Introducing EmbeddingGemma 2: A best-in-class open model for natively multimodal embeddings | Google

Google DeepMind has introduced EmbeddingGemma 2, an open multimodal embedding model. This model maps text, images, video, and audio inputs into a unified 768-dimensional vector space. With 740M parameters, it combines a 270M parameter text model with modular vision (170M) and audio (300M) encoders. Designed for consumer hardware, EmbeddingGemma 2 provides low-latency semantic representations for on-device applications such as search, RAG, classification, and clustering.

This model is the first to natively support multimodal embeddings across text, images, video, and audio within a single 768-dimensional vector space.