What's Azure AI Search?

Azure AI Search (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.

Information retrieval is foundational to any app that surfaces text and vectors. Common scenarios include catalog or document search, data exploration, and increasingly chat-style apps over proprietary grounding data. When you create a search service, you work with the following capabilities:

• A search engine for vector search and full text and hybrid search over a search index
• Rich indexing with integrated data chunking and vectorization (preview), lexical analysis for text, and optional applied AI for content extraction and transformation
• Rich query syntax for vector queries, text search, hybrid queries, fuzzy search, autocomplete, geo-search and others
• Azure scale, security, and reach
• Azure integration at the data layer, machine learning layer, Azure AI services and Azure OpenAI

Architecturally, a search service sits between the external data stores that contain your un-indexed data, and your client app that sends query requests to a search index and handles the response.

In your client app, the search experience is defined using APIs from Azure AI Search, and can include relevance tuning, semantic ranking, autocomplete, synonym matching, fuzzy matching, pattern matching, filter, and sort.

Across the Azure platform, Azure AI Search can integrate with other Azure services in the form of indexers that automate data ingestion/retrieval from Azure data sources, and skillsets that incorporate consumable AI from Azure AI services, such as image and natural language processing, or custom AI that you create in Azure Machine Learning or wrap inside Azure Functions.

Inside a search service

On the search service itself, the two primary workloads are indexing and querying.

• Indexing is an intake process that loads content into your search service and makes it searchable. Internally, inbound text is processed into tokens and stored in inverted indexes, and inbound vectors are stored in vector indexes. The document format that Azure AI Search can index is JSON. You can upload JSON documents that you've assembled, or use an indexer to retrieve and serialize your data into JSON.

  Applied AI through a skillset extends indexing with image and language models. If you have images or large unstructured text in source document, you can attach skills that perform OCR, describe images, infer structure, translate text and more. You can also attach skills that perform data chunking and vectorization.

• Querying can happen once an index is populated with searchable content, when your client app sends query requests to a search service and handles responses. All query execution is over a search index that you control.

  Semantic ranking is an extension of query execution. It adds secondary ranking, using language understanding to reevalute a result set, promoting the most semantically relevant results to the top.

Why use Azure AI Search?

Azure AI Search is well suited for the following application scenarios:

• Use it for traditional full text search and next-generation vector similarity search. Back your generative AI apps with information retrieval that leverages the strengths of both keyword and similarity search. Use both modalities to retrieve the most relevant results.

• Consolidate heterogeneous content into a user-defined and populated search index composed of vectors and text. You maintain ownership and control over what's searchable.

• Integrate data chunking and vectorization for generative AI and RAG apps.

• Apply granular access control at the document level.

• Offload indexing and query workloads onto a dedicated search service.

• Easily implement search-related features: relevance tuning, faceted navigation, filters (including geo-spatial search), synonym mapping, and autocomplete.

• Transform large undifferentiated text or image files, or application files stored in Azure Blob Storage or Azure Cosmos DB, into searchable chunks. This is achieved during indexing through cognitive skills that add external processing from Azure AI.

• Add linguistic or custom text analysis. If you have non-English content, Azure AI Search supports both Lucene analyzers and Microsoft's natural language processors. You can also configure analyzers to achieve specialized processing of raw content, such as filtering out diacritics, or recognizing and preserving patterns in strings.

For more information about specific functionality, see Features of Azure AI Search

How to get started

Functionality is exposed through the Azure portal, simple REST APIs, or Azure SDKs like the Azure SDK for .NET. The Azure portal supports service administration and content management, with tools for prototyping and querying your indexes and skillsets.

An end-to-end exploration of core search features can be accomplished in four steps:

1. Decide on a tier and region. One free search service is allowed per subscription. All quickstarts can be completed on the free tier. For more capacity and capabilities, you'll need a billable tier.

2. Create a search service in the Azure portal.

3. Start with Import data wizard. Choose a built-in sample or a supported data source to create, load, and query an index in minutes.

4. Finish with Search Explorer, using a portal client to query the search index you just created.

Alternatively, you can create, load, and query a search index in atomic steps:

1. Create a search index using the portal, REST API, .NET SDK, or another SDK. The index schema defines the structure of searchable content.

2. Upload content using the "push" model to push JSON documents from any source, or use the "pull" model (indexers) if your source data is of a supported type.

3. Query an index using Search explorer in the portal, REST API, .NET SDK, or another SDK.

Compare search options

Customers often ask how Azure AI Search compares with other search-related solutions. The following table summarizes key differences.

Key strengths include:

• Support for vector and nonvector (text) indexing and queries. With vector similarity search, you can find information that’s semantically similar to search queries, even if the search terms aren’t exact matches. Use hybrid search for the best of keyword and vector search.
• Ranking and relevance tuning through semantic ranking and scoring profiles. Query syntax supports term boosting and field prioritization.
• Azure data integration (crawlers) at the indexing layer.
• Azure AI integration for transformations that make content text and vector searchable.
• Microsoft Entra security for trusted connections, and Azure Private Link for private connections in no-internet scenarios.
• Full search experience: Linguistic and custom text analysis in 56 languages. Faceting, autocomplete queries and suggested results, and synonyms.
• Azure scale, reliability, and global reach.