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Kai Demo

Konveyor AI (kai) is Konveyor's approach to easing modernization of application source code to a new target by leveraging LLMs with guidance from static code analysis augmented with data in Konveyor that helps to learn how an Organization solved a similar problem in the past (Source: https://github.com/konveyor-ecosystem/kai/blob/main/README.md)

In this demo, we will showcase the capabilities of Konveyor AI (Kai) in facilitating the modernization of application source code to a new target. We will illustrate how Kai can handle various levels of migration complexity, ranging from simple import swaps to more involved changes such as modifying scope from CDI bean requirements. Additionally, we will look into migration scenarios that involves EJB Remote and Message Driven Bean(MBD) changes

We will focus on migrating a partially migrated JavaEE Coolstore application to Quarkus, a task that involves not only technical translation but also considerations for deployment in OpenShift. By the end of this demo, you will understand how Konveyor AI (Kai) can assist and expedite the modernization process.

Pre-req

  • Podman
  • VSCode
  • Git
  • OpenShift cluster
  • GenAI credentials
  • mvn
  • Quarkus 3.10
  • Java 17

Step 1: Setup

In this step, we will configure the Kai IDE plugin within VSCode to showcase the capabilities of Kai. For this demo, we will be using the model meta-llama/llama-3-70b-instruct within the Kai configuration. This setup ensures that the demo runs seamlessly within the VSCode environment, highlighting the efficiency and power of Kai for code migration in the context of our specific scenario.

Setup Kai VSCode IDE plugin

  • Follow along the steps listed in here to intergrate Kai IDE plugin with VSCode.

  • Before starting the Kai server, Select model meta-llama/llama-3-70b-instruct by uncommenting the following block in kai/config.toml file

# **IBM served codellama**
# ```
 [models]
   provider = "ChatIBMGenAI"

   [models.args]
      model_id = "meta-llama/llama-3-70b-instruct"
      parameters.max_new_tokens = 2048
# ```

Make sure to comment the default provider, as there can be only one model provider enabled at any given time.

[models]
provider = "ChatIBMGenAI"

[models.args]
model_id = "mistralai/mixtral-8x7b-instruct-v01"

If you are interested in using gpt-3.5-turbo refer to the section below,

Running Kai with GPT-3.5

  • Export your OpenAI api key export OPENAI_API_KEY="<your-openai-api-key>"

  • Before starting the Kai server, Select model gpt-3.5-turbo by uncommenting the following block in kai/config.toml file

# **OpenAI GPT 3.5**
# ```
 [models]
   provider = "ChatOpenAI"

   [models.args]
   model = "gpt-3.5-turbo"
# ```
  • Run the server using make run-server

Run Kai with demo mode

If you don't have access to GEN_AI key, you can run the server in demo mode which will use cached responses

To run the Kai server in demo mode, GENAI_KEY="dummyvalue" DEMO_MODE=true make run-server

Step 2: Demo

In this demo, we will explore Kai's capabilities through practical examples within the Coolstore application.

2.1 Clone the coolstore app

Let's clone the Coolstore application, which we will be used demo the migration process to Quarkus.

  1. To clone the Coolstore demo from its repository -

    git clone https://github.com/konveyor-ecosystem/coolstore.git

  2. Next, switch to the branch of the Coolstore app that's been partially migrated -

    git checkout partial-migration

2.2 Run Analysis

We will analyze the Coolstore application using the following migration targets to identify potential areas for improvement:

  • containterization
  • jakarta-ee
  • jakarta-ee8+
  • jakarata-ee9+
  • quarkus
  1. Open VSCode and load coolstore project if it is not already loaded.
  2. Follow steps from 1 through 4 from https://github.com/konveyor-ecosystem/kai/tree/main/ide#usage-summary
  3. Once the analysis is complete you will see incidents listed on the following files
  • src/main/java/com/redhat/coolstore/model/ShoppingCart.java
  • src/main/java/com/redhat/coolstore/model/InventoryEntity.java
  • src/main/java/com/redhat/coolstore/service/CatalogService.java
  • src/main/java/com/redhat/coolstore/service/ShippingService.java
  • src/main/java/com/redhat/coolstore/service/ShoppingCartOrderProcessor.java

Note: While the file src/main/java/com/redhat/coolstore/rest/RestApplication.java is flagged, for the purpose of this demo it is okay to skip that file and proceed.

The incidents in the above files will provide insights into potential issues or areas that require attention during the migration process.

2.2.1 Change import namespaces

  • Right-click on the file src/main/java/com/redhat/coolstore/model/ShoppingCart.java.
  • Select Kai Fix-All.
  • Accept the proposed changes.
  • Repeat the same process for the file - src/main/java/com/redhat/coolstore/model/InventoryEntity.java.

Change import namespaces

Th above steps show how Kai simplifies the translation of import namespaces, ensuring seamless automated migration of javax libraries to jakarta persistence libraries.

2.2.2 Modify Scope from CDI bean requirements

In this step, we will use Kai to modify the scope in CatalogService.java to adhere to Quarkus CDI bean requirements. Kai will handle this automatically, ensuring seamless migration.

  • Right-click on the file src/main/java/com/redhat/coolstore/service/CatalogService.java.
  • Select Kai Fix-All.
  • Accept the proposed changes.

Modify Scope from CDI bean requirements

2.2.3 EJB Remote and Message Driven Bean(MDB) changes

We will address EJB Remote and MDB functionalities in ShippingService.java and ShoppingCartOrderProcessor.java respectively. Kai will guide us through replacing EJBs with REST functionality and updating related imports and annotations.

EJB Remote
  • Right-click on the file src/main/java/com/redhat/coolstore/service/ShippingService.java.
  • Select Kai Fix-All.
  • Accept the proposed changes.

EJB Remote - Before/After

Due to the absence of support for Remote EJBs in Quarkus, you will notice that these functionalities are removed and replaced with REST functionality.

Message Driven Bean(MDB)

  • Right-click on the file src/main/java/com/redhat/coolstore/service/ShoppingCartOrderProcessor.java.

  • Select Kai Fix-All and accept the changes.

MDB - before/after

  • Note the changes made to ordersEmitter channel:

    @Inject
@Channel("orders")
Emitter<String> ordersEmitter;

    Although this change is valid, it's important to note that the LLM is unaware of the number of consumers for orders channel. It assumed that there is a single consumer for orders, but, this app has multiple consumers of orders. Assuming there's only a single consumer can lead to io.smallrye.reactive.messaging.providers.wiring.TooManyDownstreamCandidatesException. To avoid this, add the @Broadcast annotation:

    import io.smallrye.reactive.messaging.annotations.Broadcast;

@Inject
@Channel("orders")
@Broadcast
Emitter<String> ordersEmitter;

  • Since the LLM doesn't have access to the project's pom.xml file, it lacks information about available dependencies. Update the import statements to the following:

    import org.eclipse.microprofile.reactive.messaging.Channel;
import org.eclipse.microprofile.reactive.messaging.Emitter;

MDB - Manual updates

2.3 Deploy app to OpenShift

Now, its time to deploy the coolstore quarkus app on OpenShift cluster.

  • Make sure you can access the cluster from your terminal
  • Navigate to the coolstore project folder
  • Deploy a postgres container
oc new-app -e POSTGRESQL_USER=quarkus \
            -e POSTGRESQL_PASSWORD=quarkus \
            -e POSTGRESQL_DATABASE=coolstore \
            openshift/postgresql:latest \
            --name=coolstore-database
  • Once the postgres container is deployed, deploy the coolstore app using the following command
mvn clean compile package -Dquarkus.kubernetes.deploy=true
  • Once deployed, access the website via http://coolstore-<namespace>.apps.ai.migration.redhat.com

deploy app

Conclusion

In this demo, we showcased the capability of Kai in facilitating various types of code migrations within the Coolstore application. By leveraging Kai's capabilities, organizations can expedite the modernization process. If you are intereted to learn more about our ongoing efforts and future plans, please reach out to us in the slack channel