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Example

What you'll build​

Build an integration that connects to Azure AI Search and uploads documents to a search index using the WSO2 Integrator low-code canvas. The integration creates an automation flow that calls the Documents Index operation to batch-upload documents and logs the result.

Operations used:

  • Documents Index : Uploads or indexes a batch of documents to an Azure AI Search index using an IndexBatch payload

Architecture​

Prerequisites​

  • An Azure subscription with an Azure AI Search service provisioned
  • Your Azure AI Search service endpoint URL and API key

Setting up the Azure AI Search Index integration​

New to WSO2 Integrator? Follow the Create a New Integration guide to set up your integration first, then return here to add the connector.

Adding the Azure AI Search Index connector​

Search for and add the Azure AI Search Index connector to your integration project.

Step 1: Open the connector palette​

Select + Add Connection to open the connector palette and view all available connectors before searching.

Azure AI Search Index connector palette open with search field before any selection

Step 2: Search for and select the Index connector​

  1. Enter "Index" in the search box.
  2. Select the ballerinax/azure.ai.search.index connector card (labeled Index) to open the connection configuration form.

Configuring the Azure AI Search Index connection​

Step 3: Fill in the connection parameters​

Bind each connection field to a configurable variable so credentials aren't hardcoded.

  • serviceUrl : The Azure AI Search service endpoint URL, bound to a configurable variable
  • Config : HTTP-level transport configuration; leave as {} to accept all defaults

Note: The ConnectionConfig record for this connector is an HTTP transport-level config only and has no auth field. Pass the Azure AI Search API key as the api-key request header in individual operation calls, not in the connection config.

Azure AI Search Index connection form fully filled with all parameters before saving

Step 4: Save the connection​

Select Save Connection to persist the connection. The canvas updates and indexClient appears under Connections.

Azure AI Search Index Connections panel showing indexClient entry after saving

Step 5: Set actual values for your configurables​

  1. In the left panel, select Configurations.
  2. Set a value for each configurable listed below.
  • serviceUrl (string) : The Azure AI Search index endpoint, for example https://<your-search-service>.search.windows.net/indexes/<index-name>
  • apiKey (string) : Your Azure AI Search API key

Configuring the Azure AI Search Index Documents Index operation​

Step 6: Add an Automation entry point​

  1. Select + Add Artifact on the design canvas.
  2. Select Automation from the artifact picker.
  3. Accept the defaults in the Create New Automation form and select Create.

The automation flow canvas opens, showing Start → Error Handler → End.

Step 7: Select and configure the Documents Index operation​

  1. Select the + node between Start and Error Handler to open the node panel.
  2. Under Connections, expand indexClient to view its available operations.

Azure AI Search Index connection node expanded showing all available operations before selection

  1. Select Documents Index to open the operation configuration panel.
  2. Fill in the operation fields:
  • Payload : An index:IndexBatch record with a value array of IndexAction items; enter {value: []} in expression mode
  • Api-version : The Azure AI Search REST API version, for example 2024-07-01
  • Result : Variable name for the operation result

Azure AI Search Index Documents Index operation configuration filled with all values

Select Save to add the node to the flow.

Completed Azure AI Search Index automation flow

Try it yourself​

Try this sample in WSO2 Integration Platform.

Deploy to Devant

View source on GitHub

More code examples​

The Azure AI Search Index connector provides practical examples illustrating usage in various scenarios. Explore these examples, covering the following use cases:

  1. Document search - Search for documents in an Azure AI Search index with various query parameters and filters.