Built the missing GUI for Gemini File Search managed RAG (github.com)

A new web-based GUI has been developed for managing Google's Gemini File Search (RAG) API, filling a significant gap in functionality for AI practitioners. This intuitive interface allows users to effortlessly upload various document formats—including PDF, TXT, and DOCX—while configuring chunking and adding custom metadata. Notably, it features real-time status polling for document ingestion and a chat-based playground, enabling users to test retrieval capabilities and utilize model selection with citation displays. This development is significant for the AI/ML community as it streamlines the integration of document management with advanced retrieval mechanisms, enhancing the usability of the Gemini File Search API. Built with Next.js and TypeScript, the project employs Tailwind CSS for styling and incorporates TanStack Query for state management. Users can clone the repository, configure the API key, and run the application locally, empowering them to create, manage, and interact with File Search stores efficiently. This GUI not only simplifies the user experience but also promotes broader adoption of Gemini's capabilities in real-world applications, making AI-powered document handling more accessible.