VisQueryPDF processes PDF files by splitting them into chunks, generating vector embeddings via OpenAI or compatible models, and storing those embeddings in a local vector store. Users can submit natural language queries to retrieve the most relevant chunks. Search hits are displayed with highlighted text on the original PDF pages and plotted in a two-dimensional embedding space, allowing interactive exploration of semantic relationships between document segments.

The DALL-E 2 Google Extension allows users to integrate OpenAI's DALL-E 2 image generation capabilities into Google Images. By incorporating AI-generated visuals alongside standard results, it transforms search experiences, inspiring creativity and enabling users to explore a myriad of visual styles and interpretations. Imagine searching for a concept and instantly finding unique art pieces that reflect that idea, making it an invaluable tool for artists, designers, and content creators.

arXiv Viz provides a visual method for exploring and understanding the comprehensive database of research papers available on arXiv. This tool aims to make it easier for researchers, students, and enthusiasts to quickly grasp the key ideas, trends, and connections in academic literature across various fields such as physics, mathematics, computer science, and more. By transforming the traditional text-based search and browsing experience into an intuitive visual interface, arXiv Viz enhances the way users interact with and comprehend scholarly articles.