Implementing RAG with Azure SQL and AI

Sep 27, 2024

Lecture Notes: RAG with .NET, AI, and Azure SQL

Introduction

  • Speaker: Davide Mauri, Principal Product Manager of Azure SQL
  • Topic: RAG (Retrieval Augmented Generation) on data using .NET, AI, and Azure SQL
  • Objective: Demonstrate how to utilize RAG with Azure SQL, .NET, and AI

Azure SQL Overview

  • Azure SQL: Enterprise AI database
    • Modern relational database with multi-model support
    • Supports JSON, graph data, XML, geospatial, and column store
    • Scalable up to 100 TB, 80 vCore, 30 replicas
    • Vector support in early preview

Benefits for Developers

  • Security: Enterprise-grade security with encryption
  • Integration: Connects easily with Azure services (Azure Functions, Static Web Apps, etc.)
  • Library Support: Compatible with .NET EF Core, Semantic Kernel, Long Chain, etc.

AI in Azure SQL

  • Use Cases
    • Vector search to enhance database queries
    • Store chatbot memories with Semantic Kernel
    • RAG: Retrieve data using semantic and vector search, then generate responses with AI

RAG Pattern: Steps

  1. Hybrid Filtering: Use semantic and vector search to filter relevant data
  2. AI Model Interaction: Send filtered data to an AI model (e.g., GPT) to generate natural language responses

System Architecture

  • Components
    • Azure SQL, Azure Functions, OpenAI, Static Web Apps, Data API Builder
    • Frontend: React application
    • Backend: REST/GraphQL endpoint for frontend interaction

Example Application

  • Session Database

    • Stores titles, abstracts, and speakers with embeddings and vectors
    • Uses Azure Functions to update embeddings upon data changes

  • Data API Builder

    • Exposes database as REST/GraphQL endpoints
    • Integrated with Static Web Apps

  • Frontend Interaction

    • Query sessions based on topics using natural language
    • Implemented as a React application

How to Implement

  • Setup

    • Deploy using GitHub repository and AZD for Azure
    • Local deployment possible without Azure (except Azure SQL preview)

  • Functionality

    • Automatic embedding generation using Azure Functions
    • Use OpenAI to convert text to embeddings
    • Store and search vectors in Azure SQL

Additional Details

  • Data API Builder Configuration

    • Specify REST/GraphQL exposure in configuration files

  • Stored Procedures and Endpoints

    • Example procedures for embedding retrieval and vector search

Conclusion

  • Resources: GitHub repository available for setup
  • Future Steps: Explore early preview for vector search in Azure SQL
  • Encouragement: Try the setup on your data and experiment with the architecture

This lecture provided a comprehensive overview of implementing RAG solutions using Azure SQL, showcasing its integration with other Azure services and AI models to provide powerful data retrieval and augmentation.