Create a Vector RAG Index in Minutes with Azure AI Search Wizard
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Discover how to build a vector-powered RAG (Retrieval-Augmented Generation) workflow inside Azure AI Search — without writing a single line of code.
In this step-by-step tutorial, you’ll learn how to use the Import Data Wizard in the Azure portal to:
Upload your documents to Azure Blob Storage
Create and connect your embedding model through Azure AI Foundry
Automatically configure your vector index for semantic similarity search
By the end of this video, you’ll have a fully functional RAG-ready vector index that you can use in your own AI applications, chatbots, or semantic retrieval systems — all powered by Azure.
👨💻 Perfect for: Developers, data scientists, and AI builders exploring Azure AI’s new vector capabilities.
▬▬▬▬▬▬ C H A P T E R S ▬▬▬▬▬▬
00:00:00 Intro
00:00:41 How to upload files to Azure Blob Storage
00:01:57 How to deploy a Vector Embedding AI model in Azure AI Foundry
00:03:20 How to use Import Data Wizard for vector search
00:07:48 How do I Integrate Azure AI Search?
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Check out the Azure AI Search Masterclass!
https://youtu.be/yu4M7OKjnR4?si=VS7B1PZ5jny6c4go
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Chapters (5)
Intro
0:41
How to upload files to Azure Blob Storage
1:57
How to deploy a Vector Embedding AI model in Azure AI Foundry
3:20
How to use Import Data Wizard for vector search
7:48
How do I Integrate Azure AI Search?
🎓
Tutor Explanation
DeepCamp AI