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
REST
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
REST
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.
