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REST samples for Azure AI Search

Learn about REST API samples that demonstrate the functionality and workflow of an Azure AI Search solution. These samples use the Search Service REST APIs.

REST is the definitive programming interface for Azure AI Search. All operations that can be invoked programmatically are first available in REST, followed by the SDKs. For this reason, most examples in our documentation use the REST APIs to demonstrate and explain important concepts.

You can use any client that supports HTTP calls. To learn how to formulate the HTTP request using Visual Studio Code with the REST Client extension, see the REST portion of Quickstart: Full-text search.

Doc samples

Code samples from the Azure AI Search team demonstrate features and workflows. The following samples are referenced in tutorials, quickstarts, and how-to articles. You can find these samples in Azure-Samples/azure-search-rest-samples on GitHub.

Sample Article Description
quickstart Quickstart: Full-text search Create, load, and query a search index using sample data.
quickstart-agentic-retrieval Quickstart: Agentic retrieval Integrate semantic ranking with LLM-powered query planning and answer generation.
quickstart-RAG Quickstart: Classic generative search (RAG) Use grounding data from Azure AI Search with a chat completion model from Azure OpenAI.
quickstart-semantic-search Quickstart: Semantic ranking Add semantic ranking to an index schema and run semantic queries.
quickstart-vectors Quickstart: Vector search Index and query vector content.
acl Query-time ACL and RBAC enforcement Implement query-time access control using role-based access control (RBAC) and access control lists (ACLs).
custom-analyzers Tutorial: Create a custom analyzer for phone numbers Use an analyzer to preserve patterns and special characters in searchable content.
debug-sessions Tutorial: Fix a skillset using Debug Sessions Create search objects that you later debug in the Azure portal.
index-json-blobs Tutorial: Index JSON blobs from Azure Storage Create an indexer, data source, and index for nested JSON within a JSON array. Demonstrates the jsonArray parsing model and documentRoot parameters.
knowledge-store Create a knowledge store using REST Populate a knowledge store for knowledge mining workflows.
projections Define projections in a knowledge store Specify the physical data structures in a knowledge store.
skillset-tutorial Tutorial: AI-generated searchable content from Azure blobs Create a skillset that iterates over Azure blobs to extract information and infer structure.

Other samples

Currently, there are no other REST samples available.

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