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Average Ratings 0 Ratings
Description
EmbeddingGemma 2 is a versatile and lightweight multimodal embedding model that facilitates the mapping of text, code, images, video, and audio into a unified embedding space, which is useful for applications such as search, retrieval, classification, routing, and RAG. It is constructed on the Gemma 4 framework and distributed under the Apache 2.0 license, featuring an impressive 740 million parameters while being fine-tuned for efficient on-device inference. Its flexible architecture allows for the use of only 270 million parameters for text-centric tasks, and it includes additional vision and audio encoders for comprehensive multimodal capabilities. Furthermore, the innovative Matryoshka Representation Learning technique enables developers to compress output vectors from 768 dimensions down to 512, 256, or even 128 dimensions, effectively reducing the storage and memory demands for local vector databases. The model is equipped with an 8K-token context window, providing the capability to handle up to 5.5 minutes of audio, 29 images, 58 video frames, or various combinations of these inputs seamlessly on local hardware. This adaptability makes it particularly valuable for developers seeking to enhance their applications with rich multimedia integration.
Description
Voyage AI has introduced voyage-3-large, an innovative general-purpose multilingual embedding model that excels across eight distinct domains, such as law, finance, and code, achieving an average performance improvement of 9.74% over OpenAI-v3-large and 20.71% over Cohere-v3-English. This model leverages advanced Matryoshka learning and quantization-aware training, allowing it to provide embeddings in dimensions of 2048, 1024, 512, and 256, along with various quantization formats including 32-bit floating point, signed and unsigned 8-bit integer, and binary precision, which significantly lowers vector database expenses while maintaining high retrieval quality. Particularly impressive is its capability to handle a 32K-token context length, which far exceeds OpenAI's 8K limit and Cohere's 512 tokens. Comprehensive evaluations across 100 datasets in various fields highlight its exceptional performance, with the model's adaptable precision and dimensionality options yielding considerable storage efficiencies without sacrificing quality. This advancement positions voyage-3-large as a formidable competitor in the embedding model landscape, setting new benchmarks for versatility and efficiency.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Cohere
No
LangChain
No
OneSignal
No
PyTorch
No
Snowflake
No
Voyage AI
No
Integrations
Cohere
Yes
LangChain
Yes
OneSignal
Yes
PyTorch
Yes
Snowflake
Yes
Voyage AI
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
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
No
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
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
Vendor Details
Company Name
Founded
1998
Country
United States
Website
blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/
Vendor Details
Company Name
MongoDB
Founded
2007
Country
United States
Website
blog.voyageai.com/2025/01/07/voyage-3-large/
Product Features
Product Features
Alternatives
No Alternatives