데브허브 | DEVHUB | 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
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Streamlit
Present AI agents framework
📝 Installation Steps:
Download Ollama from olama.com
Pull DeepSeek R1 model
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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!
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