases. Its multilingual capabilities encompass over 100 languages, positioning it as a highly adaptable tool for applications across the globe. Consequently, users can leverage this platform to handle diverse datasets effectively while maintaining performance efficiency.

Description

NVIDIA NeMo Retriever is a suite of microservices designed for creating high-accuracy multimodal extraction, reranking, and embedding workflows while ensuring maximum data privacy. It enables rapid, contextually relevant responses for AI applications, including sophisticated retrieval-augmented generation (RAG) and agentic AI processes. Integrated within the NVIDIA NeMo ecosystem and utilizing NVIDIA NIM, NeMo Retriever empowers developers to seamlessly employ these microservices, connecting AI applications to extensive enterprise datasets regardless of their location, while also allowing for tailored adjustments to meet particular needs. This toolset includes essential components for constructing data extraction and information retrieval pipelines, adeptly extracting both structured and unstructured data, such as text, charts, and tables, transforming it into text format, and effectively removing duplicates. Furthermore, a NeMo Retriever embedding NIM processes these data segments into embeddings and stores them in a highly efficient vector database, optimized by NVIDIA cuVS to ensure faster performance and indexing capabilities, ultimately enhancing the overall user experience and operational efficiency. This comprehensive approach allows organizations to harness the full potential of their data while maintaining a strong focus on privacy and precision.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Cohere
NVIDIA NIM
NVIDIA NeMo
voyage-4-large

Integrations

Cohere
NVIDIA NIM
NVIDIA NeMo
voyage-4-large

Pricing Details

$0.47 per image
Free Trial
Free Version

Pricing Details

No price information available.
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

Cohere

Founded

2019

Country

Canada

Website

cohere.com/embed

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

developer.nvidia.com/nemo-retriever

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