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.

TextDivider is designed to alleviate the hassle of inputting long texts into analysis tools by breaking them down into smaller segments. This free and user-friendly tool efficiently divides papers, articles, and other lengthy writings so they can be more easily managed. It's particularly useful for educators, students, and professionals who often work with extensive text documents. The tool ensures that no part of your text is lost, preserving the original context and meaning while making the text more manageable.