### Properties File Example

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Example structure of a properties file for configuring OpenAI or Azure OpenAI client settings.

```properties
# If using openai.com
client.openai.key:"my-key"
client.openai.organizationid:"my-org-id"
# if using Azure Open AI
client.azureopenai.key:"my-key"
client.azureopenai.endpoint:"url of azure openai endpoint"
client.azureopenai.deploymentname:"deployment name"
```

--------------------------------

### Basic Code Block Example

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/resources/com/microsoft/semantickernel/samples/syntaxexamples/rag/example.md

This is a simple code block demonstrating basic syntax. It does not require specific imports or setup.

```plaintext
some code
```

--------------------------------

### Example: OpenAI Connector with Maven

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/PACKAGES.md

Configure your Maven project to use the OpenAI AI services connector along with the core API and BOM.

```xml
<project>
    <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>com.microsoft.semantic-kernel</groupId>
                <artifactId>semantickernel-bom</artifactId>
                <version>${semantickernel.version}</version>
                <scope>import</scope>
                <type>pom</type>
            </dependency>
        </dependencies>
    </dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>com.microsoft.semantic-kernel</groupId>
            <artifactId>semantickernel-api</artifactId>
        </dependency>
        <dependency>
            <groupId>com.microsoft.semantic-kernel</groupId>
            <artifactId>semantickernel-connectors-ai-openai</artifactId>
        </dependency>
    </dependencies>
</project>
```

--------------------------------

### Define Kernel with AI Service

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a Kernel instance using the Kernel builder pattern, configuring it with an AI service. This example shows how to build a Kernel with a pre-configured ChatCompletionService.

```java
ChatCompletionService chatCompletionService = ChatCompletionService.builder()
    .withOpenAIAsyncClient(client)
    .withModelId("gpt-3.5-turbo-0613")
    .withServiceId("fridayChatGeneration")
    .build();

return Kernel.builder()
    .withAIService(ChatCompletionService.class, chatCompletionService);
```

--------------------------------

### Direct Call to TextPlugin Function

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Example of directly calling a function from a plugin, such as the TextPlugin's uppercase method.

```java
TextPlugin text = new TextPlugin();
return text.uppercase("ciao!");
```

--------------------------------

### Retrieve an AI Service from Kernel

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Get an instance of a specific AI service, such as ChatCompletionService, from the configured Kernel.

```java
ChatCompletionService service = kernel.getService(ChatCompletionService.class);
```

--------------------------------

### Define a KernelPlugin with Native Functions

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a KernelPlugin by defining a class with methods annotated with @DefineKernelFunction. This example defines 'date' and 'time' functions within a 'Time' class.

```java
public static class Time {

    @DefineKernelFunction(name = "date")
    public String date() {
        System.out.println("date is called");
        Date now = new Date();
        SimpleDateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd");
        return dateFormat.format(now);
    }

    @DefineKernelFunction(name = "time")
    public String time() {
        System.out.println("time is called");
        Date now = new Date();
        SimpleDateFormat timeFormat = new SimpleDateFormat("HH:mm:ss");
        return timeFormat.format(now);
    }
}

KernelPlugin time = KernelPluginFactory.createFromObject(new Time(), "time");
```

--------------------------------

### Add PostChatCompletionHook

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Add a hook to process the results after a chat completion. This example prints the completion ID and logs that the hook was triggered.

```java
kernel.getGlobalKernelHooks().addPostChatCompletionHook(event -> {
    System.out.println("------- Triggered after ChatCompletion ------- ");
    System.out.println("--- Output ChatCompletion and id ----");
    System.out.println("Chat completion");
    System.out.println("Id: " + event.getChatCompletions().getId());
    return event;
});
```

--------------------------------

### Add PreChatCompletionHook

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Add a hook to modify chat completion options before the LLM call. This example adds a system message to instruct the LLM to use uppercase text.

```java
kernel.getGlobalKernelHooks().addPreChatCompletionHook(event -> {
    ChatCompletionsOptions options = event.getOptions();
    List<ChatRequestMessage> messages = options.getMessages();
    messages = new ArrayList<>(messages);
    messages.add(new ChatRequestSystemMessage("Use upper case text when responding to the prompt."));
    System.out.println("------- Triggered before ChatCompletion ------- ");
    System.out.println("---- Added: Use upper case text when responding to the prompt. ----");
    for (ChatRequestMessage msg : messages) {
        System.out.println(msg);
    }

    return new PreChatCompletionEvent(
        PreChatCompletionHook.cloneOptionsWithMessages(options, messages)
    );
});
```

--------------------------------

### Invoke KernelFunction via Kernel.invokeAsync

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Invoke a KernelFunction using the Kernel's invokeAsync method, providing arguments and blocking for the result. This example uses a pre-defined plugin.

```java
Kernel kernel = Kernel.builder().build();
KernelPlugin kernelPlugin = KernelPluginFactory.createFromObject(new StaticTextPlugin(), "text");
KernelFunctionArguments arguments = KernelFunctionArguments.builder()
    .withVariable("input", "Today is: ")
    .withVariable("day", "Monday")
    .build();

return kernel.invokeAsync(kernelPlugin.get("AppendDay"))
    .withArguments(arguments)
    .block()
    .getResult();
```

--------------------------------

### Implement PromptRenderedHook

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a hook that triggers after a prompt has been rendered. This handler can modify the final prompt string, for example, by appending instructions.

```java
PromptRenderedHook myRenderedHandler = event -> {
    System.out.println(event.getFunction().getName() + " : Triggered PromptRenderedHook");
    String prompt = event.getPrompt() + "\nUSE SHORT, CLEAR, COMPLETE SENTENCES.";
    return new PromptRenderedEvent(event.getFunction(), event.getArguments(), prompt);
};
```

--------------------------------

### Define a Native Kernel Function

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Implement a native Kernel function within a class, using the @DefineKernelFunction annotation for the function name and description, and @KernelFunctionParameter for parameters. This example defines an 'Uppercase' function.

```java
public class TextPlugin {
    @DefineKernelFunction(description = "Change all string chars to uppercase.", name = "Uppercase")
    public String uppercase(@KernelFunctionParameter(description = "Text to uppercase", name = "input") String text) {
        return text.toUpperCase(Locale.ROOT);
    }
}
```

--------------------------------

### Directly Call AI Service for Chat Response

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Invoke the AI service directly to get chat completion results by providing chat history and the kernel instance. Use .block() to wait for the asynchronous operation to complete.

```java
ChatCompletionService service = kernel.getService(ChatCompletionService.class);
var chatHistory = new ChatHistory(systemMessage);
chatHistory.addUserMessage(userMessage);
var answer = service.getChatMessageContentsAsync(chatHistory, kernel, null).block();
```

--------------------------------

### Using Different Prompt Template Formats

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Demonstrates invoking prompts using both 'semantic-kernel' and 'handlebars' template formats. Ensure the correct template factory is provided.

```java
runPrompt(kernel,
        "semantic-kernel",
        "Hello AI, my name is {{$name}}. What is the origin of my name?",
        templateFactory);

runPrompt(kernel,
        "handlebars",
        "Hello AI, my name is {{name}}. What is the origin of my name?",
        templateFactory);
```

--------------------------------

### Full Build with Static Analysis and Tests

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/BUILD.md

Perform a full build that includes static analysis and end-to-end tests. This command may require a valid OpenAI key.

```shell
./mvnw clean install -Prelease,bug-check,with-samples
```

--------------------------------

### Run Sample with OpenAI

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Execute a Java sample using OpenAI by setting environment variables for client type, key, and organization ID.

```shell
OPENAI_CLIENT_TYPE=OPENAI \
OPEN_AI_KEY="my-key" \
OPEN_AI_ORGANIZATION_ID="organisation id" \
../../mvnw clean package exec:java -Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Create Prompt Template with PromptTemplateFactory.build

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a PromptTemplate object using PromptTemplateFactory.build for simpler template configurations. Ensure the systemPromptTemplate variable is defined.

```java
String systemPromptTemplate = "...";
PromptTemplate promptTemplate = PromptTemplateFactory.build(
    PromptTemplateConfig
        .builder()
        .withTemplate(systemPromptTemplate)
        .build()
);
```

--------------------------------

### Build and Test .NET Projects

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/CONTRIBUTING.md

Use these commands to build and test .NET projects within the Semantic Kernel repository. Ensure your environment is set up for .NET development.

```bash
run build.cmd
```

```bash
bash build.sh
```

--------------------------------

### Run Sample with Azure OpenAI

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Execute a Java sample using Azure OpenAI by setting environment variables for client type, key, and endpoint.

```shell
OPENAI_CLIENT_TYPE=AZURE_OPEN_AI \
AZURE_OPEN_AI_KEY="my-key" \
AZURE_OPEN_AI_ENDPOINT="endpoint url" \
../../mvnw clean package exec:java -Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Compile Java Samples

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Compile all Java samples using the Maven wrapper.

```shell
ப்பட்டு../../mvnw clean package
```

--------------------------------

### Build Kernel with Plugins and Services

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Construct a Kernel instance using the builder pattern, adding plugins and AI services. This snippet demonstrates adding a custom plugin and an AI service.

```java
Kernel kernel = Kernel.builder()
    .withPlugin(myPlugin)
    .withAIService(openAiChatService)
    .withServiceSelector()
    .build();
```

--------------------------------

### Run Compiled Java Sample

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Execute a specific compiled Java sample using the Maven wrapper and specifying the sample name.

```shell
../../mvnw exec:java -Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Build Semantic Kernel with Maven Wrapper

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/BUILD.md

Clone the repository and build the project using the Maven Wrapper. This is the standard way to build the project.

```shell
git clone https://github.com/microsoft/semantic-kernel-java
cd semantic-kernel
./mvnw install
```

--------------------------------

### System Properties Configuration for OpenAI

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Configure OpenAI client settings directly via system properties on the command line.

```shell
# OpenAI
../../mvnw exec:java \
-DOPENAI_CLIENT_TYPE=AZURE_OPEN_AI \
-Dclient.openai.key="my-key" \
-Dclient.openai.organizationid="my-org-id" \
-Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Properties File Configuration

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Configure client settings by specifying a properties file location using the CONF_PROPERTIES environment variable or system property.

```shell
CONF_PROPERTIES=my.properties \
OPENAI_CLIENT_TYPE=OPEN_AI \
../../mvnw exec:java -Dsample=Example04_CombineLLMPromptsAndNativeCode

OR

../../mvnw exec:java \
-DCONF_PROPERTIES=my.properties \
-DOPENAI_CLIENT_TYPE=AZURE_OPEN_AI \
-Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Build with Bug Check Profile

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/BUILD.md

Clean, package, and run the bug-check profile before submitting a pull request. This profile enforces formatting and detects static analysis issues.

```shell
./mvnw clean package -Pbug-check
```

--------------------------------

### System Properties Configuration for Azure OpenAI

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Configure Azure OpenAI client settings directly via system properties on the command line.

```shell
# Azure
../../mvnw exec:java \
-DOPENAI_CLIENT_TYPE=AZURE_OPEN_AI \
-Dclient.azureopenai.key="my-key" \
-Dclient.azureopenai.endpoint="url of azure openai endpoint" \
-Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Create KernelFunction from Simple Prompt

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a KernelFunction directly from a simple prompt string. Configure execution settings and specify the template format.

```java
var message = "Translate this date " + date + " to French format";
var fixedFunction = KernelFunction
        .createFromPrompt(message)
        .withDefaultExecutionSettings(
                PromptExecutionSettings.builder()
                        .withMaxTokens(100)
                        .build())
        .withTemplateFormat(PromptTemplateConfig.SEMANTIC_KERNEL_TEMPLATE_FORMAT)
        .withName("translator")
        .build();
```

--------------------------------

### Create Prompt Template with KernelPromptTemplateFactory

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Define a prompt template string and create a PromptTemplate object using KernelPromptTemplateFactory. This is useful for complex templates with dynamic variables.

```java
String functionDefinition = """
    Today is: {{time.date}}
    Current time is: {{time.time}}

    Answer the following questions using JSON syntax, including the data used.
    Is it morning, afternoon, evening, or night (morning/afternoon/evening/night)?
    Is it weekend time (weekend/not weekend)?
""";

PromptTemplate promptTemplate = new KernelPromptTemplateFactory().tryCreate(
    PromptTemplateConfig
        .builder()
        .withTemplate(functionDefinition)
        .build()
);
```

--------------------------------

### Create KernelFunction from Prompt Template

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Use KernelFunctionFromPrompt.builder() to create a function from a multi-line prompt template. Configure execution settings like temperature and max tokens.

```java
String promptTemplate = """
    Generate a creative reason or excuse for the given event.
    Be creative and be funny. Let your imagination run wild.

    Event: I am running late.
    Excuse: I was being held ransom by giraffe gangsters.

    Event: I haven't been to the gym for a year
    Excuse: I've been too busy training my pet dragon.

    Event: {{$input}}
""".stripIndent();

var excuseFunction = KernelFunctionFromPrompt.builder()
    .withTemplate(promptTemplate)
    .withDefaultExecutionSettings(
        PromptExecutionSettings.builder()
            .withTemperature(0.4)
            .withTopP(1)
            .withMaxTokens(500)
            .withUser("bx-h")
            .build()
    )
    .withName("ExcuseGeneratorFunction")
    .build();
```

--------------------------------

### Set OpenAI Client Type via Command Line

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Specify the OpenAI client type (OpenAI or Azure OpenAI) directly on the command line when running a sample.

```shell
OPENAI_CLIENT_TYPE=OPEN_AI ../../mvnw exec:java -Dsample=Example04_CombineLLMPromptsAndNativeCode

OR

../../mvnw exec:java -DOPENAI_CLIENT_TYPE=AZURE_OPEN_AI -Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Run All Tests Locally

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/BUILD.md

Execute all tests, including integration tests that require an OpenAI key. Ensure environment variables for API keys are set if needed.

```shell
./mvnw verify -Prelease,bug-check,with-samples
```

--------------------------------

### Define an AI Service with OpenAI

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Configure and build a ChatCompletionService using the builder pattern, specifying the OpenAI client, model ID, and a service ID.

```java
ChatCompletionService chatCompletionService = ChatCompletionService.builder()
    .withOpenAIAsyncClient(client)
    .withModelId("gpt-3.5-turbo-0613")
    .withServiceId("fridayChatGeneration")
    .build();
```

--------------------------------

### Run Unit Tests Locally

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/BUILD.md

Execute only the unit tests for the Semantic Kernel Java project. This command does not require an OpenAI key.

```shell
./mvnw package
```

--------------------------------

### Environment Variables for OpenAI

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Set environment variables for OpenAI client type, key, and organization ID to run a sample.

```shell
# OPENAI:
OPENAI_CLIENT_TYPE=OPEN_AI \
OPEN_AI_KEY="my-key" \
OPEN_AI_ORGANIZATION_ID="organisation id" \
../../mvnw clean package exec:java -Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Build and Test TypeScript Projects

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/CONTRIBUTING.md

Execute these commands to build and test TypeScript components of the Semantic Kernel. This is essential for ensuring code integrity.

```bash
yarn build
```

--------------------------------

### Define KernelFunctionArguments with Input

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create KernelFunctionArguments using the builder pattern, setting the primary input value.

```java
KernelFunctionArguments.builder().withInput("Jupiter").build();
```

--------------------------------

### Implement PromptRenderingHook

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a hook that triggers when a prompt is about to be rendered. This handler can modify arguments, such as adding a 'style' context variable.

```java
PromptRenderingHook myRenderingHandler = event -> {
    System.out.println(event.getFunction().getName() + " : Triggered PromptRenderingHook");
    event.getArguments().put("style", ContextVariable.of("Seinfeld"));
    return event;
};
```

--------------------------------

### Lint and Format .NET Code

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/CONTRIBUTING.md

Automatically format and lint .NET code to ensure consistency and adherence to project standards. This command helps in maintaining code quality.

```bash
dotnet format
```

--------------------------------

### Environment Variables for Azure OpenAI

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/semantickernel-syntax-examples/README.md

Set environment variables for Azure OpenAI client type, key, and endpoint to run a sample.

```shell
# AZURE:
OPENAI_CLIENT_TYPE=AZURE_OPEN_AI \
AZURE_OPEN_AI_KEY="my-key" \
AZURE_OPEN_AI_ENDPOINT="endpoint url" \
../../mvnw clean package exec:java -Dsample=Example04_CombineLLMPromptsAndNativeCode
```

--------------------------------

### Import Semantic Kernel BOM for Version Management

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/PACKAGES.md

Use the semantickernel-bom in your Maven project's dependencyManagement to define versions for all Semantic Kernel packages.

```xml
<dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>com.microsoft.semantic-kernel</groupId>
                <artifactId>semantickernel-bom</artifactId>
                <version>${semantickernel.version}</version>
                <scope>import</scope>
                <type>pom</type>
            </dependency>
        </dependencies>
    </dependencyManagement>
```

--------------------------------

### Define KernelFunction from JSON Configuration

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a KernelFunction by parsing a JSON configuration payload. This allows defining function schema, name, description, and completion settings.

```java
var prompt = "Hello AI, what can you do for me?";
String configPayload = """
      {
      "schema": 1,
      "name": "HelloAI",
      "description": "Say hello to an AI",
      "type": "completion",
      "completion": {
        "max_tokens": 256,
        "temperature": 0.5,
        "top_p": 0.0,
        "presence_penalty": 0.0,
        "frequency_penalty": 0.0
      }
    }
""".stripIndent();

PromptTemplateConfig promptConfig = PromptTemplateConfig
    .parseFromJson(configPayload)
    .copy()
    .withTemplate(prompt)
    .build();

var func = KernelFunction
    .createFromPrompt(promptConfig)
    .build();
```

--------------------------------

### Define KernelFunctionArguments with Variable

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create KernelFunctionArguments using the builder pattern, specifying variables by name.

```java
KernelFunctionArguments.builder().withVariable("input", "Jupiter").build();
```

--------------------------------

### Render Prompt Without LLM Query

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Render a prompt template asynchronously without sending it to an LLM. This is useful for debugging or pre-processing prompts. The result is printed to the console.

```java
var renderedPrompt = promptTemplate.renderAsync(kernel, KernelFunctionArguments, InvocationContext).block();
System.out.println(renderedPrompt);
```

--------------------------------

### Implement FunctionInvokingHook

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a hook that triggers before a function is called. This handler logs the function name and can be used for pre-execution logic.

```java
FunctionInvokingHook preHook = event -> {
    System.out.println(event.getFunction().getName() + " : Pre Execution Handler - Triggered");
    return event;
};
kernel.getGlobalKernelHooks().addHook("", preHook);
```

--------------------------------

### Invoke KernelFunction with Specific Arguments

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Invoke a KernelFunction (excuseFunction) using Kernel.invokeAsync, providing arguments via the builder and blocking for the result.

```java
var result = kernel
    .invokeAsync(excuseFunction)
    .withArguments(
        KernelFunctionArguments.builder()
            .withInput("I missed the F1 final race")
            .build()
    )
    .block();
```

--------------------------------

### Register Global Kernel Hook

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Register a hook to be executed globally across all kernel operations. Use this for cross-cutting concerns that should always be applied.

```java
kernel.getGlobalKernelHooks().addHook("hookName", KernelHook);
```

--------------------------------

### Add Semantic Kernel API Dependency

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/PACKAGES.md

Include the semantickernel-api package in your Maven project to access the core public API.

```xml
<dependency>
        <groupId>com.microsoft.semantic-kernel</groupId>
        <artifactId>semantickernel-api</artifactId>
    </dependency>
```

--------------------------------

### Register Kernel Hook for Single Invocation

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Register hooks that are effective only for a specific kernel invocation. This allows for fine-grained control over behavior for individual function calls.

```java
KernelHooks kernelHooks = new KernelHooks();
kernelHooks.addPreChatCompletionHook(...);

var result = kernel.invokeAsync(writerFunction)
    .withArguments(KernelFunctionArguments.builder().build())
    .addKernelHooks(kernelHooks)
    .block();
```

--------------------------------

### Implement FunctionInvokedHook

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/samples/semantickernel-concepts/README.md

Create a hook that triggers after a function has been called. This handler modifies the result by replacing vowels and numbers with asterisks.

```java
FunctionInvokedHook hook = event -> {
    String result = (String) event.getResult().getResult();
    System.out.println(event.getFunction().getName() + " : Modified result via FunctionInvokedHook: " + result);
    result = result.replaceAll("[aeiouAEIOU0-9]", "*");

    return new FunctionInvokedEvent(
        event.getFunction(),
        event.getArguments(),
        new FunctionResult<>(ContextVariable.of(result), event.getResult().getMetadata(), result)
    );
};
kernel.getGlobalKernelHooks().addHook(hook);
```

--------------------------------

### Lint and Fix TypeScript Code

Source: https://github.com/microsoft/semantic-kernel-java/blob/main/CONTRIBUTING.md

Apply automatic fixes to linting issues in TypeScript code. This command helps maintain code style and quality across the project.

```bash
yarn lint:fix
```

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