Building with Gemini Embedding 2: Agentic multimodal RAG and beyond- Google Developers Blog

What this post added

Introduces Gemini Embedding 2, a multimodal embedding model that maps text, images, video, audio, and documents into a single embedding space. Details its use in agentic multimodal RAG with task prefixes for optimized embeddings, multimodal search with prefixes like 'task: search result', and search reranking using distance metrics. Also covers clustering, classification, and anomaly detection using symmetric task prefixes. Discusses efficient storage using vector databases and the Matryoshka Representation Learning (MRL) feature for truncating embeddings. Provides code examples for interleaved input processing, task prefix preparation, and similarity calculation.