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RAG Course (Youtube) course

A soon-to-be-uploaded course about RAG (Retrieval-Augmented Generation), including PDF uploading and personalized question generation.

Role

Learner / Developer

Challenge

Implementing PDF upload + text extraction + personalized question generation

Result

Course will demonstrate the RAG pipeline, PDF processing, and dynamic question creation for personalized learning.

Project Overview

RAG Course is a comprehensive learning platform focused on Retrieval-Augmented Generation (RAG). It enables users to upload PDFs (in Persian or English), process the content, and receive personalized questions and guidance based on the material.

Key features include:

  • PDF Upload — Users can upload any PDF, which is then processed and indexed.
  • Text Extraction — Extracts text from PDFs using OCR, including Persian documents.
  • Vector Embeddings — Converts text into embeddings with SentenceTransformer for semantic search and personalized question generation.
  • ChromaDB Integration — Stores embeddings for fast retrieval and reference.
  • Personalized Question Generation — Generates context-aware questions and exercises based on the uploaded material.
  • Full Backend API — Powered by FastAPI to handle uploads, processing, and RAG queries efficiently.
  • Multi-Language Support — Works with Persian and English PDFs seamlessly.

Technologies used:

  • Python for NLP and backend processing
  • FastAPI for API handling
  • OCR for PDF text extraction
  • SentenceTransformer for semantic embeddings
  • ChromaDB for storing and retrieving embeddings
  • JavaScript for frontend interactions

This course demonstrates advanced techniques in RAG pipelines, personalized AI question generation, and PDF content processing. It is an ideal project for anyone looking to combine AI, NLP, and web technologies to create interactive learning tools.