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. After you sign up, you are automatically signed in to your new Unstructured Let’s Go account, at https://platform.unstructured.io. To sign up for a Business account instead, contact Unstructured Sales, or learn more.
• 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. For other types of Business accounts, see your Unstructured account administrator for sign-in instructions, or email Unstructured Support at [e-mail].

After you sign in, the Unstructured user interface (UI) appears, which you use to create your destination connector.

After you create the destination connector, add it along with a source connector to a workflow. Then run the worklow as a job. To learn how, try out the hands-on UI quickstart or watch the 4-minute video tutorial.

You can also create destination connectors with the Unstructured API. Learn how.

If you need help, email Unstructured Support at [e-mail].

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.

• The name of the index in Azure AI Search. Create an 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 <number-of-dimensions> (in three locations in the following example) with the number of dimensions of the embedding model you are using:

  {
    "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": <number-of-dimensions>,
        "vectorSearchProfile": "embeddings-config-profile"
      }
    ],
    "vectorSearch": {
      "algorithms": [
        {
          "name": "hnsw-<number-of-dimensions>",
          "kind": "hnsw",
          "hnswParameters": {
            "m": 4,
            "efConstruction": 400,
            "efSearch": 500,
            "metric": "cosine"
          }
        }
      ],
      "profiles": [
        {
          "name": "embeddings-config-profile",
          "algorithm": "hnsw-<number-of-dimensions>"
        }
      ]
    },
    "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.

  The record_id identifies the source document.

  • For S3 and Azure Blob connectors, the record ID is a Version 5 UUID generated from the namespace and file path of the document. This ensures that the ID is deterministic and unique at the file level.
  • For all other blob connectors, the record ID is a Version 4 UUID representing a random 32-character hexadecimal string.
  • For SQL connectors, the record ID is generated from the table name and record ID of the database table.

  The element_id identifies the specific element. Source connectors generate element ID in one of the following ways, depending on the source connector:

  • A SHA-256 hash of the element’s text, its position on the page, the page number it’s on, and the name of the related file. This is to ensure that the ID is deterministic and unique at the file level.
  • A Version 4 UUID generated using random numbers.
  Older connectors generate SHA-256 hashes for element IDs, while more modern connectors generate UUIDs. Going forward, older connectors will be converted to using UUIDs as well.

  Each element from the same document contains that document’s record ID; this enables Unstructured to identify all the elements generated from a given document.

  If a source connector has been set to not reprocess all documents each time a workflow runs, Unstructured uses the record ID (along with the record version) to determine which documents are unchanged and should not be processed again.

  The id represents the actual row that Unstructured writes into the destination location. The ID is a Version 5 UUID generated from the namespace, element ID and record ID of the source document. The ID is deterministic and unique at the row level.

  Destination connectors process document updates in one of the following ways, depending on the connector:

  • Use the record ID to identify and delete all elements from a given document, prior to writing new elements from that document into the destination.
  • Use the ID to perform upsert operations without generating duplicate rows, ensuring that reprocessing documents is idempotent.

  See also:

  • Search indexes in Azure AI Search
  • Schema of a search index
  • Example index schema
  • Unstructured document elements and metadata

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.