LLMII uses a local LLM to label metadata and index images. It does not rely on a cloud service or database. A visual language model runs on your computer and is used to create captions and keywords for images in a directory tree. The generated information is then added to each image file's metadata.
This repository provides a Python script to fetch and summarize research papers from arXiv using the free Gemini API. It includes features for summarizing a single paper or multiple papers, easy setup, and automatic daily extraction and summarization based on specific keywords. The tool is designed to help researchers, students, and enthusiasts quickly extract key insights from arXiv papers without manually reading through lengthy documents.
