Microsoft Developer, featuring Davide, presents a practical guide to building an agentic RAG solution with Azure SQL, OpenAI, and Azure Web Apps, focusing on both cost-efficiency and technical depth.

Building an Agentic RAG Solution with Azure SQL, OpenAI, and Web Apps

Join Davide in this Microsoft Developer video as he constructs a budget-friendly agentic Retrieval-Augmented Generation (RAG) solution using Azure SQL, Azure OpenAI, and Web Apps. The walkthrough covers essential architecture blueprints, backend and frontend practices, and integration strategies for AI-driven search and interaction.

Key Sections

Blueprint Overview: Understand the overall design (00:21)
Natural Language Resource Search: Learn how to find code samples and resources using AI-powered natural language queries (2:00)
Backend Setup: Discover how to use Data API builder on Azure Container Apps (3:06)
Frontend: Implement the user interface with Azure Static Web Apps (3:20)
AI Integration: Utilize https://ai.awesome.azuresql.dev and Azure SQL DB Samples for AI Agentic RAG search capabilities (4:36, 6:11)
Behind the Scenes: Explore the technical magic powering the solution (7:02 - 12:19)
LangChain Examples: See practical RAG implementation using LangChain (12:19)
Budget Breakdown: Learn strategies to keep your solution cost-effective (13:14)

Technologies and Tools

Azure SQL
Azure OpenAI Service
Azure Web Apps (Static Web Apps)
Data API Builder
Azure Container Apps
LangChain

Learning Outcomes

Build a Retrieval-Augmented Generation solution leveraging Azure’s data and AI services
Integrate AI search, natural language interfaces, and structured APIs
Deploy frontend and backend components on Azure within a limited budget

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