Use Machine Learning to Enrich Employee Data with Swagger UI

Use the POST /data/jobs endpoint to add your own master data records to the database to enrich the information extracted from documents.

1. Expand the POST /data/jobs endpoint.

2. Click Try it out.

3. Define the data in the payload field, so that the system knows which extracted field (using, for example, the employee ID from master data) should be enriched.

   {
      "value":[
         {
            "id":"E0001",
            "email":"",
            "firstName":"Linda",
            "middleName":"",
            "lastName":"Owens"
         },
         {
            "id":"E0002",
            "email":"",
            "firstName":"Lin",
            "middleName":"",
            "lastName":"Owens"
         }
      ]
   }
   

4. Choose the enrichment data type employee.

5. Enter your clientId (created in the previous step).

6. Click Execute.

What just happened?

In this example, in the payload field, several master data records (full name and ID, for example) from 2 employees (Linda Owens and Lin Owens) are provided, so this additional information can be added to the document extracted fields prediction when the information matches.

You should receive a response like the following with status PENDING:

Copy the id from the Response body to see the result of the enrichment data status in the next step.

Set data activation to manual, instead of using the default automatic refresh of enrichment data that takes place every 4 hours.

1. Expand the POST /configuration endpoint.

2. Click Try it out.

3. Enter the following in the payload field:

   {
     "value": {
       "manualDataActivation":"true"
     }
   }  
   

4. Click Execute.

You should receive a response like the following:

Document Information Extraction uses a globally pre-trained machine learning model that currently obtains better accuracy results with invoices and payment advices in the languages listed in Supported Languages and Countries. The team is working to support additional document types and languages in the near future.

When enrichment data has been uploaded and fits to a certain prediction it is added to the results from the GET /document/jobs/{id} endpoint. To have the enrichment data in the prediction, you need to have the following part in the query of the POST /document/jobs endpoint (it is usually already there by default):

"enrichment": {
    "sender": {
      "top": 5,
      "type": "businessEntity",
      "subtype": "supplier"
    },
    "employee": {
      "type": "employee"
    }
}

Do the following:

1. Expand the POST /document/jobs endpoint.

2. Click Try it out.

3. Right click Sample Invoice 2, then click Save link as to download locally the document file for this enrich employee data example.

   You can also upload to the service and enrich any document file in PDF or single-page PNG and JPEG format that has content in headers and tables, such as an invoice. In this case, make sure the data you define in the payload field, in step 4, matches your document fields.

4. Upload the document file you want to enrich.

5. In options, enter the list of fields to be extracted from the uploaded file (documentNumber, taxId, purchaseOrderNumber, shippingAmount, netAmount, senderAddress, senderName, grossAmount, for example), the client id you created in step 3 (c_27, for example), the document type (invoice, for example), receivedDate (2020-02-17, for example) and the enrichment data type employee.

   {
      "extraction":{
         "headerFields":[
            "barcode",
            "documentNumber",
            "taxId",
            "taxName",
            "purchaseOrderNumber",
            "shippingAmount",
            "netAmount",
            "grossAmount",
            "currencyCode",
            "receiverContact",
            "documentDate",
            "taxAmount",
            "taxRate",
            "receiverName",
            "receiverAddress",
            "receiverTaxId",
            "deliveryDate",
            "paymentTerms",
            "deliveryNoteNumber",
            "senderBankAccount",
            "senderAddress",
            "senderName",
            "dueDate",
            "discount"
         ],
         "lineItemFields":[
            "description",
            "netAmount",
            "quantity",
            "unitPrice",
            "materialNumber",
            "unitOfMeasure"
         ]
      },
      "clientId":"c_27",
      "documentType":"invoice",
      "receivedDate":"2020-02-17",
      "enrichment":{
         "employee":{
            "type":"employee"
         }
      }
   }
   

6. Click Execute.

This is how the request should look like:

And that’s how the response looks like:

Copy the id from the Response body to get enrichment data prediction in the next step.

To delete enrichment data which has been uploaded before:

1. Expand the DELETE /data endpoint.

2. Click Try it out.

3. Define the data in the payload field, so that the system knows which data entry (using, for example, the data entry ID) should be deleted.

   {
      "value":[
         {
            "id":"E0001"
         },
         {
            "id":"E0002"
         }
      ]
   }
   

4. Choose the enrichment data type employee and enter your clientId.

5. Click Execute.

You should receive a response like the following:

You can repeat step 2 and delete the client you created in step 3.

Congratulations, you have completed this tutorial.