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Gemini API File Search is now multimodal

read original get OpenAI GPT-4 Multimodal Model → more articles
Why This Matters

The expansion of Gemini API's File Search to support multimodal data marks a significant advancement in retrieval-augmented generation systems, enabling more intuitive and efficient processing of both text and images. This development enhances the capabilities of applications across industries, from creative agencies to enterprise solutions, by improving data organization, search accuracy, and transparency. Ultimately, it empowers developers and businesses to build smarter, more context-aware AI tools that better serve user needs.

Key Takeaways

Today, we are expanding the Gemini API’s File Search tool. You can now build retrieval-augmented generation (RAG) systems with multimodal data and custom metadata. We’re also introducing page citations to improve grounding and transparency.

Whether you are prototyping a weekend project or scaling a production application for thousands of users, your RAG systems can now natively process and better organize your text and visual data.

Give your apps a photographic memory

File Search now processes images and text together. Powered by the Gemini Embedding 2 model, the tool understands native image data, providing your agents contextual awareness.

Think of a creative agency trying to dig up a specific visual asset. Instead of relying on keywords or filenames, your app can search an entire archive for an image matching a specific emotional tone or visual style described in a natural language brief.

See how developers are already using it: