TL;DRAI

Azure AI Search uses HNSW vector search for RAG, converting documents to embeddings for semantic matching over keyword retrieval. Semantic retrieval cuts LLM hallucinations and improves answer accuracy, making vector indexing essential for production AI deployments.

In this article, we will understand how vector search works in Azure AI Search and how to use it as the retrieval layer in a Retrieval-Augmented Generation (RAG) system. The article is meant for software engineers. We will not stop at theory. We will build a small, working example that you can run on your own machine and follow along step by step.

By the end, you will have a small document search service that takes a user question, finds the most relevant text using vector similarity, and prepares the context that you can pass to a language model.

Please note that Azure AI Search was earlier called Azure Cognitive Search. The service was renamed, but many older articles and code samples still use the old name. The concepts are the same.

Let us begin.

What is a RAG system, in short

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Building a Vector Index in Azure AI Search: HNSW, Profiles, and RAG Retrieval

In this article, we will understand how vector search works in Azure AI Search and how to use it as...

martedì 2 giugno 2026 New tab

TL;DRAI

2,457 words~11 min read

Please note that Azure AI Search was earlier called Azure Cognitive Search. The service was renamed, but many older articles and code samples still use the old name. The concepts are the same.

Let us begin.

What is a RAG system, in short

Building a Vector Index in Azure AI Search: HNSW, Profiles, and RAG Retrieval

Building a Vector Index in Azure AI Search: HNSW, Profiles, and RAG Retrieval

Other newsrooms on this story

Related reading

I Built a Vector Search Engine from Scratch — Here's What I Learned

Improved Multitenancy Support in Vector Search: Introducing Flat Indexes

Vector Search at Scale: Why Your Index Isn't as Healthy as You Think

Advantages of Building a Vector Search Solution

RAG and Vector Search with pgvector and Amazon Bedrock (Part 4)

Context Graphs vs. Vector Search: When RAG Falls Short

Other newsrooms on this story

Related reading

I Built a Vector Search Engine from Scratch — Here's What I Learned

Improved Multitenancy Support in Vector Search: Introducing Flat Indexes

Vector Search at Scale: Why Your Index Isn't as Healthy as You Think

Advantages of Building a Vector Search Solution

RAG and Vector Search with pgvector and Amazon Bedrock (Part 4)

Context Graphs vs. Vector Search: When RAG Falls Short