Use an Azure Logic Apps workflow for indexer-based indexing in Azure AI Search

2025-05-19

Note

This feature is currently in public preview. This preview is provided without a service-level agreement and isn't recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Support for Azure Logic Apps integration is now in public preview, available in the Azure portal Import and vectorize data wizard only. In Azure AI Search, a logic app workflow is used for indexing and vectorization, and it's equivalent to an indexer and data source in Azure AI Search.

You can create a workflow in Azure AI Search using the Import and vectorize data wizard, and then manage the workflow in Azure Logic Apps alongside your other workflows. Behind the scenes, the wizard follows a workflow template that pulls in (ingests) content from a source for indexing in AI Search. The connectors used in this scenario are prebuilt and already exist in Azure Logic Apps, so the workflow template just provides details for those connectors to create connections to the data source, AI Search, and other items to complete the ingestion workflow.

Note

A logic app workflow is a billable resource. For more information, see Azure Logic Apps pricing.

Key features

Azure Logic Apps integration in Azure AI Search adds support for:

More data sources from Microsoft and other providers
Integrated vectorization
Scheduled or on-demand indexing
Change detection of new and existing documents

Import and vectorize data wizard inputs include:

A supported data source
A supported text embedding model

After the wizard completes, you have the following components:

Component	Location	Description
Search index	Azure AI Search	Contains indexed content from a supported Logic Apps connector. The index schema is a default index created by the wizard. You can add extra elements, such as scoring profile or semantic configuration, but you can't change existing fields. You view, manage, and access the search index on Azure AI Search.
Logic app resource and workflow	Azure Logic Apps	You can view the running workflow, or you can open the designer in Azure Logic Apps to edit the workflow, as you regularly do if you'd started from Azure Logic Apps instead. You can edit and extend the workflow, but exercise caution so as to not break the indexing pipeline. The workflow created by the wizard uses the Consumption hosting option.
Logic app templates	Azure Logic Apps	Up to two templates created per workflow: one for on-demand indexing, and a second template for scheduled indexing. You can modify the indexing schedule in the Index multiple documents step of the workflow.

Prerequisites

Review the following requirements before you start:

You must be an Owner or Contributor in your Azure subscription, with permissions to create resources.
Azure AI Search, Basic pricing tier or higher if you want to use a search service identity for connections to an Azure data source, otherwise you can use any tier, subject to tier limits.
Azure OpenAI, with a supported embedding model deployment. Vectorization is integrated into the workflow. If you don't need vectors, you can ignore the fields or try another indexing strategy.
Azure Logic Apps is a supported region. It should have a system-assigned managed identity if you want to use Microsoft Entra ID authentication on connections rather than API keys.

Supported regions

End-to-end functionality is available in the following regions, which provide the data source connection, document cracking, document chunks, support for Azure OpenAI embedding models, and the Azure AI indexer support for pulling the data. The following regions for Azure Logic Apps provide the ParseDocument action upon which Azure AI Search integration is based.

East US
East US 2
South Central US
West US 2
West US 3
Brazil South
Australia East
East Asia
Southeast Asia
North Europe
Sweden Central
UK South

Supported models

The logic app path through the Import and vectorize data wizard supports a selection of embedding models. Deploy one of the following embedding models on Azure OpenAI for your end-to-end workflow.

text-embedding-3-small
text-embedding-3-large
text-embedding-ada-002

Supported connectors

The following connectors are useful for indexing unstructured data, as a complement to classic indexers that primarily target structured data.

Limitations

Currently, the public preview has these limitations:

The search index is generated using a fixed schema (document ID, content, and vectorized content), with text extraction only. You can modify the index as long as the update doesn't affect existing fields.
Vectorization supports text embedding only.
Deletion detection isn't supported. You must manually delete orphaned documents from the index.
Duplicate documents in the search index are a known issue in this preview. Consider deleting objects and starting over if this becomes an issue.
No support for private endpoints in the logic app workflow created by the portal wizard. The workflow is hosted using the Consumption hosting option and is subject to its constraints. To use the Standard hosting option, use a programmatic approach to creating the workflow. Use the 2025-05-01-preview REST API or a prerelease Azure SDK package that provides the feature.

Create a logic app workflow

Follow these steps to create a logic app workflow for indexing content in Azure AI Search.

Start the Import and vectorize data wizard in the Azure portal.
Choose a supported Azure Logic Apps indexer.
In Connect to your data, provide a name prefix used for the search index and workflow. Having a common name helps you manage them together.
Specify the indexing frequency. If you choose on a schedule, a template that includes a scheduling option is used to create the workflow. You can modify the indexing schedule in the Index multiple documents step of the workflow after it's created.
Select an authentication type where the logic app workflow connects to the search engine and starts the indexing process. The workflow can connect using Azure AI Search API keys or the wizard can create a role assignment that grants permissions to the Logic Apps system-assigned managed identity, assuming one exists.
Select Next to continue to the next page.
In Vectorize your text, provide the model deployment and Azure OpenAI connection information. Choose the subscription and service, a supported text embedding model, and the authentication type that the workflow uses to connect to Azure OpenAI.
Select Next to continue to the next page. Review the configuration.
Select Create to begin processing.

The workflow runs as a serverless workflow in Logic Apps (Consumption), separate from the AI Search service. 
Confirm index creation in the Azure portal, in the Indexes page in Azure AI Search. Search Explorer is the first tab. Select Search to return some content.

Modify existing objects

You can make the following modifications to a search index without breaking indexing:

You can make the following updates to a workflow without breaking indexing:

Modify List files in folder to change the number of documents sent to indexing.
Modify Chunk Text to vary token inputs. The recommended token size is 512 tokens for most scenarios.
Modify Chunk Text to add a page overlap length.
Modify Index multiple documents step to control indexing frequency if you chose scheduled indexing in the wizard.

In logic apps designer, review the workflow and each step in the indexing pipeline. The workflow specifies document extraction, default document chunking (Text Split skill), embedding (Azure OpenAI embedding skill), output field mappings, and finally indexing.

Template and workflow management

The wizard creates templates and workflows when you specify a Logic Apps indexer. To create and manage them, including template deletion, use the logic app designer. The Azure portal search service dashboard doesn't provide template or workflow management, and currently there's no programmatic support in Azure AI Search APIs.