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Cloud Function that demonstrates how to process an uploaded file in Google Cloud Storage and perform summarization using PaLM Vertex AI API on the contents
Author(s) Romin Irani

This application demonstrates a Cloud Function written in Python that gets triggered when a file is uploaded to a specific Google Cloud Storage bucket that is configured with. It does the following:

  • Reads the content of the file.
  • Invokes the PaLM Text Bison model with a Prompt to summarize the contents.
  • Writes the summarized data into another Google Cloud Storage (GCS) bucket.

NOTE: Before you move forward, ensure that you have followed the instructions in SETUP.md. Additionally, ensure that you have cloned this repository and are currently in the summarization-gcs-cloudfunction folder. This should be your active working directory for the rest of the commands.

Environment variables required

Your Cloud Function requires access to two environment variables:

  • GCP_PROJECT : This the Google Cloud Project Id.
  • GCP_REGION : This is the region in which you are deploying your Cloud Function. For e.g. us-central1.

These variables are needed since the Vertex AI initialization needs the Google Cloud Project Id and the region. The specific code line from the main.py function is shown here: vertexai.init(project=PROJECT_ID, location=LOCATION)

In Cloud Shell, execute the following commands:

export GCP_PROJECT='<Your GCP Project Id>'  # Change this
export GCP_REGION='us-central1'             # If you change this, make sure region is supported by Model Garden. When in doubt, keep this.

These variables can be set via the following instructions via any of the following ways:

  1. At the time of deploying the Google Cloud Function. We will be using this method in the next section when we deploy the Cloud Function.
  2. Updating the environment variables after deploying the Google Cloud Function.

Deploying the Cloud Function and associated Cloud resources

Create the GCS Buckets

We will need to create 2 GCS buckets:

  • The first bucket will be used to upload the files to summarize. Let us call the bucket $BUCKETNAME. Create the environment variable to store your Bucket name as shown below:
export BUCKET_NAME='Your GCS Bucket Name'
  • The second bucket will having a prefix -summaries.

You can create a bucket either from Google Cloud Console or from the command line via the gsutil command. Execute the commands below in Cloud Shell.

gsutil mb -l $GCP_REGION gs://"$BUCKET_NAME"
gsutil mb -l $GCP_REGION gs://"$BUCKET_NAME"-summaries

Deploy the function

Assuming that you have a copy of this project on your local machine with gcloud SDK setup on the machine, follow these steps:

  1. Go to the root folder of this project.

  2. You should have both the main.py and requirements.txt file present in this folder.

  3. Provide the following command:

    gcloud functions deploy summarizeArticles \
--gen2 \
--runtime=python311 \
--source=. \
--region=$GCP_REGION \
--project=$GCP_PROJECT \
--entry-point=summarize_gcs_object \
--trigger-bucket=$BUCKET_NAME \
--set-env-vars=GCP_PROJECT=$GCP_PROJECT,GCP_REGION=$GCP_REGION \
--max-instances=1 \
--quiet

Invoking the Cloud Function

Since this Cloud Function is deployed with a GCS trigger, you will need to do the following to see the entire flow in action:

  1. Ensure that you have the following GCS buckets created $BUCKET_NAME and $BUCKET_NAME-summaries.
  2. Upload a file (a sample file story.md has been provided) with some text in the $BUCKET_NAME bucket.
  3. This should trigger the summarizeArticles function and within a few seconds, you should see a story.md (summarized form) file created in the $BUCKET-summaries bucket.