UNIVY
AI-Powered Study Helper
Transform lecture scripts, slides, and altklausuren into structured study materials
To help students learn smarter by automatically generating traceable, structured, and context-aware study materials from their university resources. By combining document parsing, semantic analysis, and knowledge graph building, UNIVY bridges the gap between passive reading and active learning.
Uploads and parses scripts, slides, altklausuren into structured document representations.
Generates semantic notes with clickable references to the original source.
Creates flashcards from lecture content and old exam questions.
Answers altklausur questions based on provided scripts and slides using document-grounded RAG.
Builds a knowledge graph to visualize key concepts and their relationships.
Docling PDF Viewer
View scripts, slides, and exams with clickable highlights and anchors
Smart Notes
AI-generated, context-aware notes linked to document locations
Flashcard Generator
Creates Q&A pairs from parsed material for active recall
Altklausur Answering Mode
Automatically answers past exam questions using LightRAG
Node-Entity Graph
Visual map of entities and their connections from uploaded content
Source Tracing
Notes and flashcards are always linked back to script/slides
🖥 Frontend: React
- • Viewer: Docling-based lightweight PDF viewer
- • UI: MUI / Tailwind (customizable)
- • State Management: Zustand or Context API
- • Components: Note Panel, Flashcard Deck, Graph View, Altklausur Answerer
⚙️ Backend: FastAPI
- • Upload & Document Ingestion: FastAPI routes for file parsing
- • RAG Engine: LightRAG for document-grounded Q&A and flashcard support
- • Worker Queue: Celery
🧠 AI & Embeddings
- • Document Parsing: Custom logic + LLM + PDFMiner/MinerU
- • Sentence Chunking: spaCy or LLM tokenizer
- • Embedding: OpenAI Embeddings / LightRAG
- • QA / Notes / Flashcards: GPT-4-turbo / Claude / Custom Prompts
- • Node-Entity Graph: Entity linking via LLM + Neo4j or simple JSON graph schema
- 1.User uploads PDF → FastAPI receives & stores document.
- 2.Document is parsed → Sections, headings, and sentences extracted.
- 3.Content is chunked & embedded via LightRAG.
- 4.Notes and flashcards are generated from embeddings and semantic cues.
- 5.Altklausur mode extracts questions, answers using grounded QA.
- 6.Entity graph builder maps key terms and relationships.
- 7.Frontend renders all outputs, traceable and interactive.
Upload your ML lecture script, slides, and altklausur. UNIVY parses everything and generates:
- • A sidebar with highlighted smart notes linked to the PDF
- • A flashcard deck based on key concepts
- • Altklausur answers grounded in script context
- • A graph view showing key topic relationships