Sage-Wiki: An LLM-compiled personal knowledge base (github.com)

Sage-Wiki has emerged as an innovative personal knowledge management tool that utilizes large language models (LLMs) to transform documents into a structured, interlinked wiki. Users can simply drop in their papers, articles, and notes, and the system automatically reads, summarizes, extracts concepts, and writes interconnected content. Notably, Sage-Wiki can scale to handle over 100,000 documents, delivering quick searchability and comprehensive knowledge compilation, marking a significant advancement for researchers and professionals in the AI and machine learning communities. The tool integrates seamlessly with existing workflows, supporting various document formats including Markdown, PDFs, and PowerPoint presentations, while also functioning natively within Obsidian. Its capabilities include enhanced search features using chunk-level indexing and the ability to answer natural language queries based on the compiled knowledge base. Additionally, Sage-Wiki's tiered compilation mechanism ensures that only relevant information is processed, thus optimizing resource efficiency. This combination of features not only enhances knowledge management but also augments the collective intelligence of the wiki by enriching articles with new sources, underscoring its potential impact in the era of AI-driven knowledge discovery.