Export-to-Workbench pipeline

You can transfer documents from the Document AI Warehouse to the Document AI Workbench using the export-to-Workbench pipeline. The pipeline exports the documents to a Cloud Storage folder, then imports them to a Document AI dataset. You provide the Cloud Storage folder and the Document AI dataset.

Prerequisites

Before you begin, you need the following:

• Under the same Google Cloud project, follow the steps to create a processor .

• Dedicate an empty Cloud Storage folder for storing exported documents.

• On the custom processor page, click Configure Your Datasetand then Continueto initialize the dataset.

Run the pipeline

 curl  
--location  
--request  
POST  
 'https://contentwarehouse.googleapis.com/v1/projects/ PROJECT_NUMBER 
/locations/ LOCATION 
:runPipeline' 
  
 \ 
--header  
 'Content-Type: application/json' 
  
 \ 
--header  
 "Authorization: Bearer 
 ${ 
 AUTH_TOKEN 
 } 
 " 
  
 \ 
--data  
 '{ 
 "name": "projects/ PROJECT_NUMBER 
/locations/ LOCATION 
", 
 "export_cdw_pipeline": { 
 "documents": [ 
 "projects/ PROJECT_NUMBER 
/locations/ LOCATION 
/documents/ DOCUMENT 
", 
 ], 
 "export_folder_path": "gs:// CLOUD STORAGE FOLDER 
", 
 "doc_ai_dataset": "projects/ PROJECT_NUMBER 
/locations/ LOCATION 
/processors/ PROCESSOR 
/dataset", 
 "training_split_ratio": RATIO 
, 
 }, 
 "request_metadata": { 
 "user_info": { 
 "id": "user: USER EMAIL ADDRESS 
", 
 } 
 } 
 
  }' 
 

The training and test split ratio can be specified in the training_split_ratio field as a floating-point number. For example, for a set of 10 documents, if the ratio is specified as 0.8 , 8 documents will be added to the training set and the remaining 2 documents to the test set.

This command returns a resource name for a long-running operation. Use it to track the progress of the pipeline in the next step.

Get long-running operation result

 curl  
--location  
--request  
GET  
 'https://contentwarehouse.googleapis.com/v1/projects/ PROJECT_NUMBER 
/locations/ LOCATION 
/operations/ OPERATION 
' 
  
 \ 
--header  
 "Authorization: Bearer 
 ${ 
 AUTH_TOKEN 
 } 
 " 
 

Next step

• Go to your Document AI to check exported documents.