> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Azure AI Search
First time creating a connector? [Read this first](/pipelines/connector-first-time-reqs).
Send processed data from Unstructured to Azure AI Search.
The following video shows how to fulfill the minimum set of Azure AI Search requirements:
Here are some more details about these requirements:
* The endpoint and API key for Azure AI Search. [Create an endpoint and API key](https://learn.microsoft.com/azure/search/search-create-service-portal).
* The name of the index in Azure AI Search. [Create an index](https://learn.microsoft.com/rest/api/searchservice/create-index).
The Azure AI Search index that you use must have an index schema that is compatible with the schema of the documents
that Unstructured produces for you. Unstructured cannot provide a schema that is guaranteed to work in all
circumstances. This is because these schemas will vary based on your source files' types; how you
want Unstructured to partition, chunk, and generate embeddings; any custom post-processing code that you run; and other factors.
You can adapt the following index schema example for your own needs. Be sure to replace ``
(in three locations in the following example) with the number of dimensions of the embedding model you are using:
```json theme={null}
{
"name": "elements-index",
"fields": [
{
"name": "id",
"type": "Edm.String",
"key": true
},
{
"name": "record_id",
"type": "Edm.String",
"filterable": true
},
{
"name": "element_id",
"type": "Edm.String"
},
{
"name": "text",
"type": "Edm.String",
"searchable": true
},
{
"name": "type",
"type": "Edm.String"
},
{
"name": "metadata",
"type": "Edm.ComplexType",
"fields": [
{
"name": "orig_elements",
"type": "Edm.String"
},
{
"name": "category_depth",
"type": "Edm.Int32"
},
{
"name": "parent_id",
"type": "Edm.String"
},
{
"name": "attached_to_filename",
"type": "Edm.String"
},
{
"name": "filetype",
"type": "Edm.String"
},
{
"name": "last_modified",
"type": "Edm.DateTimeOffset"
},
{
"name": "is_continuation",
"type": "Edm.Boolean"
},
{
"name": "file_directory",
"type": "Edm.String"
},
{
"name": "filename",
"type": "Edm.String"
},
{
"name": "data_source",
"type": "Edm.ComplexType",
"fields": [
{
"name": "url",
"type": "Edm.String"
},
{
"name": "version",
"type": "Edm.String"
},
{
"name": "date_created",
"type": "Edm.DateTimeOffset"
},
{
"name": "date_modified",
"type": "Edm.DateTimeOffset"
},
{
"name": "date_processed",
"type": "Edm.DateTimeOffset"
},
{
"name": "permissions_data",
"type": "Edm.String"
},
{
"name": "record_locator",
"type": "Edm.String"
}
]
},
{
"name": "coordinates",
"type": "Edm.ComplexType",
"fields": [
{
"name": "system",
"type": "Edm.String"
},
{
"name": "layout_width",
"type": "Edm.Double"
},
{
"name": "layout_height",
"type": "Edm.Double"
},
{
"name": "points",
"type": "Edm.String"
}
]
},
{
"name": "languages",
"type": "Collection(Edm.String)"
},
{
"name": "page_number",
"type": "Edm.String"
},
{
"name": "links",
"type": "Collection(Edm.String)"
},
{
"name": "page_name",
"type": "Edm.String"
},
{
"name": "link_urls",
"type": "Collection(Edm.String)"
},
{
"name": "link_texts",
"type": "Collection(Edm.String)"
},
{
"name": "sent_from",
"type": "Collection(Edm.String)"
},
{
"name": "sent_to",
"type": "Collection(Edm.String)"
},
{
"name": "subject",
"type": "Edm.String"
},
{
"name": "section",
"type": "Edm.String"
},
{
"name": "header_footer_type",
"type": "Edm.String"
},
{
"name": "emphasized_text_contents",
"type": "Collection(Edm.String)"
},
{
"name": "emphasized_text_tags",
"type": "Collection(Edm.String)"
},
{
"name": "text_as_html",
"type": "Edm.String"
},
{
"name": "regex_metadata",
"type": "Edm.String"
},
{
"name": "detection_class_prob",
"type": "Edm.Double"
}
]
},
{
"name": "embeddings",
"type": "Collection(Edm.Single)",
"dimensions": ,
"vectorSearchProfile": "embeddings-config-profile"
}
],
"vectorSearch": {
"algorithms": [
{
"name": "hnsw-",
"kind": "hnsw",
"hnswParameters": {
"m": 4,
"efConstruction": 400,
"efSearch": 500,
"metric": "cosine"
}
}
],
"profiles": [
{
"name": "embeddings-config-profile",
"algorithm": "hnsw-"
}
]
},
"semantic": {
"configurations": [
{
"name": "default-semantic-config",
"prioritizedFields": {
"titleField": null,
"prioritizedContentFields": [
{ "fieldName": "text" }
],
"prioritizedKeywordsFields": []
}
}
]
}
}
```
The `record_id`, `element_id`, and `id` fields are closely related, but each has a distinct purpose. For more information, see [How connectors use record IDs, element IDs, and IDs](/api-reference/record-element-id).
See also:
* [Search indexes in Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-an-index)
* [Schema of a search index](https://learn.microsoft.com/azure/search/search-what-is-an-index#schema-of-a-search-index)
* [Example index schema](https://learn.microsoft.com/rest/api/searchservice/create-index#examples)
* [Unstructured document elements and metadata](/api-reference/legacy-api/partition/document-elements)
Set up Enterprise Connect authentication
Enterprise Connect is available for [dedicated instance](/business/dedicated-instances/overview) customers only, and must be enabled on your instance before use. Contact your Unstructured account team or [Unstructured Support](https://support.unstructured.io/) to request access and have it enabled.
Enterprise Connect is an authentication method for Azure connectors. It uses a federated identity credential to authenticate Unstructured as a customer-configured App Registration. During a workflow run, Unstructured uses this credential to receive a short-lived access token. Tokens expire automatically and no secrets are stored. For an overview, see [Enterprise Connect for Azure](/business/azure/enterprise-connect).
To configure an Azure AI Search connector to use Enterprise Connect, first complete the following setup in your Azure subscription:
1. Enable role-based access on your Azure AI Search service.
Azure AI Search defaults to API key authentication only. Complete this step to configure the service to accept the Microsoft Entra ID tokens that Enterprise Connect uses.
Follow the instructions in [Enable role-based access control for Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-security-enable-roles) in the Azure AI Search documentation. In the Azure portal, navigate to your search service, select **Settings** > **Keys**, and select **Both** to allow both API key and role-based authentication. If you want to use role-based authentication exclusively, select **Role-based access control** instead.
2. Create an App Registration for Unstructured in Microsoft Entra ID.
In your Azure subscription, follow the instructions in [How to register an app in Microsoft Entra ID](https://learn.microsoft.com/en-us/entra/identity-platform/quickstart-register-app) in the Microsoft Entra documentation. Enter a meaningful name for your App Registration (for example, `unstructured-connector`). For **Supported account types**, select **Single tenant only**.
You are registering this app for a third-party service (Unstructured) accessing resources in your own tenant. This is the [single-tenant scenario](https://learn.microsoft.com/en-us/entra/identity-platform/single-and-multi-tenant-apps) as defined by Microsoft.
3. Add a federated identity credential to the App Registration.
Follow the instructions in [Configure an app to trust an external identity provider](https://learn.microsoft.com/en-us/entra/workload-id/workload-identity-federation-create-trust) in the Microsoft Entra documentation. Navigate to your App Registration, select **Certificates & secrets** in the left navigation pane, select the **Federated credentials** tab, and select **Add credential**.
For **Federated credential scenario**, select **Other issuer**.
Set the following values:
| Field | Value |
| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Name** | A unique name for this credential (for example, `unstructured-federated-credential`). This cannot be changed after creation. |
| **Issuer** | The OIDC issuer URL for your Unstructured instance. Get this value from your Unstructured account team. (Example: `https://oidc.prod-aks.example.com/...`) |
| **Subject** | The service account identity for your Unstructured instance. Get this value from your Unstructured account team. (Example: `system:serviceaccount:etl:etl-job-runner`) This value must exactly match what Unstructured provides. If it does not match, the token exchange will fail without displaying an error. |
| **Audience** | Set this to `api://AzureADTokenExchange`. |
Your Unstructured instance may require more than one federated identity credential. The platform uses separate identities for different operations, such as connection testing and running workflows. If your account team provides more than one Subject value, repeat these steps for each one.
4. Add a role assignment to grant your App Registration access to your Azure AI Search service.
See [Assign Azure roles using the Azure portal](https://learn.microsoft.com/en-us/azure/role-based-access-control/role-assignments-portal) in the Azure documentation. Use the following values:
* **Scope**: the Azure AI Search service that contains the index you want the connector to access.
* **Role**: assign both **Search Index Data Contributor** (required for document indexing) and **Search Service Contributor** (required for connection testing). Repeat the role assignment steps for each role.
* **Members**: select **User, group, or service principal**, then search for and select the App Registration you created in Step 2.
When you reach the **Review + assign** tab, click **Review + assign** to complete the assignment.
5. Note the following values from your App Registration. You will need them when configuring the connector in Unstructured. Both values are available on the **Overview** page of your App Registration in the [Microsoft Entra admin center](https://entra.microsoft.com).
* The **Tenant ID** (also called Directory ID) for your Azure subscription.
* The **Client ID** of your App Registration.
Next, create the connector by following the steps in the **Create the destination connector** section on this page.
## Create the destination connector
To create the destination connector:
1. On the sidebar, click **Connectors**.
2. Click **Destinations**.
3. Click **New** or **Create Connector**.
4. Give the connector some unique **Name**.
5. In the **Provider** area, click **Azure AI Search**.
6. Click **Continue**.
7. Follow the on-screen instructions to fill in the fields as described later on this page.
8. Click **Save and Test**.
Fill in the following fields:
* **Name** (*required*): A unique name for this connector.
* **Endpoint** (*required*): The endpoint URL for your Azure AI Search service, in the format `https://.search.windows.net`.
* **Index Name** (*required*): The name of the index in your Azure AI Search service.
* **Authentication Method** (*required*): The authentication method to use. Select **API Key** or **Enterprise Connect (Workload Identity)**.
**API Key only:**
* **API Key** (*required*): The admin API key for your Azure AI Search service.
**Enterprise Connect only:**
* **Tenant ID** (*required*): The Tenant ID (also called Directory ID) for your Azure subscription.
* **Client ID** (*required*): The Client ID of your App Registration.