RAG Knowledge Base is a Retrieval-Augmented Generation document Q&A system that lets users upload documents and ask questions, receiving AI-generated answers with precise citations. It uses TF-IDF vectorization to index content and retrieves relevant passages before generating responses, ensuring transparency. Users can manage multiple document collections and explore source materials directly.
Key features
Document upload supporting PDF, TXT, and Markdown AI-powered Q&A with answers from retrieved passages Citation-linked answers showing exact source documents TF-IDF vectorization for accurate document retrieval Multiple document collections with independent indexing Question history with saved answers and exploration Responsive interface designed for research workflows
Angular 
TypeScript 
Tailwind CSS 
RxJS 
Source code 