Upendra Valera@uvtechnologyins838

AI-Powered Interactive Roadmap Generation for Learning Our system transforms unstructured documents into structured, interactive learning roadmaps using Retrieval-Augmented Generation (RAG) and Chroma as a vector store. It helps learners navigate complex materials efficiently by consolidating key topics into logical, visually structured nodes. How It Works: Chunking & Metadata Extraction – Documents are split into chunks (~1000 tokens), each tagged with chunk_id, topic, keywords, and summary for efficient retrieval. Roadmap Generation – Instead of mapping chunks directly, the LLM organizes related content into structured learning nodes, ensuring a logical study flow. Each node is assigned a difficulty level to help learners prioritize topics effectively. Quiz-Based Reinforcement – AI-generated quizzes for each node encourage active learning. Questions reference chunk IDs to maintain accuracy and relevance. Interactive Q&A – Users can ask topic-specific questions, and the system retrieves relevant chunks to generate informed responses. If gaps exist, it suggests further reading. Web-Based Visualization – The structured roadmap is displayed in an interactive web interface, allowing users to explore topics, track progress, attempt quizzes, and engage with AI-powered insights. This system enhances comprehension, retention, and engagement by transforming static documents into an intuitive, adaptive, and interactive learning experience.