Google Gemini Embedding 2: First Fully Multimodal Embedding Model Explained

Description

Google DeepMind has introduced Gemini Embedding 2, its first fully multimodal embedding model capable of understanding text, images, video, audio, and documents in a single embedding space. Unlike traditional embedding models that only process text, Gemini Embedding 2 enables developers to embed multiple media types together, making it powerful for multimodal search, retrieval-augmented generation (RAG), clustering, and sentiment analysis. In this video, we break down how Gemini Embedding 2 works, its capabilities, and why it is a major step toward next-generation multimodal AI applications. Key Features • Supports text (up to 8,192 tokens) • Handles images (up to 6 PNG/JPEG per request) • Processes video up to 120 seconds • Native audio embeddings without transcription • PDF documents up to 6 pages The model also uses Matryoshka Representation Learning, allowing developers to scale embedding dimensions (3072, 1536, or 768) to balance performance and storage. Developers can access Gemini Embedding 2 through: • Gemini API • Vertex AI • LangChain • LlamaIndex • Haystack • Weaviate This new model enables powerful multimodal retrieval, search systems, and AI knowledge applications across large datasets. 🚀 If you're building AI search systems, RAG pipelines, or multimodal applications, Gemini Embedding 2 is a model worth exploring. #GeminiEmbedding2 #MultimodalAI #GoogleDeepMind #AIEmbeddings #GenerativeAI