Скачать с ютуб This DeepSeek AI RAG Agent can REASON! Run it 100% Local! в хорошем качестве

This DeepSeek AI RAG Agent can REASON! Run it 100% Local!

🔥 DeepSeek RAG: Create Powerful AI Agents Locally Learn how to build an intelligent RAG chatbot using DeepSeek and run it 100% locally! In this tutorial, we'll explore how to leverage RAG (Retrieval Augmented Generation) to create more accurate and knowledgeable AI responses. Please Star ⭐ the repo https://github.com/MervinPraison/Prai... https://docs.praison.ai/models/deepseek 🚀 What You'll Learn: Complete setup of DeepSeek RAG agents with local deployment Step-by-step implementation with simple code Building a user interface with Streamlit How to prevent AI hallucination using RAG Working with custom knowledge bases ⚡️ Key Technologies: DeepSeek R1 (7B distilled model) Ollama NOMIC embed text Streamlit Present AI agents framework 📝 Installation Steps: Download Ollama from olama.com Pull DeepSeek R1 model Pull NOMIC embed text Install required packages 💡 Why RAG Matters: Dynamic knowledge evolution Autonomous context optimization Deep contextual awareness Improved accuracy Reduced hallucinations 🎓 Prerequisites: Basic Python knowledge Familiarity with AI concepts Local development environment Timestamp: 0:00 - Introduction to Deep Seek RAG Agent 1:16 - Demo of RAG Chatbot Capabilities 1:43 - Deep Seek R1 Model Introduction 2:12 - Project Build Overview 2:44 - Installation Steps for Ollama 3:33 - Setting Up Required Dependencies 3:50 - Code Implementation Begins 5:11 - Running the Code 6:02 - Creating User Interface 6:30 - Live Demo & Results 7:12 - Conclusion #DeepSeekAI #MachineLearning #AITutorial #RAG #ArtificialIntelligence #Python #Programming ❓Questions? Drop them in the comments below! 🔔 Subscribe for more AI development tutorials and don't forget to hit the notification bell!