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Install Rasa

warning

Before you begin, ensure you've completed all the previous sections to deploy required infrastructure and to set environment variables that this section requires.

We have now deployed all the required infrastructure and configured everything required on our cluster. We will now proceed to actually install Rasa Pro using the Rasa Helm Chart. In this section, we will:

  • Install Kafka, which is used by components of Rasa and Studio to communicate.
  • Install Rasa itself using its Helm chart.
  • Deploy a TLS certificate and Kubernetes Ingress to make your assistant available outside the cluster via HTTPS.

Kafka Installation​

We will begin by setting up Kafka, which is used by Rasa Pro and Rasa Studio to communicate with each other, as well as for internal purposes.

You can run the following script to deploy Kafka with an autogenerated random password for authentication. When we setup the assistant in the next step, it will automatically fetch this password and save it in a Kubernetes secret so that other Rasa components can connect to Kafka. You will see some warnings about authentication failures until everything is up and running and it should eventually succeed.

./aws/rasa/kafka/install-kafka.sh

At the end, you should see here that topics called rasa and rasa-events-dlq have been created. This means that you have now successfully deployed Kafka into the cluster.

Assistant Setup​

Now that we've set up Kafka, we can set up the Rasa assistant using the Rasa Helm Chart.

You can run our script to perform the initial setup for the Assistant, and then we'll walk you through the setup that it's most important to understand.

./aws/rasa/assistant/setup-assistant.sh

Values File​

The values.yaml file will contain all the configuration that gets applied to your assistant by Helm. You can read about all the available configuration options here, and you can fully customise your setup to meet your organisation's needs and security policies. For simplicity, we've provided a file in the playbook repo aws/rasa/assistant/values.template.yaml that sets all the key values we need to integrate Rasa with the AWS infrastructure we've already deployed, leaving placeholder values for things we want to inject as environment variables. You can set these values manually and add any others you require, but we recommend you begin by automatically creating a version with all the environment variables set from the previous sections:

envsubst "$(printf '$%s ' $(env | cut -d= -f1))" < aws/rasa/assistant/values.template.yaml > aws/rasa/assistant/values.yaml

If you open this newly created file, you should see that all of your values have been automatically populated from the environment variables we've been setting as we go along.

Pin the Rasa Pro Image to Match Your Local CLI​

Rasa Pro models are version-locked to the CLI that produced them — running a model trained with one Rasa Pro version against an image with a different version may cause the assistant to fail to load.

Check your local CLI version:

rasa --version

Then in aws/rasa/assistant/values.yaml, set rasa.image.tag to match. For example, if rasa --version prints Rasa Pro Version : 3.16.5:

rasa:
  image:
    repository: "rasa/rasa-pro"
    tag: "3.16.5"

Align modelGroups with Your Trained Bot​

The endpoints.modelGroups block in values.template.yaml declares which LLM model groups the assistant can reference. The bot you trained must reference only model groups that are declared here, otherwise it will fail to load with an error like:

Failed to load agent: Your <component>'s 'llm' configuration of the trained model
references the model group '<your-id>', but this model group DOES NOT EXIST in
the endpoints.yml file.

Open your bot's endpoints.yml and copy the model_groups block into aws/rasa/assistant/values.yaml under endpoints.modelGroups (note the camelCase). For example, if your bot's endpoints.yml contains:

model_groups:
  - id: openai-gpt-5-1
    models:
      - provider: openai
        model: gpt-5.1-2025-11-13
        reasoning_effort: "none"
        timeout: 15

Then values.yaml should include the same group under endpoints.modelGroups:

rasa:
  settings:
    endpoints:
      modelGroups:
      - id: openai-gpt-5-1
        models:
        - provider: openai
          model: gpt-5.1-2025-11-13
          reasoning_effort: "none"
          timeout: 15

Upload Your Trained Model​

The Rasa Helm chart is configured to load your trained model from the S3 model bucket on startup. Before deploying, upload your model to that bucket.

The chart uses --remote-storage aws with the default --model models, which means it downloads the model from s3://${MODEL_BUCKET}/models.tar.gz — at the bucket root, with the file named models.tar.gz:

aws s3 cp <your-bot>/models/<your-trained-model>.tar.gz s3://${MODEL_BUCKET}/models.tar.gz
aws s3 ls s3://${MODEL_BUCKET}/   # confirm models.tar.gz is at the root
warning

Do not upload to s3://${MODEL_BUCKET}/models/model.tar.gz (with a folder prefix). The Rasa pod looks for models.tar.gz at the bucket root and will fail to start with a ModelNotFound error if the path is wrong.

Deploy Rasa​

Now that all the configuration is done, you can deploy Rasa using all the configuration that you've set:

helm upgrade --install -n $NAMESPACE rasa aws/rasa/assistant/repos/rasa-helm/rasa -f aws/rasa/assistant/values.yaml

You can ignore the instructions that will be printed by the above command to use port forwarding to access the deployment.

Check yourself if the Rasa pod has a status of RUNNING by running:

kubectl get pods --namespace $NAMESPACE -l "app.kubernetes.io/name=rasa,app.kubernetes.io/instance=rasa"

Configure Ingress & TLS Certificate​

Finally, we can deploy a Kubernetes Ingress and a TLS certificate for the Rasa Pro deployment. This will allow us to interact with the bot from outside the cluster.

We'll create a certificate and deploy an ingress with another script:

./aws/rasa/ingress/setup-ingress.sh

You will need to wait a few minutes while your certificate is issued and the DNS records propagate. Finally, you will be able to visit your assistant's URL: https://assistant.$DOMAIN. For example, if the value you used for $DOMAIN was example.com, you could visit your assistant on https://assistant.example.com If you receive "Hello from Rasa" response, you have successfully deployed Rasa Pro. If you receive an error, there may just be a delay in issuing the certificate or having the DNS records propagate - wait a few minutes and try again!

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