Google Gemini Embedding 2 for multimodal RAG
A Google Developers post about one embedding model for text, images, video, audio, and documents, which is very relevant for agentic RAG.
Source
Building with Gemini Embedding 2: Agentic multimodal RAG and beyond
Why I saved it
Most RAG examples still feel text-only. This article is useful because it treats retrieval as multimodal from the beginning.
Gemini Embedding 2 maps text, images, video, audio, and documents into one embedding space. That is a big product idea for agents that need to work over real company data.
My notes
- The model supports more than 100 languages.
- It can handle interleaved inputs, not just one clean text block.
- Task prefixes help retrieval quality by telling the embedding model what kind of match is needed.
- The product use cases include legal retrieval, memory, visual search, and multimodal question answering.
What I want to remember
Future RAG systems should not assume all important context is text. Screenshots, PDFs, product images, audio, and short videos can all be part of the same knowledge layer.