Building a Powerful Reasoning Agent with DeepSeek & GPT-3.5
📝 In this tutorial, we explore how to create a reasoning extraction agent that enhances GPT-3.5 Turbo's capabilities to compete with more advanced models like Claude 3.5 Sonnet. Learn how to leverage Deep Seek's reasoning capabilities to improve smaller language models' performance.
📚 Full Code Implementation:
GitHub Repository: Please STAR the repo https://github.com/MervinPraison/Prai...
Documentation: https://docs.praison.ai/features/reas...
🔧 Key Components:
DeepSeek R1 (Open-source reasoning model)
GPT-3.5 Turbo
PraisonAI Agents Library
🔑 Required API Keys:
OpenAI API Key
DeepSeek API Key
🛠️ Technical Features:
Reasoning extraction pipeline
Multi-agent system architecture
API integration
Performance optimization
🔍 Why Deep Seek R1?
Top-performing open-source model
Superior reasoning capabilities
Cost-effective solution
Competitive benchmark results
Like and Subscribe
Hit the notification bell
Share with fellow developers
Leave your feedback in comments
#AIAgent #DeepLearning #Programming #Python #Tutorial #TechEducation #MachineLearning #ArtificialIntelligence #CodingTutorial #TechTutorial
Timestamp:
0:00 - Introduction to reasoning extract agent
0:18 - Concept explanation: Using reasoning model with small models
0:32 - Example with GPT 3.5 Turbo
0:57 - Deep Seek reasoning process demo
1:32 - Tutorial setup and requirements
2:31 - Code implementation walkthrough
3:38 - Running the code and results demo
4:18 - Conclusion
