REST code samples for Azure Cognitive Search
Learn about the REST API samples that demonstrate the functionality and workflow of an Azure Cognitive Search solution. These samples use the Search REST APIs.
REST is the definitive programming interface for Azure Cognitive Search, and all operations that can be invoked programmatically are available first in REST, and then in SDKs. For this reason, most examples in the documentation leverage the REST APIs to demonstrate or explain important concepts.
REST samples are usually developed and tested on Postman, but you can use any client that supports HTTP calls:
- Start with Quickstart: Create an Azure Cognitive Search index using REST APIs for help in formulating HTTP calls.
- Try Visual Studio Code extension for Azure Cognitive Search, currently in preview, if you work in Visual Studio Code.
Code samples from the Cognitive Search team demonstrate features and workflows. Many of these samples are referenced in tutorials, quickstarts, and how-to articles. You can find these samples in Azure-Samples/azure-search-postman-samples on GitHub.
|Quickstart||Source code for Quickstart: Create a search index using REST APIs. This article covers the basic workflow for creating, loading, and querying a search index using sample data.|
|Tutorial||Source code for Tutorial: Use REST and AI to generate searchable content from Azure blobs. This article shows you how to create a skillset that iterates over Azure blobs to extract information and infer structure.|
|Debug-sessions||Source code for Tutorial: Diagnose, repair, and commit changes to your skillset. This article shows you how to use a skillset debug session in the Azure portal. REST is used to create the objects used during debug.|
|custom-analyzers||Source code for Tutorial: Create a custom analyzer for phone numbers. This article explains how to use analyzers to preserve patterns and special characters in searchable content.|
|knowledge-store||Source code for Create a knowledge store using REST and Postman. This article explains the necessary steps for populating a knowledge store used for knowledge mining workflows.|
|projections||Source code for Define projections in a knowledge store. This article explains how to specify the physical data structures in a knowledge store.|
|index-encrypted-blobs||Source code for How to index encrypted blobs using blob indexers and skillsets. This article shows how to index documents in Azure Blob Storage that have been previously encrypted using Azure Key Vault.|
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The following samples are also published by the Cognitive Search team, but are not referenced in documentation. Associated readme files provide usage instructions.
|Query-examples||Postman collections demonstrating the various query techniques, including fuzzy search, RegEx and wildcard search, autocomplete, and so on.|