Detailed Introduction
DeepTutor, developed by the HKU Data Intelligence Lab, is a multi-agent personalized learning system designed to provide end-to-end support from knowledge retrieval and understanding to practice and assessment. The platform combines Retrieval-Augmented Generation (RAG), knowledge graph capabilities, and multi-agent collaborative reasoning to deliver document-level Q&A, automated exercise generation, interactive visual explanations, and simulated exam scenarios with traceable citations and session memory.
Main Features
- Large-scale document Q&A: build knowledge bases and deliver cited answers via vector retrieval and RAG.
- Multi-agent problem solving: dual-loop architecture for analysis and solving with real-time streaming reasoning.
- Intelligent exercise generation: produce and validate practice questions by difficulty and exam style, supporting batch and mimic modes.
- Interactive learning visualization: transform complex concepts into interactive step-by-step demonstrations and visual aids.
Use Cases
Ideal for university teaching, online course platforms, literature reviews, and self-learners: instructors can rapidly build question banks and mock exams; students benefit from interactive explanations and personalized practice; researchers can run deep retrieval and report generation for systematic reviews and idea synthesis.
Technical Features
The system uses Python/FastAPI for backend and Next.js for frontend, supports Docker deployment and local development. The retrieval layer uses embeddings and knowledge graph structures; the research pipeline features a parallelized dynamic task queue and centralized citation management, and the platform supports plugin-style tool integrations (web search, code execution, PDF parsing, etc.).