Deploy Rasa

You are viewing documentation for our open source project which is maintained by the community. If you want to get started building assistants with Rasa please check out our latest documentation here.

note

The Rasa Helm Chart is open source and available in the helm-charts repository. Please create an issue in this repository if you discover bugs or have suggestions for improvements.

Installation Requirements

  1. Check that you have installed the Kubernetes or OpenShift command line interface (CLI). You can check this using the following command:

Copy

kubectl version --short --client

The output should be similar to this

Client Version: v1.19.11

If this command resulted in an error, please install the Kubernetes CLI or the OpenShift CLI depending on the cluster you’re using.

  1. Make sure that the Kubernetes / OpenShift CLI is correctly connected to your cluster. You can do so by using the following commands:

If you get an error when executing the command, you are not connected to your cluster. To get the command to connect to the cluster please consult your cluster’s admin or the documentation of your cloud provider.

  1. Make sure you have the Helm CLI installed. To check this, run:

Copy

helm version --short

The output should be similar to this

v3.6.0+g7f2df64

If this command leads to an error, please install the Helm CLI.

In case you are using a version <3.5 of Helm, please update to Helm version >=3.5.

Installation

1. Create Namespace

We recommend installing Rasa in a separate namespace to avoid interfering with existing cluster deployments. To create a new namespace run the following command:

2. Create Values File

Prepare an empty file called rasa-values.yml which will include all your custom configuration for the installation with Helm.

All available values you can find in the Rasa Helm Chart repository.

note

The default configuration of the Rasa chart deploys a Rasa Server, downloads a model, and serves the downloaded model. Visit the Rasa Helm Chart repository to check out more examples of configuration.

3. Loading an initial model

The first time you install Rasa, you may not have a model server available yet, or you may want a lightweight model for testing the deployment. For this purpose, you can choose between training or downloading an initial model. By default, the Rasa chart downloads an example model from GitHub. To use this option, you don't have to change anything.

If you want to define an existing model to download from a URL you define instead, update your rasa-values.yaml with the URL according to the following configuration:

Copy

applicationSettings:
initialModel:"https://github.com/RasaHQ/rasa-x-demo/blob/master/models/model.tar.gz?raw=true"
note

The URL for the initial model download has to point to a tar.gz file and must not require authentication.

If you want to train an initial model you can do this by setting the applicationSettings.trainInitialModel to true. It creates a init container that trains a model based on data located in the /app directory. If the /app directory is empty it creates a new project. You can find an example that shows how to download data files from a git repository and train an initial model in the Rasa Helm Charts examples.

4. Deploy Rasa Assistant

Run the following commands:

Copy

kubectl version --short --client

The output should be similar to this

Client Version: v1.19.11

Copy

# Add the repository which contains the Rasa Helm Chart
helm repo add rasa https://helm.rasa.com

# Deploy Rasa
helm install\
 --namespace <your namespace>\
 --values rasa-values.yml \
<release name>\
rasa/rasa
note

OpenShift only: If the deployment fails and oc get events returns 1001 is not an allowed group spec.containers[0].securityContext.securityContext.runAsUser, re-run the installation command with the following values:

Copy

postgresql:
volumePermissions:
securityContext:
runAsUser:"auto"
securityContext:
enabled:false
shmVolume:
chmod:
enabled:false
nginx:
image:
name: nginxinc/nginx-unprivileged
port:8080

Then wait until the deployment is ready. If you want to check on its status, the following command will block until the Rasa deployment is ready:

5. Access Rasa Assistant

By default the Rasa deployment is exposed via the rasa (<release name>) service and accessible only within a Kubernetes cluster. To access Rasa Assistant by using kubectl port-forward, use these commands:

You can then access the deployment on http://127.0.0.1:${SERVICE_PORT}. The other option is to expose your deployment on NodePort and access it directly.

  1. Prepare configuration that switch the rasa service to NodePort.

    Copy

    # rasa-values.yaml
service:
type:"NodePort"

  2. Upgrade deployment.

    Copy

    helm upgrade --namespace <NAMESPACE> --reuse-values -f rasa-values.yaml <RELEASE NAME> rasa/rasa

  3. Get the node port and address for the rasa service.

    Copy

    export NODE_PORT=$(kubectl get --namespace <NAMESPACE> -o jsonpath="{.spec.ports[0].nodePort}" services <RELEASE NAME>)
$ curl http://127.0.0.1:${NODE_PORT}

Hello from Rasa: 2.8.7

Visit the Rasa Helm Chart README to learn other ways to expose your deployment.

Next Steps