DeepSeek R1 RAG: Build Your Own AI Document Q&A | Project Code Walkthrough скачать в хорошем качестве

DeepSeek R1 RAG: Build Your Own AI Document Q&A | Project Code Walkthrough

🤯 Get Instant Answers From ANY PDF! 🤯 Tired of sifting through endless documents? In this video, I'll show you how I built an AI-powered Question-Answering system that can instantly answer your questions from uploaded PDFs. Using cutting-edge Retrieval-Augmented Generation (RAG) technology, powered by Groq's blazing-fast DeepSeek-R1 model, this system unlocks the hidden insights within your documents in real-time. What you'll learn: 🔥 How to build your own AI document Q&A system using Streamlit, LangChain, and Groq's DeepSeek-R1. 🚀 How RAG technology works and why it's revolutionizing document processing. 🧠 How to use vector databases (Chroma) to quickly find relevant information. ⚡️ How Groq's DeepSeek-R1 delivers lightning-fast AI responses. 🛠️ Step-by-step walkthrough of the code (`main.py` and `utility.py`). Whether you're a developer, a student, or just curious about the power of AI, this video is for you! Key Technologies: Retrieval-Augmented Generation (RAG) Groq DeepSeek-R1 LangChain Streamlit Chroma (Vector Database) Resources: GitHub Repository: https://github.com/aniket-1177/DocQA-RAG Groq API: https://console.groq.com/keys Learn More About RAG:   / how-rag-works-detailed-explanation-its-com...   👍 Like and subscribe for more AI projects and tutorials! 💬 Leave a comment below with your questions and suggestions. *#AI #RAG #DeepSeek #LangChain #Streamlit #VectorDatabase #PDF #QuestionAnswering #Groq #MachineLearning #Tutorial #Programming*