> ## 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
If you're new to Unstructured, read this note first.
Before you can create a destination connector, you must first sign in to your Unstructured account:
* If you do not already have an Unstructured account, [sign up for free](https://unstructured.io/?modal=try-for-free). After you sign up, you are automatically signed in to your new Unstructured **Let's Go** account, at [https://platform.unstructured.io](https://platform.unstructured.io).
To sign up for a **Business** account instead, [contact Unstructured Sales](https://unstructured.io/?modal=contact-sales), or [learn more](/ui/overview#how-am-i-billed%3F).
* If you already have an Unstructured **Let's Go**, **Pay-As-You-Go**, or **Business SaaS** account and are not already signed in, sign in to your account at
[https://platform.unstructured.io](https://platform.unstructured.io). For other types of **Business** accounts, see your Unstructured account administrator for sign-in instructions,
or email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
After you sign in, the [Unstructured user interface](/ui/overview) (UI) appears, which you use to create your destination connector.
After you create the destination connector, add it along with a
[source connector](/ui/sources/overview) to a [workflow](/ui/workflows). Then run the worklow as a
[job](/ui/jobs). To learn how, try out the [hands-on UI quickstart](/ui/quickstart#remote-quickstart) or watch the 4-minute
[video tutorial](https://www.youtube.com/watch?v=Wn2FfHT6H-o).
You can also create destination connectors with the Unstructured API.
[Learn how](/api-reference/workflow/destinations/overview).
If you need help, email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
You are now ready to start creating a destination connector! Keep reading to learn how.
Send processed data from Unstructured to Azure AI Search.
The requirements are as follows.
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": []
}
}
]
}
}
```
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)
To create the destination connector:
1. On the sidebar, click **Connectors**.
2. Click **Destinations**.
3. Cick **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 Azure AI Search.
* **Index Name** (*required*): The name of the index for Azure AI Search.
* **API Key** (*required*): The API key for Azure AI Search.