What Multimodal Embeddings Mean
Standard embedding models convert text into vectors that capture semantic meaning. Multimodal embedding models do the same thing across different data types — and crucially, put them in the same vector space.
This means:
- You can search for an image using a text description (semantically, not keyword-matching)
- You can find related documents and images together, not in separate searches
- You can build RAG systems that retrieve across text and visual content simultaneously
For most RAG applications to date, you've had to choose: index text or index images. Gemini Embedding 2 makes "index everything together" practical.
Benchmark Position
Google is calling this state-of-the-art on multimodal retrieval benchmarks. Independent evaluations will determine whether that holds up in practice, but Google's embedding models have historically been competitive.
Practical Applications for Solo Founders
If you're building:
- A search system that needs to surface relevant images alongside documents
- A RAG application that works over mixed content (slides, PDFs, screenshots, text)
- Any system where "find me things related to this" needs to work across data types
Gemini Embedding 2 is worth evaluating as the retrieval layer.
The API is available now through Google AI Studio and Vertex AI. Pricing follows standard embedding model rates — significantly cheaper than language model inference.