Gemini File Search 2.0 Simplifies Multimodal RAG to API Calls

AI with Surya2026-05-07go watch the original →

the gist

Gemini File Search 2.0 auto-handles document chunking, multimodal embedding of text and images into one vector space via Embedding 2, storage, retrieval, and generation after simple upload to a managed store; demo on Transformer paper answers diagram queries like 'add & norm' between attention and feed-forward layers.

The Breakthrough

Gemini File Search 2.0 collapses the full multimodal RAG stack into one managed file store and API calls by automating chunking, Embedding 2-based text-and-image embedding into a shared vector space, vector storage, retrieval, and generation.

What Actually Worked

Developers create a file search store and configure it for multimodal embeddings; the store becomes the active location for documents.
They upload a document like the "Attention Is All You Need" PDF directly to the store, which triggers asynchronous indexing that chunks the content, embeds text and figures/diagrams, and performs semantic clustering in a unified vector space.
The API supports queries combining text and visuals, such as "based on the architecture diagram in figure one what exactly comes between the multi head attention layer and the feed forward layer in the encoder"; it returns "add & norm" by referencing page 3's diagram.
After one-time ingestion to the store, the API performs real-time embeddings, vector storage, retrieval, and LLM generation without separate infrastructure.

Context

The author built an interactive app to demonstrate Gemini File Search 2.0 on the Transformer paper, proving it grounds answers in both text and diagrams. Traditional multimodal RAG demanded heavy engineering: separate document parsing for tables/lists/images, custom chunking with overlap logic, dedicated embeddings API calls, vector database management, and retriever-LLM orchestration. File Search 2.0, powered by Gemini Embedding 2's multimodal vector space, eliminates this stack for faster prototyping of RAG apps, though it does not replace all custom RAG needs.

Notable Quotes

"It did the chunking it also embedded the text and also embedded the figures and the diagrams all into the same vector space."
"This is possible because of... embeddings 2 which is the latest embeddings model from Google which allows you to create a multi-dimensional multimodal vector space in one shot."
"This will not actually kill a rag but this is only going to make the process much more easier for anyone who wants to build a multimodal rag system."