GSP367

Overview

In a challenge lab you’re given a scenario and a set of tasks. Instead of following step-by-step instructions, you will use the skills learned from the labs in the course to figure out how to complete the tasks on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.

When you take a challenge lab, you will not be taught new Google Cloud concepts. You are expected to extend your learned skills, like changing default values and reading and researching error messages to fix your own mistakes.

To score 100% you must successfully complete all tasks within the time period!

This lab is recommended for students enrolled in the Automate Data Capture at Scale with Document AI skill badge course. Are you ready for the challenge?

Challenge scenario

You are a data engineer at large infrastructure management company and have been assigned to work on a internal project with the financial division of the company. The company has to process an ever increasing mountain of documents that all require individual manual processing for validation and authorization, which is an expensive task that requires a lot of staff. The company plans to leverage Google Cloud tools to automate the process of collecting, categorizing, and verifying documents in an efficient and less labor intensive manner.

Your challenge

You must create a document processing pipeline that will automatically process documents that are uploaded to Cloud Storage. The pipeline consists of a primary Cloud Run functions that processes new files using a Document AI form processor to extract the data from the document. The function then saves the form data detected in those files to BigQuery.

You are provided with the source code for a Cloud Run functions that will perform the processing, and you are expected to deploy the document processing pipeline as shown in the architecture below, making sure to correctly configure the components for your specific pipeline.

Task 1. Enable the Cloud Document AI API and copy lab source files.

In this task, you enable the Cloud Document AI API and copy your starter files into Cloud Shell.

Enable the Cloud Document AI API

• Enable the Cloud Document AI API.

Enable Cloud Document AI API

Copy the lab source files into your Cloud Shell

The Cloud Run functions with predefined code is hosted on a remote Cloud Storage bucket. Copy these source files into your Cloud Shell. These files include the source code for the Cloud Run functions and the schema for the BigQuery table that you will create in the lab.

• In Cloud Shell, enter the following command to clone the source repository for the lab:

mkdir ./document-ai-challenge gsutil -m cp -r gs://spls/gsp367/* \ ~/document-ai-challenge/

Task 2. Create a form processor

Create an instance of the general form processor using the Document AI Form Parser processor in the General (non-specialized) section. The general form processor will process any type of document and extract all the text content it can identify in the document as well as form information that it infers from the layout.

• Create the processor using the following configuration details:

Property	Value
Processor Type	Form Parser
Processor Name
Region	US

Note: You will configure a Cloud Run functions later in this lab with the PROCESSOR ID and PARSER LOCATION of this processor so that the Cloud Run functions will use this specific processor to process invoices. Click on the created processor and note the PROCESSOR ID. However, the processor region is the PARSER LOCATION. Create a form processor

Task 3. Create Google Cloud resources

Prepare your environment by creating the Google Cloud Storage and BigQuery resources that are required for your document processing pipeline.

Create input, output, and archive Cloud Storage buckets

• In this step, you must create the three Cloud Storage buckets listed below with uniform bucket level access enabled.

Bucket Name	Purpose	Storage class	Location
	For input invoices	Standard
	For storing processed data	Standard
	For archiving invoices	Standard

Note: A bucket can be created using the gsutil tool with the -mb parameter, along with -c parameter to set the storage class, -l to set the (regional) location and the -b flag with the value of on or off to set uniform bucket level access. Read the mb - Make buckets reference for more about creating buckets using gsutil.

Create a BigQuery dataset and tables

• In this step, you must create a BigQuery dataset and the output table required for your data processing pipeline.

Dataset

Dataset Name	Location
invoice_parser_results	US

Note: Use bq mk to create BigQuery resources. The command line switch parameter -d is used to create a dataset and --location is used to set the location of the resource. Read the Create datasets guide for more information about creating datasets using the bq command-line tool.

Table

The table schema for the extracted information has been provided for you in the JSON file document-ai-challenge/scripts/table-schema/doc_ai_extracted_entities.json. Use this schema to create a table named doc_ai_extracted_entities in the invoice_parser_results dataset.

Note: Use bq mk to create BigQuery resources. The command line switch --table is used to create a table. For more information about creating tables with a schema definition using the bq command-line tool, read the Create and use tables guide.

You can navigate to BigQuery in the Cloud Console and inspect the schema of tables in the invoice_parser_results dataset using BigQuery SQL workspace.

Create Google Cloud resources

Task 4. Deploy the document processing Cloud Run functions

To complete this task, you must deploy the Cloud Run functions that your data processing pipeline uses to process invoices uploaded to Cloud Storage. This function will use a Document AI API Generic Form processor to extract form data from the raw documents.

You can examine the source code of the Cloud Run functions using the Code Editor or any other editor of your choice. The Cloud Run functions is stored in the following folders in Cloud Shell:

• Process Invoices - scripts/cloud-functions/process-invoices

The Cloud Run functions, process-invoices, must be triggered when files are uploaded to the input files storage bucket you created earlier.

Deploy the Cloud Run functions to process documents uploaded to Cloud Storage

Deploy a Cloud Run functions that uses a Document AI form processor to parse form documents that have been uploaded to a Cloud Storage bucket.

1. Navigate to scripts directory:

cd ~/document-ai-challenge/scripts

2. Assign the Artifact Registry Reader role to the Compute Engine service account:

PROJECT_ID=$(gcloud config get-value project) PROJECT_NUMBER=$(gcloud projects list --filter="project_id:$PROJECT_ID" --format='value(project_number)') SERVICE_ACCOUNT=$(gcloud storage service-agent --project=$PROJECT_ID) gcloud projects add-iam-policy-binding $PROJECT_ID \ --member serviceAccount:$SERVICE_ACCOUNT \ --role roles/pubsub.publisher

3. Deploy the Cloud Run functions:

export CLOUD_FUNCTION_LOCATION={{{ project_0.default_region | "REGION" }}} gcloud functions deploy process-invoices \ --gen2 \ --region=${CLOUD_FUNCTION_LOCATION} \ --entry-point=process_invoice \ --runtime=python39 \ --service-account=${PROJECT_ID}@appspot.gserviceaccount.com \ --source=cloud-functions/process-invoices \ --timeout=400 \ --env-vars-file=cloud-functions/process-invoices/.env.yaml \ --trigger-resource=gs://${PROJECT_ID}-input-invoices \ --trigger-event=google.storage.object.finalize\ --service-account $PROJECT_NUMBER-compute@developer.gserviceaccount.com \ --allow-unauthenticated Note: If you get an permission error while deploying function wait for 2-3 minutes and re-run the commands.

If you inspect the Cloud Run Functions source code you will see that the function gets the Document AI processor details via two runtime environment variables.

• You will have to reconfigure the Cloud Run functions deployment so that the environment variablesPROCESSOR_ID and PARSER_LOCATION contain the correct values for the Form Parser processor you deployed in a previous step.
• Make sure the PARSER_LOCATION value must be in lower case.
• Make sure to also update the PROJECT_ID environment variable with your project ID.

Wait for the function to be fully redeployed.

Deploy Document Processing Cloud Run functions

Task 5. Test and validate the end-to-end solution

For your final task you must successfully process the set of invoices that are available in the ~/document-ai-challenge/invoices folder using your pipeline.

1. Upload these invoices to the input Cloud Storage bucket and monitor the progress of the pipeline.

2. Watch the events until you see a final event indicating that the function execution finished with a status of OK.

Once the pipeline has fully processed the documents, you will see that the form information that is extracted from the invoices by the Document AI processor has been written out into the BigQuery table.

Note: To monitor progress, click Logs in the Management section of the Cloud Run functions to view logs. Note : You may see some errors that do not affect document processing significantly, especially timeouts, in this lab. If you not see data being reported as being written to BigQuery within 5 minutes in the logs then double check that the parameters set in the .env.yaml file in the previous section are correct and try again.

In particular make sure the Processor ID and location variables that you set are valid and note that the location parameter must be in lower case. Also note that the event list does not automatically refresh. Validate data processed by the pipeline

Congratulations!

Congratulations! In this lab, you have successfully created a document processing pipeline that automatically processes documents uploaded to Cloud Storage using the Document AI API. You have created a form processor, deployed a Cloud Run functions to process documents, and validated the end-to-end solution by processing a set of invoices.

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Manual Last Updated November 4, 2024

Lab Last Tested November 4, 2024

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