Gemini API File Search is now multimodal: build efficient, verifiable RAG

Source: Google Date: 2026-05-05 URL: https://blog.google/innovation-and-ai/technology/developers-tools/expanded-gemini-api-file-search-multimodal-rag/

Summary

Google expanded the Gemini API File Search tool to support multimodal retrieval: the Gemini Embedding 2 model now processes images and text together, allowing search by visual style or semantic meaning rather than filenames. Two additional capabilities shipped alongside — custom key-value metadata filters for reducing noise at query time, and page-level citations that tie model responses to specific source document pages for verifiable RAG outputs.

Implications

• RAG infrastructure: Page-level citations and metadata filtering address the two biggest trust and precision gaps in production RAG systems, reducing the need for custom preprocessing pipelines.
• Multimodal retrieval thread: Native image search via embeddings (not just OCR or alt-text) expands the surface of retrievable enterprise content to design assets, diagrams, and scanned documents.
• Developer platform competition: Google is folding managed RAG infrastructure — chunking, embedding, indexing, retrieval — directly into the Gemini API, tightening the moat against standalone vector DB vendors.