Process documents

On-premises or any cloud edition v1.12 keyboard_arrow_down

• SAP BTP edition v1.1
• On-premises or any cloud edition v1.12

This quickstart shows you how to process documents (invoices) from a source bucket and store the processed document (JSON file) in a target bucket by using the batch processing capability of Document AI API .

Before you begin

Before you run this quickstart, make sure that you or your administrators have completed the following prerequisites:

• You have a Google Cloud account and project.

• Billing is enabled for your project. See how to confirm that billing is enabled for your project .

• The on-premises or any cloud edition of ABAP SDK for Google Cloud is installed and configured. See how to install and configure the on-premises or any cloud edition of ABAP SDK for Google Cloud .

• Authentication to access Google Cloud APIs is set up. See how to set up authentication .

• Make sure the Document AI API is enabled in your Google Cloud project.

  Go to API library

• In the Document AI Workbench, create a processor with type INVOICE_PROCESSOR . For more information, see Creating and managing processors .

• In Cloud Storage, create a source bucket to store the invoices for processing and place the invoices in this bucket. For more information, see Create buckets .

• In Cloud Storage, create a target bucket to store the processed files.

Create a program to process documents

1. In the SAP system, create an executable program in your custom namespace (for example, Z or Y) by using transaction SE38 .

   1. In the SAP GUI, enter transaction code SE38 .

   2. In the Programfield, enter a name of your program, for example, ZDEMO_DOCUMENT_AI .

   3. Click Create.

   4. Specify the program attributes:

      1. In the Titlefield, enter a title of your program, for example, Process invoices .

      2. In the Typefield, choose Executable Program .

      3. Click Save.

   5. Save the program as a Local Object.

   6. In the ABAP Editor, add the following code:

       **********************************************************************
      *  Copyright 2023 Google LLC                                         *
      *                                                                    *
      *  Licensed under the Apache License, Version 2.0 (the "License");   *
      *  you may not use this file except in compliance with the License.  *
      *  You may obtain a copy of the License at                           *
      *      https://www.apache.org/licenses/LICENSE-2.0                   *
      *  Unless required by applicable law or agreed to in writing,        *
      *  software distributed under the License is distributed on an       *
      *  "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,      *
      *  either express or implied.                                        *
      *  See the License for the specific language governing permissions   *
      *  and limitations under the License.                                *
      **********************************************************************
      
      REPORT zr_qs_process_documents.
      
      * data declarations
      DATA:
        lv_p_projects_id   TYPE string,
        lv_p_locations_id  TYPE string,
        lv_p_processors_id TYPE string,
        ls_input           TYPE /goog/cl_documentai_v1=>ty_017.
      
      TRY.
      
      * open http connection
          DATA(lo_client) = NEW /goog/cl_documentai_v1( iv_key_name = ' DEMO_DOC_PROCESSING 
      ' ).
      
      * populate relevant parameters
          lv_p_projects_id  = ' PROJECT_ID 
      '.
          lv_p_locations_id = ' LOCATION_ID 
      '.
          lv_p_processors_id = ' PROCESSOR_ID 
      '.
          ls_input-input_documents-gcs_prefix-gcs_uri_prefix = ' SOURCE_BUCKET_URI 
      '.
          ls_input-document_output_config-gcs_output_config-gcs_uri = ' TARGET_BUCKET_URI 
      '.
      
      * call api method
          CALL METHOD lo_client->batch_process_processors
            EXPORTING
              iv_p_projects_id   = lv_p_projects_id
              iv_p_locations_id  = lv_p_locations_id
              iv_p_processors_id = lv_p_processors_id
              is_input           = ls_input
            IMPORTING
              es_output          = DATA(ls_output)
              ev_ret_code        = DATA(lv_ret_code)
              ev_err_text        = DATA(lv_err_text)
              es_err_resp        = DATA(ls_err_resp).
      
          IF lo_client->is_success( lv_ret_code ).
            MESSAGE 'Success' TYPE 'S'.
          ELSE.
            MESSAGE lv_err_text TYPE 'E'.
          ENDIF.
      
      * close http connection
          lo_client->close( ).
      
        CATCH /goog/cx_sdk INTO DATA(lo_exception).
          MESSAGE lo_exception->get_text( ) TYPE 'E'.
      ENDTRY. 
      

      Replace the following:

      • DEMO_DOC_PROCESSING : the client key name.
      • PROJECT_ID : the ID of the Google Cloud project.
      • LOCATION_ID : the processor's location.
      • PROCESSOR_ID : the ID of the processor.
      • SOURCE_BUCKET_URI : the URI of the Cloud Storage bucket folder where source documents are kept for processing.
      • TARGET_BUCKET_URI : the URI of the Cloud Storage bucket where the processed document (JSON file) would be stored.

2. Run your application in SE38 .

3. To validate the results, follow these steps:

   1. In the Google Cloud console, go to Cloud Storage Bucketspage.

   2. Open the target bucket. The processed document is stored in the form of a JSON file.

What's next

• Explore other quickstarts available on the GitHub repository, GoogleCloudPlatform/google-cloud-abap/abap-sdk /ZGOOG_SDK_QUICKSTART/ .
• Read the guide Application development with the on-premises or any cloud edition of ABAP SDK for Google Cloud .
• View the code samples .
• Ask your questions and discuss ABAP SDK for Google Cloud with the community on Cloud Forums .