GoogleGemini Embedding 2 is Google's first multimodal embedding model. It currently supports mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports input context up to 8,192 tokens and flexible output dimensions from 128 to 3,072 (recommended: 768, 1536, or 3,072). Designed for cross-modal similarity, you can embed a text query and retrieve the most relevant images, or vice versa, making it well-suited for multimodal search, recommendation, and document understanding pipelines.
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