What happened
Google expanded its Gemini API's File Search tool, enabling retrieval-augmented generation (RAG) systems to process multimodal data, including text and images. Powered by the Gemini Embedding 2 model, the update introduces custom metadata filtering for unstructured data and page citations, linking model responses directly to original source page numbers for improved grounding and transparency.
Why it matters
This expansion reduces complexity for platform engineers and data architects building RAG systems by enabling native processing of visual and textual data within a single tool. Custom metadata filtering improves retrieval accuracy and speed, while page citations enhance verifiability for compliance and trust. This follows Google's continuous expansion of Gemini capabilities, including recent additions like Webhooks and expanded product integrations.
