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What is Azure AI Search?

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Azure AI Search, formerly known as "Azure Cognitive Search", provides secure information retrieval at scale over user-owned content in traditional and conversational search applications. Your code or a tool invokes data ingestion (indexing) to create and load an index. Optionally, you can add cognitive skills to apply AI processes during indexing. Using Azure AI services can add new information and structures that are useful for search and other scenarios.

On the other side of your service, your application code issues query requests and handles responses. The search experience is defined in your client by using functionality from Azure AI Search, with query execution over a persisted index that you create, own, and store in your service.

Azure AI Search is important functionality in applications. The ability to rapidly find relevant data is essential to the end-user experience and results. The Azure AI Search engine uses AI functionality that helps applications work in a more human-like manner and make associations that go beyond mere keyword matching. Azure AI zervices can help your end users find what they need to know, faster.

Diagram that shows Azure AI Search.

Functionality is exposed through a simple REST API or .NET SDK that masks the inherent complexity of information retrieval. In addition to APIs, the Azure portal provides administration and content management support, with tools for prototyping and querying your indexes. Because the service runs in the cloud, infrastructure and availability are managed by Microsoft.

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

  • Consolidation of heterogeneous content types into a private, single, searchable index. Queries are always over an index that you create and load with documents. The index always resides in the cloud on your Azure AI Search instance. You can populate an index with streams of JSON documents from any source or platform. Alternatively, for content sourced on Azure, you can use an indexer to pull data into an index. Index definition and management/ownership is a key reason for using Azure AI Search.
  • Raw content includes large undifferentiated text, image files, or application files such as Microsoft Office content types in an Azure data source such as Azure Blob Storage or Azure Cosmos DB. You can apply AI skills during indexing to add structure or extract meaning from image and application files.
  • Easy implementation of search-related features. Azure AI Search APIs simplify query construction, faceted navigation, filters (including geo-spatial search), synonym mapping, type-ahead queries, and relevance tuning. Using built-in features, you can satisfy user expectations for a search experience similar to commercial web search engines.
  • Indexing unstructured text or extracting text and information from image files. The AI enrichment feature of Azure AI Search adds AI processing to an indexing pipeline. Some common use cases include OCR over scanned documents, entity recognition and key phrase extraction over large documents, language detection and text translation, and sentiment analysis.
  • Linguistic requirements satisfied by using the custom and language analyzers of Azure AI Search. 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.

Step 1: Provision the service

You can provision an Azure AI Search instance in the Azure portal or through the Azure Resource Manager REST API. You can choose either the free service shared with other subscribers or a paid tier that dedicates resources used only by your service. For paid tiers, you can scale a service in two dimensions:

  • Add replicas to grow your capacity to handle heavy query loads.
  • Add partitions to grow storage for more documents.

By handling document storage and query throughput separately, you can calibrate resourcing based on production requirements.

Step 2: Create an index

Before you can upload searchable content, you must first define an Azure AI Search index. An index is like a database table that holds your data and can accept search queries. You define the index schema to map to reflect the structure of the documents you want to search, similar to fields in a database.

A schema can be created in the Azure portal or programmatically by using the .NET SDK or REST API.

Step 3: Load data

After you define an index, you're ready to upload content. You can use either a push or pull model.

The pull model retrieves data from external data sources. It's supported through indexers that streamline and automate aspects of data ingestion, such as connecting to, reading, and serializing data. Indexers are available for Azure Cosmos DB, Azure SQL Database, Azure Blob Storage, and SQL Server hosted in an Azure Virtual Machines instance. You can configure an indexer for on-demand or scheduled data refresh.

The push model is provided through the SDK or REST APIs used for sending updated documents to an index. You can push data from virtually any dataset by using the JSON format. For more information, see Add, update, or delete documents and How to use the .NET SDK for guidance on loading data.

After populating an index, you can issue search queries to your service endpoint by using simple HTTP requests with REST APIs or the .NET SDK. Step through creating your first search application to build and then extend a web page that collects user input and handles results. You can also use Visual Studio Code with a REST Client calls or the built-in Search explorer in the Azure portal to query an existing index.

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