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Table of contents
1.
Introduction
2.
What is Azure Cognitive Search?
3.
What are the Primary Workloads in Azure Search Service?
3.1.
Indexing
3.2.
Querying
4.
What are the Features of Azure Cognitive Search?
4.1.
Indexing features
4.2.
AI enrichment and knowledge mining
4.3.
Query and user experience
4.4.
Security features
4.5.
Programmability
5.
Frequently Asked Questions
5.1.
What is Cognitive Search?
5.2.
How is the functionality exposed in the Cognitive Search?
5.3.
What are the advantages of using Azure Cognitive Search?
5.4.
Why is it beneficial to use Cognitive Search?
5.5.
What are the key features of Azure Cognitive Search?
6.
Conclusion
Last Updated: Mar 27, 2024

Azure Cognitive Search

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Introduction

Any software that presents text material to users relies on search, and frequent situations include catalog or document search, online purchasing, and data exploration. 

In terms of architecture, a search service stands between your unindexed data in external data stores and your client app, which makes query requests to a search index and processes the answer.

When you build a search service, you'll use the following features:

  • A full-text search engine that stores user-created material in a search index.
  • Extensive indexing with text analysis and optional AI enrichment is available for sophisticated content extraction and transformation.
  • Filters, autocomplete, regex, geo-search, and other features augment free-text search.
  • Azure integrates at the data layer, machine learning layer, and AI layer using REST APIs and client libraries(Cognitive Services)

What is Azure Cognitive Search?

Azure Cognitive Search (previously known as "Azure Search") is a cloud search service that provides developers with infrastructure, APIs, and tools for creating powerful search experiences in web, mobile, and corporate applications over private, heterogeneous material.

Cognitive Search can work with other Azure services as indexers that automate data ingestion/retrieval from Azure data sources and skillsets that combine consumable AI from Cognitive Services, such as image and natural language processing, with custom AI created in Azure Machine Learning or wrapped inside Azure Functions.

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What are the Primary Workloads in Azure Search Service?

The two primary workloads on the search service are indexing and querying.

Indexing

Indexing is a content loading and searchability procedure that loads material into your search service. Internally, inbound content is tokenized and saved in inverted indexes for quick scanning. This method can upload any text in the form of JSON documents. You can also add AI enrichment through cognitive skills if your material contains mixed files. By examining the content, AI enrichment can extract text from application files and infer text and structure from non-text files.

Querying

Querying occurs when your client app makes query requests to a search service and processes results or when an index is filled with searchable content. All queries are run through a search index that you control, maintain, and keep in your service. The search experience in your client app is designed using Azure Cognitive Search APIs, and it can feature relevance tuning, autocomplete, synonym matching, fuzzy matching, pattern matching, filtering, and sorting.

What are the Features of Azure Cognitive Search?

Indexing features

  • Data sources - Text from any source may be indexed as long as it is submitted in a JSON format.
  • Nested Data Structures - Within a search index, you may represent nearly any sort of JSON structure using complex types and collections.
  • Linguistic Analysis - Lucene or Microsoft language analyzers handle language-specific linguistics such as verb tenses, gender, irregular plural nouns, word de-compounding, word-breaking, etc. Complex query forms are handled by custom lexical analyzers.

AI enrichment and knowledge mining

  • AI processing during indexing - Adding and combining talents in a skillset, which is then tied to an indexer, is how AI processing is accomplished.
  • Storing enriched content for analysis- A knowledge store is a persistent storing of enhanced material designed for non-search applications such as data science and knowledge mining.
  • Cached enrichments - Cached enrichments that can be reused throughout skillset execution are called incremental enrichment (preview).

Query and user experience

  • Free form text search - For most search-based programs, full-text search is the most common use case. Queries can use a supported syntax.
  • Geospatial search - Geospatial functions filter over and match geographic coordinates.

Security features

  • Data Encryption - Vault may be used to encrypt additional indexes and synonym maps.
  • Endpoint Protection - You may set up IP ranges across which the search service will accept queries using IP rules for inbound firewall support.

Programmability

  • REST - The Service REST API performs data plane activities, such as indexing, queries, and AI enrichment.
  • Azure SDK - It comes for Java, Javascript, .NET, and Python.

Frequently Asked Questions

What is Cognitive Search?

Cognitive search is the latest generation of enterprise search that uses artificial intelligence to enhance users' search queries and retrieve relevant material from various sources. Cognitive search capabilities go beyond traditional search engines by combining several data sources and automating categorization and customization.

How is the functionality exposed in the Cognitive Search?

The functionality is available via a simple REST API or Azure SDKs, such as the Azure SDK for .NET, which hides the fundamental complexity of information retrieval. The Azure site also has tools for designing and querying your indexes and skillsets, as well as service administration and content management. Because the service is hosted in the cloud, Microsoft is responsible for its infrastructure and availability.

What are the advantages of using Azure Cognitive Search?

Some of the advantages are: -

  • Azure Cognitive Services integrates AI and machine learning, essential if you need to make unsearchable material fully text-searchable.
  • Azure Active Directory connectivity for reliable connections and Azure Private Link integration for private connections to a search index in no-internet settings.
  • Text analysis in 56 languages, including linguistic and custom.
  • Rich query language, relevance tuning, semantic ranking, faceting, autocomplete searches and recommended results, and synonyms are all included in the whole search experience.
  • Azure's scalability, dependability, and world-class availability are unrivalled.

Why is it beneficial to use Cognitive Search?

It helps to: -

  • Consolidate disparate material into a user-defined, private search index. Using a specialized search service, offload indexing and querying duties.
  • Relevance tuning, faceted navigation, filters, synonym mapping, and autocomplete are all simple to implement search-related features.
  • Large, undifferentiated text or picture files and application files stored in Azure Blob Storage or Cosmos DB may be transformed into searchable JSON documents. This is accomplished by cognitive skills that add external processing during indexing.

What are the key features of Azure Cognitive Search?

The key features are: -

  • Indexing Features
  • AI enrichment and knowledge mining
  • Query and user experience
  • Security features
  • Portal features
  • Programmability

Conclusion

In this article, we have extensively discussed Azure Cognitive Search, its primary workloads, and its features. 

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