How to Build a RAG System with Azure OpenAI & Python Step by Step Tutorial

In this tutorial, we walk through how to build a Retrieval-Augmented Generation (RAG) system using Azure OpenAI and Python. This video covers everything from understanding RAG concepts to setting up Azure resources, creating embeddings with Azure Search, deploying models, and developing a complete RAG application with an an example, Bild RAG based travel recommendation system. Follow along as we build this powerful system step by step. Microsoft services and document used to create tutorial. 0:50 - Introduction to Retrieval-Augmented Generation (RAG) 05:07 - Setting Up Azure Resources 05:17 - Create resource group 06:54 - Create open AI resource 08:57 - Create azure AI search resource 10:52 - Create Azure Storage Account 13:01 - Resource setup for complete for solution 15:51 - Upload data to blob storage to create index 17:43 - Deploy AI model 20:48 - Create an Index with Azure AI Search 23:54 - Configure application 26:01 - Create python code, run and code walkthrough Data link used in this session provided by MS - https://aka.ms/own-data-brochures Create Flask REST API Azure open AI - https://youtu.be/Bh2YZVZdHn4 Deploy Azure openAI Flask REST API on Azure web APP - https://youtu.be/ViLHTpvenM0 GitHub link - https://github.com/baijnath4/How-to-Build-a-RAG-System-with-Azure-OpenAI-Python-Step-by-Step-Tutorial/blob/main/