I Ditched Traditional RAG for Agentic RAG and Got SHOCKING Results!

🔥 Master Agentic RAG: The Future of AI Knowledge Systems
In this comprehensive tutorial, we dive deep into Agentic RAG and how it's revolutionizing AI knowledge systems. Learn how to implement it step-by-step using Phidata!

🎯 What You'll Learn:
Traditional RAG vs Agentic RAG
Complete Implementation Tutorial
Creating User Interface
Benefits and Limitations
Real-world Applications

⚡️ Key Components Covered:

Query Decomposition
Multiple Source Intelligence
Dynamic Query Optimization
Self-validating Results
Integration with Phidata
🛠 Tools & Resources Used:

OpenAI GPT-4
Phidata Framework
PostgreSQL Database
Docker
Python

📦 Required Packages:
Copypip install Phidata
🔗 Important Links:
OpenAI Platform: https://platform.openai.com
Docker Installation: https://docker.com
https://mer.vin/2024/12/agentic-rag-p...

📚 Additional Resources:
OpenAI API Documentation: https://platform.openai.com/docs
PostgreSQL Documentation: https://www.postgresql.org/docs/

🔍 Implementation Steps:
Install Agentic Framework (Phidata)
Configure Knowledge Sources
Launch Your Agent
Create User Interface
Test and Optimize

✨ Features of Agentic RAG:

Autonomous Knowledge Navigation
Multi-source Intelligence
Self-improving Retrieval
Real-time Knowledge Validation
Zero Hallucinations

⚠️ Prerequisites:

Basic Python Knowledge
OpenAI API Key
Docker Installation
PostgreSQL Basic Understanding

0:00 - Introduction to Agentic RAG
1:13 - Implementation Overview
2:05 - Traditional RAG Explained
2:33 - Introduction to Agentic RAG
3:00 - Step 1: Installation & Setup
3:45 - Step 2: Creating Traditional RAG System
4:23 - Knowledge Base Setup
5:20 - Testing Simple Questions
6:41 - Step 3: Implementing Agentic RAG
7:42 - Creating User Interface
8:43 - Benefits and Drawbacks
9:39 - Conclusion