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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

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Confirm Your Agent Runs Locally

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

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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:

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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:
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.

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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

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Upload the Rasa Model

Navigate to your Rasa project directory
Authenticate with Google Cloud
Upload the model to your bucket
Verify upload

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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

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Restart Rasa

Restart Rasa to create a new pod which will pull your new model from your bucket. Authenticate to your GCP cluster
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
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”.
Tip: You can view info, warning and error messages in your pod logs: View the pod’s logs

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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.

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Interact With Your Rasa Agent

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

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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.

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Additional Resources

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