Скачать или смотреть Build a Document Search RAG AI System in less than 20 Minutes | End-to-End Project

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GitHub Repo: https://github.com/iQuantC/RAG_System...

⚡ Description

Are you ready to create your own personalized AI system that can read and understand PDFs? In this step-by-step tutorial, we build a Retrieval-Augmented Generation (RAG) application from scratch — powered by Hugging Face Transformers, Gradio for the web UI, and your local GPU with blazing-fast performance.


In this video, you’ll learn how to:

1. Build an End-to-End RAG pipeline
2. Upload and parse real-world PDF documents
3. Use Hugging Face embeddings to convert text into vector space
4. Store and retrieve knowledge with FAISS vector database
5. Generate accurate answers using Transformers-based LLMs
6. Deploy a responsive web app using Gradio
7. Run the whole project locally with GPU acceleration (RTX 4070 tested!)
8. Fix common issues (token limits, hallucination, CUDA OOM, etc.)
9. Handle and query documents like research papers and tech manuals
10. Make your own AI assistant trained on your own data


🚀 Tech Stack Used:

1. Python 3.10 / 3.12
2. Hugging Face Transformers
3. LangChain
4. FAISS Vector Store
5. Gradio for the UI
6. PyMuPDF (for accurate PDF parsing)
7. CUDA & PyTorch (for GPU acceleration)


💡 Whether you're into LLMOps, RAG architectures, document search engines, or just want your AI to understand your files — this project is for YOU.

🔥 Don’t forget to like 👍, comment 💬, and subscribe 🔔 if you love AI tutorials that go beyond the surface.

#RAG #HuggingFace #Gradio #AIProject #PDFtoAI #LLMOps #LangChain #Transformers #FAISS #OpenSourceAI #TechTutorial


💬 Chapters:

0:00 - Intro & What is RAG?
2:01 - Project Overview
3:13 - Setup & Requirements
5:09 - Loading the LLM & GPU
5:57 - Building the PDF Ingestion Pipeline with Embeddings & FAISS
8:48 - Generate Sample PDF file from text file
11:21 - Building & Loading the Gradio Web UI
14:45 - Querying the App
17:45 - Closing Tips & Clean Up

Disclaimer: This video is for educational purposes only. The tools and technologies demonstrated are subject to change, and viewers are encouraged to refer to the official documentation for the most up-to-date information.

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Happy LLMOpsing! 🎉