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vertex-ai-actions

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This guide will walk you through integrating Looker with [Generative AI Studio (Vertex AI)](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models) via Cloud Functions using the Looker Action API. Users can use the Looker Explore to examine data, then send the Looker query to a Cloud Function, specifying the model prompt and parameters on the form submissions.

The cloud function included in this demo is used to communicate from Looker to Vertex AI via the [Action API](https://github.com/looker-open-source/actions/blob/master/docs/action_api.md):

## Installation:

Installation can be done either via manual steps or [Terraform](#deployment-via-terraform).

### Manual Installation

_Before following the steps below, make sure you have enabled the [Secret Manager API](https://console.cloud.google.com/flows/enableapi?apiid=secretmanager.googleapis.com), [Cloud Build API](https://console.cloud.google.com/flows/enableapi?apiid=cloudbuild.googleapis.com), [Cloud Functions API](https://console.cloud.google.com/flows/enableapi?apiid=cloudfunctions.googleapis.com), [Cloud Run Admin API](https://console.cloud.google.com/flows/enableapi?apiid=run.googleapis.com), [Artifact Registry API](https://console.cloud.google.com/flows/enableapi?apiid=artifactregistry.googleapis.com), and the [Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). It will take a few minutes after enabling this APIs for it to propagate through the systems._

_Also make sure you have a Sendgrid or Mailgun account and API key to use for sending emails. You can create a Sendgrid developer account from the [GCP marketplace](https://console.cloud.google.com/marketplace/details/sendgrid-app/sendgrid-email)._

Use [Cloud Shell](https://cloud.google.com/shell) or the [`gcloud CLI`](https://cloud.google.com/sdk/docs/install) for the following steps.

The variables to modify are:

- `PROJECT` - ID you want to deploy the Cloud Functions to
- `EMAIL_SENDER` - Email address of the sender
- `MODEL_VARIANT` - Default Vertex AI model to use if not selected in form (default: gemini-2.5-flash)
- `CALL_LIMIT` - Request quota limit (default: 50)
- `ONE_MINUTE` - Time window for quota in seconds (default: 60)
- `OUTPUT_TOKEN_LIMIT` - Max output tokens (default: 8192)

1. Set the variables below:

```
   ACTION_LABEL="Vertex AI"
   ACTION_NAME="vertex-ai"
   REGION="us-central1"
   PROJECT="my-project-id"
   EMAIL_SENDER="my-sender-email-address@foo.com"
   MODEL_VARIANT="gemini-2.5-flash"
   CALL_LIMIT="50"
   ONE_MINUTE="60"
   OUTPUT_TOKEN_LIMIT="8192"

```

2. Clone this repo

```
   git clone https://github.com/looker-open-source/vertex-ai-actions
   cd vertex-ai-actions/
   ```

3. Create a [.env.yaml](.env.yaml.example) with variables:

```
   printf "ACTION_LABEL: ${ACTION_LABEL}\nACTION_NAME: ${ACTION_NAME}\nREGION: ${REGION}\nPROJECT: ${PROJECT}\nEMAIL_SENDER: ${EMAIL_SENDER}\nGEN2_ROUTER: true\nMODEL_VARIANT: ${MODEL_VARIANT}\nCALL_LIMIT: ${CALL_LIMIT}\nONE_MINUTE: ${ONE_MINUTE}\nOUTPUT_TOKEN_LIMIT: ${OUTPUT_TOKEN_LIMIT}" > .env.yaml
   ```

4. Generate the LOOKER_AUTH_TOKEN secret. The auth token secret can be any randomly generated string. You can generate such a string with the openssl command:

```
   LOOKER_AUTH_TOKEN="`openssl rand -hex 64`"
   ```

5. Add the Auth Token and [Sendgrid API key](https://app.sendgrid.com/settings/api_keys) as Secrets, then create a Service Account to run the Cloud Functions and give it access to the Secrets:

```
   SENDGRID_API_KEY="copy your sendgrid api key here"

printf ${SENDGRID_API_KEY} | gcloud secrets create SENDGRID_API_KEY --data-file=- --replication-policy=user-managed --locations=${REGION} --project=${PROJECT}

printf ${LOOKER_AUTH_TOKEN} | gcloud secrets create LOOKER_AUTH_TOKEN --data-file=- --replication-policy=user-managed --locations=${REGION} --project=${PROJECT}

gcloud iam service-accounts create vertex-ai-actions-cloud-function --display-name="Vertex AI Actions Cloud Functions" --project=${PROJECT}

SERVICE_ACCOUNT_EMAIL=vertex-ai-actions-cloud-function@${PROJECT}.iam.gserviceaccount.com

eval gcloud projects add-iam-policy-binding ${PROJECT} --member=serviceAccount:${SERVICE_ACCOUNT_EMAIL} --role='roles/cloudfunctions.invoker'

eval gcloud projects add-iam-policy-binding ${PROJECT} --member=serviceAccount:${SERVICE_ACCOUNT_EMAIL} --role='roles/aiplatform.user'

eval gcloud projects add-iam-policy-binding ${PROJECT} --member=serviceAccount:${SERVICE_ACCOUNT_EMAIL} --role='roles/secretmanager.secretAccessor'

eval gcloud secrets add-iam-policy-binding SENDGRID_API_KEY --member=serviceAccount:${SERVICE_ACCOUNT_EMAIL} --role='roles/secretmanager.secretAccessor' --project=${PROJECT}

eval gcloud secrets add-iam-policy-binding LOOKER_AUTH_TOKEN --member=serviceAccount:${SERVICE_ACCOUNT_EMAIL} --role='roles/secretmanager.secretAccessor' --project=${PROJECT}
   ```

6. Deploy the cloud function for the action hub (this may take a few minutes):

```
   gcloud functions deploy ${ACTION_NAME} --entry-point action_handler --env-vars-file .env.yaml --trigger-http --runtime=python311 --allow-unauthenticated --gen2 --memory=8192MB --timeout=540s --region=${REGION} --project=${PROJECT} --service-account ${SERVICE_ACCOUNT_EMAIL} --set-secrets 'LOOKER_AUTH_TOKEN=LOOKER_AUTH_TOKEN:latest,SENDGRID_API_KEY=SENDGRID_API_KEY:latest'
   ```

7. Copy the Action Hub URL (`action_list` endpoint) and the `LOOKER_AUTH_TOKEN` to input into Looker:

```
   echo Action Hub URL: $(gcloud functions describe ${ACTION_NAME} --region=${REGION} --format='value(serviceConfig.uri)')
   echo LOOKER_AUTH_TOKEN: $LOOKER_AUTH_TOKEN
   ```

8. In Looker, go to the **Admin > Actions** page and click **Add Action Hub**

- Enter the Action Hub URL
   - click **Configure Authorization** and enter the `LOOKER_AUTH_TOKEN` value for the Authorization Token and click **Enable**
   - Toggle the **Enabled** button and click **Save**

## Troubleshooting:

If the action build fails, you will receive an email notification. Go to the **Admin > Scheduler History** page to view the error message returned from the Action or use `scheduled_plan` System Activity Explore:

- <details><summary> Explore query to see details on action executions: </summary>

`https://${YOUR_LOOKER_DOMAIN}/explore/system__activity/scheduled_plan?fields=scheduled_job.id,scheduled_job.created_time,scheduled_plan_destination.action_type,scheduled_plan_destination.format,scheduled_job.status,scheduled_plan.run_once,scheduled_plan_destination.parameters,scheduled_job.status_detail&f[scheduled_plan_destination.action_type]=vertex-ai&sorts=scheduled_job.created_time+desc&limit=500`

</details>

### Deployment via Terraform

Alternatively, you can manage the deployment using [Terraform](https://www.terraform.io/). A `terraform/` directory is provided with the configuration.

1.  Navigate to the `terraform` directory:
    ```bash
    cd terraform
    ```

2.  Initialize Terraform:
    ```bash
    terraform init
    ```

3.  Create a `terraform.tfvars` file with your configuration:

```hcl
    project_id        = "your-project-id"
    region            = "us-central1"
    email_sender      = "noreply@yourdomain.com"
    looker_auth_token = "your-looker-token"
    mailgun_api_token = "your-mailgun-token" # Or sendgrid_api_key
    mailgun_domain    = "your-mailgun-domain"
    ```

4.  Plan and apply:
    ```bash
    terraform plan
    terraform apply
    ```

5. See the [Manual Installation](#manual-installation) section for instructions on how to configure Looker's action hub with the output Cloud Run url.

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