Building RAG Applications with Azure AI Search - Complete Guide

Introduction

Retrieval-Augmented Generation (RAG) is revolutionizing how we build AI applications. In this tutorial, I will show you how to leverage Azure AI Search to create enterprise-grade RAG applications that can securely query your data.

Why Azure AI Search?

Azure AI Search provides:

• Enterprise-grade security - Your data stays within your tenant
• Built-in vector search - Semantic understanding out of the box
• Hybrid search - Combine keyword and semantic search
• Scalability - Handle millions of documents

Prerequisites

Before we begin, you will need:

• An Azure subscription
• Azure AI Search service (free tier works!)
• .NET SDK or Python installed

Step 1: Set Up Azure AI Search

First, create your search service in the Azure Portal:

# Using Azure CLI
az search service create \n  --name my-rag-search \n  --resource-group my-rg \n  --sku free

Step 2: Index Your Data

Here is how to index documents with embeddings:

using Azure.Search.Documents;
using Azure.Search.Documents.Indexes;

var indexClient = new SearchIndexClient(
    "https://my-rag-search.search.windows.net",
    new AzureKeyCredential("your-admin-key"));

await indexClient.CreateIndexAsync(new SearchIndex("my-index"));

Step 3: Implement Semantic Search

The magic of RAG comes from semantic search:

var searchClient = indexClient.GetSearchClient("my-index");

var options = new SearchOptions
{
    VectorQueries = new[]
    {
        new RawVectorQuery
        {
            Vector = embedding,
            KNearestNeighborsCount = 3
        }
    },
    Select = { "content", "title" }
};

var results = await searchClient.SearchAsync<SearchDocument>("your question", options);

Step 4: Integrate with LLM

Finally, send the retrieved context to your LLM:

var context = string.Join("
", results.Results.Select(r => r.Document["content"]));
var prompt = $"Answer based on this context:

{context}

Question: {userQuestion}";

Conclusion

Azure AI Search makes building RAG applications straightforward. The combination of vector search, semantic ranking, and enterprise security makes it ideal for production applications.

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Whats Next?

• Try adding custom embeddings
• Experiment with different chunking strategies
• Explore Azure AI Studio for more features

Have questions? Drop them in the comments!

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