> ## Documentation Index
> Fetch the complete documentation index at: https://unstructured-53-doc-4-secrets-api.mintlify.site/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 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": []
}
}
]
}
}
```
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)
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.