Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Snowflake's Arctic Embed 2.0 brings enhanced multilingual functionality to its text embedding models, allowing for efficient global-scale data retrieval while maintaining strong performance in English and scalability. This version builds on the solid groundwork of earlier iterations, offering support for various languages and enabling developers to implement stream-processing pipelines that utilize neural networks and tackle intricate tasks, including tracking, video encoding/decoding, and rendering, thus promoting real-time data analytics across multiple formats. The model employs Matryoshka Representation Learning (MRL) to optimize embedding storage, achieving substantial compression with minimal loss of quality. As a result, organizations can effectively manage intensive workloads such as training expansive models, fine-tuning, real-time inference, and executing high-performance computing operations across different languages and geographical areas. Furthermore, this innovation opens new opportunities for businesses looking to harness the power of multilingual data analytics in a rapidly evolving digital landscape.
Description
The Gemini Embedding's inaugural text model, known as gemini-embedding-001, is now officially available through the Gemini API and Gemini Enterprise Agent Platform, having maintained its leading position on the Massive Text Embedding Benchmark Multilingual leaderboard since its experimental introduction in March, attributed to its outstanding capabilities in retrieval, classification, and various embedding tasks, surpassing both traditional Google models and those from external companies. This highly adaptable model accommodates more than 100 languages and has a maximum input capacity of 2,048 tokens, utilizing the innovative Matryoshka Representation Learning (MRL) method, which allows developers to select output dimensions of 3072, 1536, or 768 to ensure the best balance of quality, performance, and storage efficiency. Developers are able to utilize it via the familiar embed_content endpoint in the Gemini API.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Gemini
No
Gemini Enterprise
No
Gemini Enterprise Agent Platform
No
Google AI Studio
No
Mimasa AI
No
OpenAI
Yes
Python
No
Snowflake
Yes
Integrations
Gemini
Yes
Gemini Enterprise
Yes
Gemini Enterprise Agent Platform
Yes
Google AI Studio
Yes
Mimasa AI
Yes
OpenAI
No
Python
Yes
Snowflake
No
Pricing Details
$2 per credit
Free Trial
No
Free Version
No
Pricing Details
$0.15 per 1M input tokens
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
Vendor Details
Company Name
Snowflake
Founded
2012
Country
United States
Website
www.snowflake.com/en/engineering-blog/snowflake-arctic-embed-2-multilingual/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
developers.googleblog.com/en/gemini-embedding-available-gemini-api/