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What is Gemini Embedding 2?

The Gemini Embedding models, particularly the sophisticated Gemini Embedding 2, are a vital component of Google's Gemini AI framework, designed to convert text, phrases, sentences, and code into numerical vectors that capture their semantic essence. Unlike generative models that produce new content, these embedding models transform inputs into dense vectors that represent meaning mathematically, allowing for the analysis and comparison of information through conceptual relationships rather than just specific wording. This unique capability enables a wide range of applications, such as semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation processes. Furthermore, the model supports over 100 languages and can process inputs of up to 2048 tokens, which allows it to efficiently embed longer texts or code while maintaining a strong contextual understanding. As a result, the Gemini Embedding models significantly contribute to the effectiveness of AI-driven tasks in various industries, making them indispensable tools for modern applications. Their adaptability and robust performance highlight the importance of advanced embedding techniques in the evolving landscape of artificial intelligence.

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.

Media

Media

Integrations Supported

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

Integrations Supported

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

API Availability

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

$0.15 per 1M input tokens

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

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

Categories and Features

Embedding Models

Not specified

Categories and Features

Embedding Models

Not specified

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