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RAG Simple - MEAI Vectors Memory

This sample demonstrates Retrieval-Augmented Generation (RAG) using Microsoft.Extensions.AI with Azure OpenAI and an in-memory vector store.

Prerequisites

  • .NET 10.0 SDK
  • Azure OpenAI / Microsoft Foundry endpoint and API key

Setup

Set your Azure OpenAI credentials using user secrets:

cd samples/CoreSamples/RAGSimple-02MEAIVectorsMemory
dotnet user-secrets set "endpoint" "https://<your-endpoint>.services.ai.azure.com/"
dotnet user-secrets set "apikey" "<your-api-key>"
dotnet user-secrets set "embeddingModelName" "text-embedding-3-small"

Running the Sample

dotnet run

What This Sample Does

  1. Creates an in-memory vector store
  2. Loads a collection of movies with descriptions
  3. Generates embeddings for each movie using Azure OpenAI's text-embedding-3-small model
  4. Performs a vector search based on a query
  5. Returns the most relevant movies

Code Overview

The sample uses:

  • Microsoft.Extensions.AI for embeddings generation
  • Azure.AI.OpenAI for connecting to Azure OpenAI / Microsoft Foundry
  • Microsoft.Extensions.VectorData for in-memory vector storage

Alternative Providers

See the other RAG samples (RAGSimple-03, RAGSimple-04) for Azure AI Search and Qdrant vector store examples.