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What is EmbeddingGemma 2?

EmbeddingGemma 2 is a highly adaptable and lightweight multimodal embedding model that enables the integration of text, code, images, video, and audio into a single cohesive embedding space, serving a variety of functions such as search, retrieval, classification, routing, and RAG. Built on the Gemma 4 framework and licensed under Apache 2.0, it boasts an impressive 740 million parameters while being optimized for efficient operation on devices. The model's architecture is designed to allow for the use of only 270 million parameters when focusing on text-related tasks, and it is also equipped with additional encoders for vision and audio to provide a full range of multimodal functionalities. Moreover, the groundbreaking Matryoshka Representation Learning technique empowers developers to condense output vectors from 768 dimensions to smaller sizes of 512, 256, or even 128 dimensions, significantly minimizing the storage and memory requirements for local vector databases. Additionally, with an 8K-token context window, the model can seamlessly process up to 5.5 minutes of audio, 29 images, 58 video frames, or various combinations of these inputs on local hardware without a hitch. This level of versatility and efficiency makes it an invaluable asset for developers looking to enrich their applications with sophisticated multimedia capabilities. Ultimately, the potential applications of EmbeddingGemma 2 are vast, paving the way for innovative advancements in the field of multimodal technology.

What is Cohere Embed?

Cohere's Embed emerges as a leading multimodal embedding solution that adeptly transforms text, images, or a combination of the two into superior vector representations. These vector embeddings are designed for a multitude of uses, including semantic search, retrieval-augmented generation, classification, clustering, and autonomous AI applications. The latest iteration, embed-v4.0, enhances functionality by enabling the processing of mixed-modality inputs, allowing users to generate a cohesive embedding that incorporates both text and images. It includes Matryoshka embeddings that can be customized in dimensions of 256, 512, 1024, or 1536, giving users the ability to fine-tune performance in relation to resource consumption. With a context length that supports up to 128,000 tokens, embed-v4.0 is particularly effective at managing large documents and complex data formats. Additionally, it accommodates various compressed embedding types such as float, int8, uint8, binary, and ubinary, which aid in efficient storage solutions and quick retrieval in vector databases. Its multilingual support spans over 100 languages, making it an incredibly versatile tool for global applications. As a result, users can utilize this platform to efficiently manage a wide array of datasets, all while upholding high performance standards. This versatility ensures that it remains relevant in a rapidly evolving technological landscape.

Media

Media

Integrations Supported

Integrations Supported

Cohere
voyage-4-large

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

$0.47 per image

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/

Company Facts

Organization Name

Cohere

Date Founded

2019

Company Location

Canada

Company Website

cohere.com/embed

Categories and Features

Embedding Models

Not specified

Categories and Features

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