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What is Gemini Embedding?

The first text model of the Gemini Embedding, referred to as gemini-embedding-001, has officially launched and is accessible through both the Gemini API and Gemini Enterprise Agent Platform, having consistently held its top spot on the Massive Text Embedding Benchmark Multilingual leaderboard since its initial trial in March, thanks to its exceptional performance in retrieval, classification, and multiple embedding tasks, outperforming both legacy Google models and those from other external developers. Notably, this versatile model supports over 100 languages and features a maximum input limit of 2,048 tokens, employing the cutting-edge Matryoshka Representation Learning (MRL) technique, which enables developers to choose from output dimensions of 3072, 1536, or 768 for optimal quality, efficiency, and performance. Users can easily access this model through the well-known embed_content endpoint in the Gemini API. This transition process is designed for a smooth user experience, minimizing any impact on existing workflows and ensuring continuity in operations. The launch of this model represents a significant step forward in the field of text embeddings, paving the way for even more advancements in multilingual applications.

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.

Media

Media

Integrations Supported

Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google AI Studio
Mimasa AI
Python

Integrations Supported

API Availability

Has API

API Availability

Has API

Pricing Information

$0.15 per 1M input tokens

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

developers.googleblog.com/en/gemini-embedding-available-gemini-api/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

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

Categories and Features

Embedding Models

Not specified

Categories and Features

Embedding Models

Not specified

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