Configuring and Using Red Hat Developer Lightspeed for MTA

Procedure

1. Open the my-Java project in Visual Studio (VS) Code.
2. Download the Red Hat Developer Lightspeed for migration toolkit for applications extension from the VS Code marketplace.

3. Open Command Palette:

   1. Type Ctrl+Shift+P in Windows and Linux systems.
   2. Type Cmd+Shift+P in Mac systems.
4. Type Preferences: Open Settings (UI) in the Command Palette to open the VS Code settings and select Extensions > MTA.
5. Select Gen AI:Agent Mode.
6. In the Red Hat Developer Lightspeed for MTA extension, click Open Analysis View.
7. Type MTA: Manage Analysis Profile in the Command Palette to open the analysis profile page.

8. Configure the following fields:

   1. Profile Name: Type a profile name
   2. Target Technologies: quarkus
   3. Custom Rules: Select custom rules if you want to include them while running the analysis. By default, Red Hat Developer Lightspeed for MTA enables Use Default Rules for quarkus.
9. Close the profile manager.
10. Type MTA: Open the Gen AI model provider configuration file in the Command Palette.

11. Configure the following in the provider-settings file and close it:

    models:
      openshift-example-model: &active
        environment:
          OPENAI_API_KEY: "<Server's OPENAI_API_KEY>"
          CA_BUNDLE: "<Servers CA Bundle path>"
        provider: "ChatOpenAI"
        args:
          model: "my-model"
          configuration:
            baseURL: "https://<serving-name>-<data-science-project-name>.apps.konveyor-ai.example.com/v1"

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    Note

    You must change the provider-setting configuration if you plan to use a different LLM provider.

12. Type MTA: Open Analysis View in the Command Palette.
13. Click Start to start the MTA RPC server.
14. Select the profile you configured.

15. Click Run Analysis to scan the Java application.

    MTA identifies the issues in the code.

16. Click the solutions icon ( ) in an issue to request suggestions to resolve the issue.

    Red Hat Developer Lightspeed for MTA streams the issue description, a preview of the code changes that resolve the issue, and the file(s) in which the changes are to be made.

    You can review the code changes in the editor and accept or reject the changes. If you accept the changes, Red Hat Developer Lightspeed for MTA creates a new file with the accepted code changes.

17. Click Continue to allow Red Hat Developer Lightspeed for MTA to run a follow-up analysis.

    This round of analysis detects lint issues, compilation issues, or diagnostic issues that may have occurred when you accepted the suggested code change.

    Repeat the review and accept or reject the resolutions. Red Hat Developer Lightspeed for MTA continues to run repeated iterations of scan if you allow until all issues are resolved.

Procedure

1. Create a credentials secret named kai-api-keys in the openshift-mta project.

   1. For Amazon Bedrock as the provider, type:

      oc create secret generic aws-credentials \
       --from-literal=AWS_ACCESS_KEY_ID=<YOUR_AWS_ACCESS_KEY_ID> \
       --from-literal=AWS_SECRET_ACCESS_KEY=<YOUR_AWS_SECRET_ACCESS_KEY>

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   2. For Azure OpenAI as the provider, type:

      oc create secret generic kai-api-keys -n openshift-mta \
       --from-literal=AZURE_OPENAI_API_KEY='<YOUR_AZURE_OPENAI_API_KEY>'

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   3. For Google as the provider, type:

      oc create secret generic kai-api-keys -n openshift-mta \
       --from-literal=GEMINI_API_KEY='<YOUR_GOOGLE_API_KEY>'

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   4. For the OpenAI-compatible providers, type:

      oc create secret generic kai-api-keys -n openshift-mta \
       --from-literal=OPENAI_API_BASE='https://example.openai.com/v1' \
       --from-literal=OPENAI_API_KEY='<YOUR_OPENAI_KEY>'

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      Note

      You can also set the base URL as the kai_llm_baseurl variable in the Tackle custom resource.

2. (Optional) Force a reconcile so that the MTA operator picks up the secret immediately

   kubectl patch tackle tackle -n openshift-mta --type=merge -p \
   '{"metadata":{"annotations":{"konveyor.io/force-reconcile":"'"$(date +%s)"'"}}}'

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Prerequisites

1. You deployed an additional RWO volume for the Red Hat Developer Lightspeed for MTA-database if you want to use Red Hat Developer Lightspeed for MTA. See Persistent volume requirements for more information.
2. You installed the MTA operator v8.0.0.

Procedure

1. Log in to the Red Hat OpenShift cluster and switch to the openshift-mta project.

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

   oc edit tackle

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3. Enter applicable values for kai_llm_provider and kai_llm_model variables.

   ---
   kind: Tackle
   apiVersion: tackle.konveyor.io/v1alpha1
   metadata:
     name: mta
     namespace: openshift-mta
   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>
   ...

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   Note

   For OpenAI models and LLMs deployed in the OpenShift AI cluster, enter OpenAI as the kai_llm_provider value.

4. Apply the Tackle CR by in the openshift-mta project using the following command.

    $ oc apply -f tackle_hub.yaml

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Verification

1. Enter the following command to verify the Red Hat Developer Lightspeed for MTA resources deployed for Solution Server.

   oc get deploy,svc -n openshift-mta | grep -E 'kai-(api|db|importer)'

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   Note

   When you enable Solution Server, the Solution Server API endpoint is served through the MTA Hub. You need not complete any further task, such as creating a route for the Solution Server API.

Procedure

1. Go to the Podman AI Lab extension and click Catalog under Models.
2. Download one or more models.
3. Go to Services and click New Model Service.
4. Select a model that you downloaded in the Model drop down menu and click Create Service.
5. Click the deployed model service to open the Service Details page.
6. Note the server URL and the model name. You must configure these specifications in the Red Hat Developer Lightspeed for MTA extension.

7. Export the inference server URL as follows:

   export OPENAI_API_BASE=<server-url>

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8. In the Red Hat Developer Lightspeed for MTA extension, type Open the GenAI model provider configuration file in the Command Palette to open the provider-settings.yaml file.

9. Enter the model details from Podman Desktop. For example, use the following configuration for a Mistral model.

   podman_mistral: &active
       provider: "ChatOpenAI"
        environment:
         OPENAI_API_KEY: "unused value"
       args:
         model: "mistral-7b-instruct-v0-2"
         base_url: "http://localhost:35841/v1"

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   Note

   The Podman Desktop service endpoint does not need a password but the OpenAI library expects the OPENAI_API_KEY to be set. In this case, the value of the OPENAI_API_KEY variable does not matter.

Procedure

1. Go to the Red Hat Developer Lightspeed for MTA settings in one of the following ways:

   1. Click Extensions > MTA Extension for VSCode > Settings
   2. Type Ctrl + Shift + P or Cmd + Shift + P on the search bar to open the Command Palette and enter Preferences: Open Settings (UI). Go to Extensions > MTA to open the settings page.
2. Configure the settings described in the following table:

Procedure

1. Type Ctrl + Shift + P or Cmd + Shift + P on the search bar and enter Preferences:Open User Settings (JSON).
2. In the settings.json file, enter Ctrl + SPACE to enable the auto-complete for the Solution Server configurable fields.

3. Modify the following configuration as necessary:

   {
       "mta-vscode-extension.solutionServer": {
   
           "url": "https://mta-openshift-mta-kai.apps.konveyor-ai.example.com/hub/services/kai/api",
   
           "enabled": true,
           "auth": {
   
              "enabled": true, #you must enter the username and password
              "insecure": true,
              "realm": "mta"
           },
   
       }
   }

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   Note

   When you enable Solution Server authentication for the first time, you must enter the username and password in the VS Code search bar.

   Tip

   Enter MTA: Restart Solution Server in the Command Palette to restart the Solution Server.

Procedure

1. Open the MTA View Analysis page in either of the following ways:

   1. Click the book icon on the MTA: Issues pane of the MTA extension.
   2. Type Ctrl + Shift + P or Cmd + Shift + P on the search bar to open the Command Palette and enter MTA:Open Analysis View.

2. Click the settings button on the MTA View Analysis page to configure a profile for your project. The Get Ready to Analyze pane lists the following basic configurations required for an analysis:

   Verification

   After you complete the profile configuration, close the Get Ready to Analyze pane. You can verify that your configuration works by running an analysis.

Procedure

1. Click the Red Hat Developer Lightspeed for MTA extension and click Open MTA Analysis View.
2. Select a profile for the analysis.
3. Click Start to start the MTA RPC server.
4. Click Run Analysis on the MTA Analysis View page.

Procedure

1. Review the issues from the Analysis results space of the MTA view analysis page by the following tabs:

   1. All: lists all incidents identified in your project.
   2. Files: lists all the files in your project for which the analysis identified issues that must be resolved.
   3. Issues: lists all issues across different files in your project.
2. Use the Category drop down to filter issues based on how crucial the fix is for the target migration. You can filter mandatory, potential, and optional issues.
3. Click Has Success Rate to check how many times the same issue resolution was accepted in previous analysis.
4. Click the solution tool to trigger automated updates to your code. If you applied any category filter, code updates are made for all incidents, specific files, or specific issues based on the filter. Red Hat Developer Lightspeed for MTA generates new files with the updated code.
5. Review and (optionally) edit the code.
6. Click Apply all in the Resolutions pane to permanently apply the changes to your code.

Procedure

1. Verify that agent mode is enabled in one of the following ways:

   1. Type Ctrl + Shift + P in VS Code search (Linux/Windows system) and Cmd + Shift + P for Mac to go to the command palette.
   2. Enter Preferences: Open User Settings (JSON) to open the settings.json file.

   3. Ensure that mta-vscode-extension.genai.agentMode is set to true.

      OR

   4. Go to Extensions > Red Hat Developer Lightspeed for MTA > settings
   5. Click the Agent Mode option to enable the server.
2. Click the Red Hat Developer Lightspeed for MTA extension and click Open MTA Analysis View.
3. Select a profile for the analysis.
4. Click Start to start the MTA RPC server.
5. Click Run Analysis on the MTA Analysis View page. The Resolution Details tab opens, where you can view the automated analysis that makes changes in applicable files.
6. Click the Review Changes option to open the editor that shows the diff view of the modified file.
7. Review the changes and click Apply to update the file with all the changes or Reject to reject all changes. If you applied the changes, then Red Hat Developer Lightspeed for MTA creates the updated file with code changes.
8. Open Source Control to access the updated file.
9. In the Resolution Details view, accept the proposal from Red Hat Developer Lightspeed for MTA to make further changes. The stream of analysis repeats, after which you can review and accept change. Red Hat Developer Lightspeed for MTA creates the file with the code changes, and the stream continues until you reject the proposal for further analysis.