Google Enhances Gemini API File Search with Multimodal Capabilities and Metadata Filtering

Story Summary
Google has updated its Gemini API File Search tool to support multimodal retrieval, allowing developers to build more sophisticated retrieval-augmented generation (RAG) systems. Powered by the Gemini Embedding 2 model, the tool now natively processes both text and image data, enabling semantic searches across visual assets. To improve data management and retrieval accuracy, Google introduced custom metadata filtering, which allows developers to apply key-value labels to unstructured data for precise scoping. Additionally, the update includes page-level citations, providing verifiable source tracking for responses derived from large documents. These features aim to reduce hallucinations and improve context management, helping developers scale production applications by providing agents with more structured, verifiable access to diverse datasets.





