Inspiration
What it does
AI Study & Document Intelligence Agent is an AI-powered study assistant that helps students learn from their own PDF notes. Users can upload a document, ask questions, get simple answers, generate summaries, and create practice questions. The system uses RAG (Retrieval-Augmented Generation) to find relevant information from the uploaded document before generating an answer. It is built using Python, FastAPI, Streamlit, and Docker, with support for Nebius cloud and NVIDIA GPU infrastructure.
How we built it
How I Built the Project I built the AI Study & Document Intelligence Agent in these steps: PDF Upload – Users upload their study notes or documents. Text Extraction – Python extracts text from the PDF. Text Chunking – Large documents are divided into smaller sections. Embeddings – The text is converted into numerical vectors. Vector Search – Relevant sections are found based on the user's question. RAG – The retrieved information is given to the AI model as context. Answer Generation – The LLM generates an answer based on the document. User Interface – Streamlit provides a simple interface for uploading and asking questions. Backend – FastAPI handles the application APIs. Deployment – Docker packages the application, with the architecture designed for Nebius cloud and NVIDIA GPUs.
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