Gemini Embedding 2 Explained: Google’s New Multimodal Embedding Model (AI Update)

Description

Google has introduced Gemini Embedding 2, its first natively multimodal embedding model, released via the Gemini API and Vertex AI. In this short explainer video, we break down what Gemini Embedding 2 is, how embeddings work, and why this update is important for modern AI systems. Unlike traditional embedding models that only work with text, Gemini Embedding 2 can embed and compare text, images, video, audio, and documents within a single shared vector space. This enables powerful capabilities like cross-modal search, multimodal retrieval, and more advanced AI applications. In this video you’ll learn: • What embeddings are in AI • What makes Gemini Embedding 2 different • How multimodal embeddings work • Real-world use cases for AI search and RAG systems • Why this matters for developers and AI products Embeddings are the backbone of many modern AI systems such as retrieval-augmented generation (RAG), semantic search, recommendation engines, and AI assistants. With multimodal embeddings, AI systems can understand and retrieve information across different types of data more effectively. Watch the full video for a quick explanation of this important AI update. -------------------------------- Tags: Gemini Embedding 2 Google AI multimodal embeddings AI embeddings explained RAG systems vector search AI search machine learning generative AI AI infrastructure Google Gemini AI AI models explained