wdoc

wdoc

wdoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It is particularly useful for handling large volumes of diverse document types, making it ideal for researchers, students, and professionals dealing with extensive information sources. Created by a medical student who needed a better way to search through diverse knowledge sources (lectures, Anki cards, PDFs, EPUBs, etc.), this tool was born from frustration with existing RAG solutions for querying and summarizing.

Key Features:

• Supports Multiple File Types: wdoc can process and analyze documents from various formats including PDFs, Anki decks, and more.
• Scalable: Capable of querying tens of thousands of documents simultaneously.
• Customizable: Users can tailor the summarization and querying processes to fit their specific needs.
• High Recall and Specificity: Designed to find a large number of relevant documents using sophisticated embedding searches.
• Markdown Formatting: Outputs are provided in a user-friendly markdown format, making it easy to read and understand.
• Active Development: The project is under continuous improvement, with regular updates and feature enhancements.

Benefits:

• Efficient Information Retrieval: Quickly summarize and query large sets of documents, saving time and effort.
• User-Centric Design: Built with feedback from users to ensure it meets the needs of those who rely on diverse information sources.
• Open Source: Available on GitHub, allowing for community contributions and transparency in development.

Highlights:

• Active Community: Engage with other users and developers to share insights and improvements.
• Documentation and Support: Comprehensive documentation is available to help users get started and troubleshoot issues.