Gemini Embedding 2 - Google's first native multimodal embedding model

What is Gemini Embedding 2?

Gemini Embedding 2 is Google's first native multimodal embedding model, built on the Gemini architecture. The model maps text, images, videos, audio, and documents to a single vector space, supporting semantic understanding across more than 100 languages. It can handle interleaved multimodal inputs (such as text-image combinations), embedding directly without audio transcription, and employs nested representation learning techniques for flexible dimensionality reduction. Gemini Embedding 2 boasts leading performance in tasks such as RAGs and semantic search, and is currently available for preview through the Gemini API and Vertex AI, compatible with mainstream AI frameworks and vector databases.

Main functions of Gemini Embedding 2

• Unified Multimodal EmbeddingIt unifies the mapping of five different modalities of data—text, images, videos, audio, and documents—into the same vector embedding space, thereby achieving true cross-modal semantic understanding.
• Interleaved multimodal inputThe model supports processing multiple interleaved input modal data in a single request, such as simultaneously inputting images and text, accurately capturing the complex relationships between different media types.
• Native audio embeddingGemini Embedding 2 natively supports direct embedding of audio data, generating vector representations without first converting the audio into intermediate text transcription results.
• PDF document embeddingThe model can be directly embedded to process PDF documents of up to 6 pages, transforming complex document content into vector form that can be used for retrieval and analysis.
• Flexible dimensional adjustmentThe model supports flexible adjustment of output dimensions. Developers can choose between 3072, 1536 or 768 dimensions according to actual needs, balancing embedding quality and storage cost.
• Multilingual semantic understandingGemini Embedding 2 can capture semantic intent across more than 100 languages, providing a unified technical foundation for multimodal applications in multilingual environments.

Technical Principles of Gemini Embedding 2

• Based on Gemini unified architectureBuilt on the Gemini architecture, it inherits leading multimodal understanding capabilities. Through a unified encoder design, the architecture enables the model to process and understand different types of input data simultaneously, and the native multimodal design ensures semantic alignment of each modality in the shared space.
• Matryoshka dolls represent learning (MRL).The model employs Matryoshka Representation Learning (MRL) technology, which uses dynamic dimensionality reduction to achieve nested information storage. MRL allows the model to learn representations of different granularities during training, enabling the direct extraction of low-dimensional subvectors from high-dimensional vectors without recalculation. The nested structure allows developers to flexibly choose the output dimension according to the application scenario, significantly reducing storage and computational overhead while maintaining high semantic quality.
• Unified cross-modal semantic spaceThe core breakthrough of Gemini Embedding 2 lies in establishing a unified cross-modal semantic space. Through large-scale multimodal contrastive learning, the model learns to map semantically similar but modally different content to adjacent vector regions. This unified space enables cross-modal retrieval, such as searching for related images using text descriptions or querying similar video clips using images, breaking the limitation of traditional unimodal embedding models that cannot directly compare different media types.

Project address for Gemini Embedding 2

• Project official website: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/

Application scenarios of Gemini Embedding 2

• Search Enhancement Generation (RAG)In the RAG system, Gemini Embedding 2 can simultaneously process knowledge base content in multiple formats such as documents, images, and audio, providing richer and more accurate contextual information for large language models, and significantly improving the quality and relevance of generated answers.
• Legal and Compliance FieldLegal professionals can use the model to quickly locate key information during the evidence discovery phase of litigation, enabling high-precision retrieval of text, image, and video materials across millions of records, significantly shortening the time required to review case materials.
• Enterprise knowledge managementEnterprises can embed scattered PDF reports, product images, training videos, and meeting recordings into a unified vector space to build a comprehensive multimodal knowledge base, enabling employees to quickly obtain the information they need through natural language queries.
• Multilingual content analysisMedia and content platforms can leverage models to achieve cross-language, multimodal content recommendation, sentiment analysis, and trend monitoring, breaking down language barriers to serve global users.