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Set Up Your Agent On GCP

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Before you begin, ensure you've finished installing Rasa Pro on Google Cloud Platform: Installation Guide

Now that you have successfully deployed Rasa Pro on GCP, you can proceed with getting a live Rasa agent in place that you can interact with.

Plan of Action:

  • Confirm Your Agent Runs Locally
  • Upload Trained Model to GCP
  • Run Your Deployed Rasa Agent

Confirm Your Agent Runs Locally​

If you already have a Rasa agent, make sure it trains and runs successfully on your local machine.

Don’t Have an Agent Yet?​

If you already have a Rasa agent you’ve built, you can skip this section and go directly to Upload Trained Model to GCP.

If not, feel free to use one of our pre-built Starter Packs. These agents come ready-made for specific industries, and you can run them as-is or customize them for your own use case.

Download the latest release of one of the following:

Run Your Starter Pack Locally​

Once you have downloaded your Starter Pack, follow the corresponding instructions linked below to train and run your Rasa agent locally:

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Make sure your local agent's endpoints.yaml matches the one in the deployed agent, so that the agent behaves identically in both environments. You can see the default values set by the deployment playbook here. If you want to change the configuration of models for the deployed agent, change gcp/rasa/assistant/values.yaml and rerun your helm upgrade command.

Upload Trained Model to GCP​

After you have successfully run your Rasa agent locally, you can now move it to the cloud to share with others. This consists of two steps:

  1. Upload the Rasa Model
  2. Restart Rasa Kubernetes Pod

Upload the Rasa Model​

Navigate to your Rasa project directory

cd /path/to/your/rasa/project

Authenticate with Google Cloud

gcloud auth login

Upload the model to your bucket

gsutil cp models/<your-model-file.tar.gz> gs://$MODEL_BUCKET/models.tar.gz

Verify upload

gsutil ls gs://$MODEL_BUCKET/

Alternatively, via Google Cloud Console UI:​

  1. Go to your Google Cloud Console UI
  2. Make sure you have the same project selected that you used to create your deployment
  3. Go to Cloud Storage > Buckets
  4. Select the bucket that you assigned to BUCKET_NAME_ENTROPY during the deployment process: gcp/setup/environment-variables.sh
    • Example: rasa-xbuc-ab-se-x2-rasa-model
    • Tip: Do not select the bucket that ends with -studio
  5. Click Upload to upload your local models.tar.gz into the bucket

Restart Rasa​

Restart Rasa to create a new pod which will pull your new model from your bucket.

Authenticate to your GCP cluster

gcloud container clusters get-credentials $NAME --region $REGION --project $PROJECT_ID

Confirm namespace

kubectl get ns
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If you changed the Kubernetes deployment name from the default rasa, use your custom deployment name in place of rasa in the commands below.

Restart deployments

kubectl -n $NAMESPACE rollout restart deployment rasa

To check if the Rasa pod is up and running yet, run the following command and see if the status of the new Rasa pod is "Running".

kubectl -n $NAMESPACE rollout status deployment/rasa
# or
kubectl -n $NAMESPACE get pods -w

Tip: You can view info, warning and error messages in your pod logs:

View the pod's logs

kubectl -n rasa logs <your-pod-name>

Alternatively, via Google Cloud Console UI:​

  1. Go to your Google Cloud Console UI
  2. Make sure you have the same project selected that you used to create your deployment
  3. Go to Kubernetes Engine > Workloads
  4. Click on the rasa workload
  5. In Deployment Details menu: Actions > Scale > Edit Replicas > Replicas: 0
  6. Press Scale
  7. Wait until the pod has been terminated
  8. In Deployment Details menu: Actions > Scale > Edit Replicas > Replicas: 1
  9. Press Scale

You have now successfully updated the model and restarted your Rasa Pro environment. Next, you’ll set up a simple front-end web page to try out your agent.

Interact With Your Rasa Agent​

Interact With Your Rasa Agent from Your Browser​

Up to now, you’ve been running and interacting with your Rasa agent locally from your browser, connected to your local instance of Rasa Pro.

Now, you'll update the connection so your browser interacts with your deployed (cloud) instance of Rasa Pro. This way, you’ll be testing your Rasa agent running in the cloud rather than on your local machine.

  1. In your Starter Pack project folder, go to the chatwidget directory
  2. Open up the index.html file in a text editor
  3. Replace
<rasa-chatbot-widget server-url="http://localhost:5005"

with the same domain you set in your $DOMAIN environment variable (the domain where your Rasa agent is deployed):

<rasa-chatbot-widget server-url="http://REPLACE_WITH_YOUR_DOMAIN"
  1. Save the changes to index.html
  2. Open up index.html in your browser

Congratulations! You should now be able to successfully interact with your Rasa agent.

Connect Your Agent to Other Channels​

In addition to running your agent through the local widget, you can connect it to many different messaging and voice channels - for example, your own website, Slack, Facebook Messenger, or Twilio.

For a full list of supported integrations and setup guides, see the Rasa documentation on channels.

Additional Resources​

Explore these guides and references to deepen your understanding of Rasa and related integrations:

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