EmbeddingGemma 2Google
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Gemini Embedding 2Google
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Related Products
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About
EmbeddingGemma 2 is an open, lightweight multimodal embedding model designed to map text, code, images, video, and audio into a shared embedding space for search, retrieval, classification, routing, and RAG applications. Built on the Gemma 4 architecture and released under the Apache 2.0 license, it has 740 million parameters and is optimized for on-device inference. Its modular design can use as little as 270M parameters for text-only workloads, with optional vision and audio encoders for full multimodal support. Matryoshka Representation Learning lets developers reduce output vectors from 768 dimensions to 512, 256, or 128, lowering storage and memory requirements for local vector databases. The model supports an 8K-token context window and can process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations on local hardware.
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About
Gemini Embedding models, including the newer Gemini Embedding 2, are part of Google’s Gemini AI ecosystem and are designed to convert text, phrases, sentences, and code into numerical vector representations that capture their semantic meaning. Unlike generative models that produce new content, the embedding model transforms input data into dense vectors that represent meaning in a mathematical format, allowing computers to compare and analyze information based on conceptual similarity rather than exact wording. These embeddings enable applications such as semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation pipelines. The model can process input in more than 100 languages and supports up to 2048 tokens per request, allowing it to embed longer pieces of text or code while maintaining strong contextual understanding.
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Platforms Supported
Windows
Not Supported
Mac
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Linux
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Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
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Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Developers and AI teams wanting to build private, efficient, on-device multimodal search, retrieval, RAG, and semantic indexing systems
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Audience
AI developers and data engineers who need a high-performance embedding model to convert text or code into semantic vectors for search, retrieval, and AI applications
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Support
Phone Support
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24/7 Live Support
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Online
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Support
Phone Support
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24/7 Live Support
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Online
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API
Offers API
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API
Offers API
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Pricing
No information available.
Free Version
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Free Trial
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Pricing
Free
Free Version
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Free Trial
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Reviews/
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Reviews/
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Training
Documentation
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Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Company InformationGoogle
Founded: 1998
United States
blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/
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Company InformationGoogle
Founded: 1998
United States
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/
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Categories |
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Integrations
Gemini
Not Supported
Gemini Enterprise
Not Supported
Gemini Enterprise Agent Platform
Not Supported
Google AI Studio
Not Supported
Mimasa AI
Not Supported
Python
Not Supported
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Integrations
Gemini
Supported
Gemini Enterprise
Supported
Gemini Enterprise Agent Platform
Supported
Google AI Studio
Supported
Mimasa AI
Supported
Python
Supported
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