Gemini File Search
by Google
A managed multimodal RAG store in the Gemini API — search over both text and images in documents without a separate vector database.
Features
- Managed RAG store: upload files, index them, and query with grounded responses — no separate vector DB required
- Multimodal retrieval: searches both text and images (charts, diagrams, screenshots, product images, graphs) in documents
- Image search is vector-based, not OCR-based — searches by visual characteristics
- Citation metadata: clickable references back to original sources for fact-checking and traceability
- Metadata filtering for scoped queries
- Asynchronous upload/index polling with duplicate-file checks and MIME type detection
- Queries wrapped as content objects and passed to
generate_contentwith file search tool enabled - Embedding model locked at store creation — cannot be changed later
- Favorable pricing: storage and query-time embedding are free; charges focus on indexing and retrieved context tokens
- Separate store required for multimodal (image-capable embedding mode)
Superpowers
Gemini File Search matters because real business documents are not plain text. PDFs, manuals, and internal knowledge bases are full of charts, diagrams, screenshots, and product images — and most RAG systems simply discard that visual information. Gemini File Search indexes both text and images in the same managed store, enabling semantic search across the full content of a document. The no-separate-vector-DB architecture simplifies infrastructure significantly: upload, index, query, get grounded answers with citations. For enterprise knowledge bases with mixed document types, this is a compelling 10x cost reduction path compared to building and hosting a custom multimodal RAG pipeline.
Pricing
Storage and query-time embedding are free. Charges apply for indexing and retrieved context tokens.