Enabling konveyor AI in Tackle custom resource

Solution Server integrates with the Konveyor Hub backend component to use the database and volumes necessary to store and retrieve the solved examples.

To enable Solution Server and other AI configurations in the Konveyor AI VS Code extension, you must modify the Tackle custom resource (CR) with additional parameters.

Prerequisites

  • You deployed an additional RWO volume for the kai-database if you want to use Konveyor AI.

  • You installed the Konveyor operator v8.0.0.

Procedure

  1. Log in to your Kubernetes cluster and switch to the konveyor-tackle namespace.

  2. Edit the Tackle CR settings in the tackle_hub.yml file with the following command:

    kubectl edit tackle

  3. Enter applicable values for kai_llm_provider and kai_llm_model variables.

      kind: Tackle
      apiVersion: tackle.konveyor.io/v1alpha1
      metadata:
        name: konveyor ai
        namespace: konveyor-tackle
      spec:
        kai_solution_server_enabled: true
        kai_llm_provider: <provider-name> #For example, OpenAI.
        # optional, pick a suitable model for your provider
        kai_llm_model: <model-name>

For OpenAI models and LLMs deployed in the Red Hat OpenShift AI cluster, enter `OpenAI` as the `kai_llm_provider` value.
  1. Apply the Tackle CR for the konveyor-ai project using the following command.
    kubectl apply -f tackle_hub.yaml

Verification

  1. Enter the following command to verify the Konveyor AI resources deployed for Solution Server.
    kubectl get deploy,svc -n konveyor-ai | grep -E 'kai-(api|db|importer)'

**NOTE:** When you enable Solution Server, the Solution Server API endpoint is served through the Konveyor Hub.

You need not complete any further task, such as creating a route for the Solution Server API.