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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

Enhance your embedding metadata and tokens through an intuitive user interface. By employing sophisticated NLP cleansing methods such as TF-IDF, you can normalize and enrich your embedding tokens, which significantly boosts both efficiency and accuracy in applications related to large language models. Furthermore, optimize the pertinence of the content retrieved from a vector database by intelligently managing the structure of the content, whether by splitting or merging, and incorporating void or hidden tokens to ensure that the chunks remain semantically coherent. With Embedditor, you gain complete command over your data, allowing for seamless deployment on your personal computer, within your dedicated enterprise cloud, or in an on-premises setup. By utilizing Embedditor's advanced cleansing features to eliminate irrelevant embedding tokens such as stop words, punctuation, and frequently occurring low-relevance terms, you have the potential to reduce embedding and vector storage costs by up to 40%, all while enhancing the quality of your search results. This innovative approach not only streamlines your workflow but also optimizes the overall performance of your NLP projects.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Docker No 
GitHub No 
IngestAI No 

Integrations

Docker Yes 
GitHub Yes 
IngestAI Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
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 No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
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 No 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

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

Vendor Details

Company Name

Embedditor

Website

embedditor.ai/

Product Features

Product Features

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Alternatives

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