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Gemini API File Search Now Supports Multimodality, Adding Metadata and Page Citation Features

This article is a translation. Read the Japanese original

With this update, developers can now integrally process image data in addition to text. By leveraging the Gemini Embedding 2 model, it is now possible to search by specifying visual atmosphere or style using natural language.

Additionally, a feature to attach custom metadata to unstructured data has been added. By assigning key-value labels such as department or status and filtering during queries, noise from unrelated documents can be reduced.

Furthermore, a function has been introduced that allows the source of an answer to be traced back to a specific page number. This enables the explicit identification of where an answer derived from a large volume of PDFs originated, contributing to improved fact-checking and reliability.


Source: Gemini API File Search is now multimodal (HN 156pt, 46 comments) (HN Search (backfill), 2026-05-10)