Azure AI Search Overview

Learn about Azure AI Search, a fully managed information retrieval platform for building search experiences and generative AI apps.

Azure AI Search Overview

Azure AI Search is a fully managed, cloud-hosted service that connects your data to AI. The service unifies access to enterprise and web content so agents and LLMs can use context, chat history, and multi-source signals to produce reliable, grounded answers.

What is Azure AI Search?

Azure AI Search is an AI-powered information retrieval platform that helps developers build rich search experiences and generative AI apps that combine large language models (LLMs) with enterprise or web data.

Common use cases include classic search and modern retrieval-augmented generation (RAG) via agentic retrieval. This makes Azure AI Search suitable for both enterprise and consumer scenarios, whether you’re adding search functionality to a website, app, agent, or chatbot.

Key Capabilities

When you create a search service, you unlock the following capabilities:

Two Search Engines

• Classic search: Single requests for predictable, low-latency queries
• Agentic retrieval: Parallel, iterative, LLM-assisted search for complex agent-to-agent workflows

Query Types

• Full-text search: Traditional keyword-based search with BM25 relevance ranking
• Vector search: Semantic similarity search using embeddings
• Hybrid search: Combined full-text and vector search for optimal results
• Multimodal search: Query across text and images in a single pipeline

AI Enrichment

• Chunk, vectorize, and transform raw content to make it searchable
• Built-in skills for OCR, entity recognition, key phrase extraction, and more
• Integrated vectorization with Azure OpenAI and Foundry Tools

Enterprise Features

• Security: Azure scale, security, monitoring, and compliance
• Access control: Document-level permissions and role-based access
• Integrations: Azure OpenAI, Microsoft Foundry, Azure data platforms

Why Use Azure AI Search?

Ground AI Agents

Provide agents and chatbots with proprietary, enterprise, or web data for accurate, context-aware responses

Multi-Source Data

Access data from Azure Blob Storage, Azure Cosmos DB, SharePoint, OneLake, and other supported data sources

Hybrid Search

Combine full-text search with vector search to balance precision and recall

Production Ready

Enterprise security, access control, and compliance through Microsoft Entra and Azure Private Link

Classic Search vs Agentic Retrieval

Aspect	Classic Search	Agentic Retrieval
Search corpus	Search index	Knowledge source
Search target	One index defined by schema	Knowledge base with multiple sources
Query plan	No plan, just a request	LLM-assisted or user-provided plan
Query request	Search documents in an index	Retrieve from knowledge sources
Response	Flattened search results	LLM-formulated answer, activity log, references
Status	Generally available	Public preview

Getting Started

1. Create a Search Service

   Deploy an Azure AI Search service in your preferred region and choose a pricing tier

2. Choose Your Path

   Decide between classic search or agentic retrieval based on your requirements

3. Create an Index

   Define your index schema with fields for text, vectors, and metadata

4. Load Data

   Use push or pull methods to ingest content into your index

5. Query Your Index

   Execute full-text, vector, or hybrid queries to retrieve relevant results

Choose Your Path

Before getting started, make these key decisions:

Choose a Search Engine

• Classic search: Best for traditional app needs with lower costs and complexity
• Agentic retrieval: Ideal for agent workflows and complex RAG scenarios requiring LLM orchestration

Choose a Region

• Select a region that supports your required features
• For agentic retrieval, choose a supported region
• Consider proximity to your data sources and users

Choose an Ingestion Method

• Pull method: Use indexers for supported data sources (Azure Blob, Cosmos DB, SQL, etc.)
• Push method: Upload JSON documents directly for real-time sync or unsupported sources

Do You Need Vectors?

• Required for semantic similarity search and embedding-based retrieval
• Use integrated vectorization to automate embedding generation
• LLMs and agents don’t require vectors unless using similarity search

Architecture

Classic Search Architecture

Agentic Retrieval Architecture

Next Steps

Create a Service

Deploy your Azure AI Search service

Quickstart

Build your first search index

Vector Search

Learn about vector search concepts

Agentic Retrieval

Explore agentic retrieval for AI agents