koog-sauce

Embeds Koog with other frameworks, featuring Spring AI integration through a specialized client utilizing Spring AI's ChatClient. Supports prompt execution and response handling using Koog's DSL.

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JVM

GitHub stars5

Authorskpavlov

Open issues5

LicenseMIT License

Creation date9 months ago

Last activity1 day ago

Latest release0.0.1 (9 months ago)

Homepage GitHub repository GitHub pages

Koog-sauce — The Finishing Touch

Koog-sauce is a missing ingredient that connects Koog with other frameworks.

Features

Spring AI Integration - Provides a Koog's SpringAiLLMClient, which uses Spring AI's ChatClient under the hood.
LangChain4j Integration - Offers Langchain4jLLMClient for seamless integration with LangChain4j, supporting both standard and streaming interactions.
AI Agent Builder - Simplifies creation and configuration of AI agents with a fluent builder pattern, making it easy to set up complex agent behaviors.

Requirements

JDK 17 or higher
Gradle 8.14.1 or higher

Getting Started

Add Dependency

Add the dependency to your build.gradle.kts file:

dependencies {
    // Core library
    implementation("me.kpavlov:koog-sauce:[LATEST]")

    // For Spring AI integration
    implementation("me.kpavlov:koog-sauce-spring-ai:[LATEST]")
    implementation("org.springframework.ai:spring-ai-openai:1.0.0")

    // For LangChain4j integration
    implementation("me.kpavlov:koog-sauce-langchain4j:[LATEST]")
    implementation("dev.langchain4j:langchain4j:0.24.0")
    implementation("dev.langchain4j:langchain4j-open-ai:0.24.0")

    // Koog library
    implementation("ai.koog:koog:0.4.0") // or newer
}

Build the Project

./gradlew build

Or using the Makefile:

make build

Usage

Spring AI Integration

// Create Spring AI ChatClient
val chatClient = org.springframework.ai.chat.client.ChatClient.builder(
    org.springframework.ai.openai.OpenAiChatModel
        .builder()
        .openAiApi(
            org.springframework.ai.openai.api.OpenAiApi
                .builder()
                .apiKey("your-api-key")
                .build(),
        ).build(),
).build()

// Create SpringAiLLMClient
val llmClient = me.kpavlov.koog.sauce.spring.ai.chat.SpringAiLLMClient(chatClient)

// Build a prompt using Koog DSL
val prompt = ai.koog.prompt.dsl.Prompt.build("myPrompt") {
    system("You are a helpful assistant")
    user("Tell me about Kotlin Multiplatform")
}

// Define the model to use
val model = ai.koog.prompt.llm.LLModel(
    ai.koog.prompt.llm.LLMProvider.OpenAI, 
    "gpt-4.1-nano", 
    listOf(ai.koog.prompt.llm.LLMCapability.Completion),
    100500
)

// Execute the prompt
suspend fun executePrompt() {
    val responses = llmClient.execute(prompt, model)

    // Process the response
    val response = responses.first()
    println("Response: ${response.content}")
}

See the complete example.

LangChain4j Integration

// Create LangChain4j ChatModel
val chatModel = dev.langchain4j.model.openai.OpenAiChatModel.builder()
    .apiKey("your-api-key")
    .modelName("gpt-4")
    .build()

// Create Langchain4jLLMClient
val llmClient = me.kpavlov.koog.sauce.langchain4j.Langchain4jLLMClient(chatModel = chatModel)

// Build a prompt using Koog DSL
val prompt = ai.koog.prompt.dsl.Prompt.build("myPrompt") {
    system("You are a helpful assistant")
    user("Tell me about LangChain4j")
}

// Define the model to use
val model = ai.koog.prompt.llm.LLModel(
    ai.koog.prompt.llm.LLMProvider.OpenAI, 
    "gpt-4", 
    listOf(ai.koog.prompt.llm.LLMCapability.Completion),
    100500,
)

// Execute the prompt
suspend fun executePrompt() {
    val responses = llmClient.execute(prompt, model)
    println("Response: ${responses.first().content}")
}

See the complete example.

AI Agent Builder

// Create a prompt executor
val promptExecutor = ai.koog.prompt.executor.model.PromptExecutor(
    llmClient = llmClient,
    defaultModel = model
)

// Create an AI agent using the builder
val agent = me.kpavlov.koog.sauce.agents.core.agent.AIAgent<String, String> {
    this.promptExecutor = promptExecutor
    this.strategy = YourCustomStrategy() // Implement AIAgentStrategy
    this.agentConfig = YourAgentConfig() // Implement AIAgentConfigBase
    this.toolRegistry = ToolRegistry.builder()
        .registerTool(YourCustomTool()) // Add your tools
        .build()
}

// Use the agent
suspend fun executeAgent() {
    val result = agent.execute("Tell me about Kotlin")
    println("Agent result: $result")
}

These examples demonstrate how to:

Integrate with Spring AI and LangChain4j
Build AI agents with the fluent builder pattern
Configure different components like prompt executors, strategies, and tools

Development

Available Make Commands

make build: Build the project
make test: Run tests
make clean: Clean the project
make publish: Publish to Maven Local
make doc: Generate KDoc documentation
make help: Show help message

Running Tests

./gradlew test

Or using the Makefile:

make test

Integration Tests

The project includes comprehensive tests for both Spring AI and LangChain4j integrations:

Spring AI Tests: SpringOpenAiTest demonstrates how to use and test the Spring AI integration with OpenAI models.
LangChain4j Tests:
- Langchain4jLLMClientTest tests the standard LangChain4j LLM client functionality.
- StreamingLangchain4jLLMClientTest tests the streaming capabilities of the LangChain4j LLM client.

These tests serve as additional examples of how to use the integrations in your own projects.

Generating Documentation

./gradlew dokkaGeneratePublicationHtml

Or using the Makefile:

make doc

The documentation is automatically generated and published to GitHub Pages when changes are pushed to the main branch. You can access the latest documentation at:

https://kpavlov.github.io/koog-sauce/

License

This project is licensed under the MIT License - see the LICENSE file for details.

JVM

GitHub stars5

Authorskpavlov

Open issues5

LicenseMIT License

Creation date9 months ago

Last activity1 day ago

Latest release0.0.1 (9 months ago)

Homepage GitHub repository GitHub pages

Koog-sauce — The Finishing Touch

Koog-sauce is a missing ingredient that connects Koog with other frameworks.

Features

Spring AI Integration - Provides a Koog's SpringAiLLMClient, which uses Spring AI's ChatClient under the hood.
LangChain4j Integration - Offers Langchain4jLLMClient for seamless integration with LangChain4j, supporting both standard and streaming interactions.
AI Agent Builder - Simplifies creation and configuration of AI agents with a fluent builder pattern, making it easy to set up complex agent behaviors.

Requirements

JDK 17 or higher
Gradle 8.14.1 or higher

Getting Started

Add Dependency

Add the dependency to your build.gradle.kts file:

dependencies {
    // Core library
    implementation("me.kpavlov:koog-sauce:[LATEST]")

    // For Spring AI integration
    implementation("me.kpavlov:koog-sauce-spring-ai:[LATEST]")
    implementation("org.springframework.ai:spring-ai-openai:1.0.0")

    // For LangChain4j integration
    implementation("me.kpavlov:koog-sauce-langchain4j:[LATEST]")
    implementation("dev.langchain4j:langchain4j:0.24.0")
    implementation("dev.langchain4j:langchain4j-open-ai:0.24.0")

    // Koog library
    implementation("ai.koog:koog:0.4.0") // or newer
}

Build the Project

./gradlew build

Or using the Makefile:

make build

Usage

Spring AI Integration

// Create Spring AI ChatClient
val chatClient = org.springframework.ai.chat.client.ChatClient.builder(
    org.springframework.ai.openai.OpenAiChatModel
        .builder()
        .openAiApi(
            org.springframework.ai.openai.api.OpenAiApi
                .builder()
                .apiKey("your-api-key")
                .build(),
        ).build(),
).build()

// Create SpringAiLLMClient
val llmClient = me.kpavlov.koog.sauce.spring.ai.chat.SpringAiLLMClient(chatClient)

// Build a prompt using Koog DSL
val prompt = ai.koog.prompt.dsl.Prompt.build("myPrompt") {
    system("You are a helpful assistant")
    user("Tell me about Kotlin Multiplatform")
}

// Define the model to use
val model = ai.koog.prompt.llm.LLModel(
    ai.koog.prompt.llm.LLMProvider.OpenAI, 
    "gpt-4.1-nano", 
    listOf(ai.koog.prompt.llm.LLMCapability.Completion),
    100500
)

// Execute the prompt
suspend fun executePrompt() {
    val responses = llmClient.execute(prompt, model)

    // Process the response
    val response = responses.first()
    println("Response: ${response.content}")
}

See the complete example.

LangChain4j Integration

// Create LangChain4j ChatModel
val chatModel = dev.langchain4j.model.openai.OpenAiChatModel.builder()
    .apiKey("your-api-key")
    .modelName("gpt-4")
    .build()

// Create Langchain4jLLMClient
val llmClient = me.kpavlov.koog.sauce.langchain4j.Langchain4jLLMClient(chatModel = chatModel)

// Build a prompt using Koog DSL
val prompt = ai.koog.prompt.dsl.Prompt.build("myPrompt") {
    system("You are a helpful assistant")
    user("Tell me about LangChain4j")
}

// Define the model to use
val model = ai.koog.prompt.llm.LLModel(
    ai.koog.prompt.llm.LLMProvider.OpenAI, 
    "gpt-4", 
    listOf(ai.koog.prompt.llm.LLMCapability.Completion),
    100500,
)

// Execute the prompt
suspend fun executePrompt() {
    val responses = llmClient.execute(prompt, model)
    println("Response: ${responses.first().content}")
}

See the complete example.

AI Agent Builder

// Create a prompt executor
val promptExecutor = ai.koog.prompt.executor.model.PromptExecutor(
    llmClient = llmClient,
    defaultModel = model
)

// Create an AI agent using the builder
val agent = me.kpavlov.koog.sauce.agents.core.agent.AIAgent<String, String> {
    this.promptExecutor = promptExecutor
    this.strategy = YourCustomStrategy() // Implement AIAgentStrategy
    this.agentConfig = YourAgentConfig() // Implement AIAgentConfigBase
    this.toolRegistry = ToolRegistry.builder()
        .registerTool(YourCustomTool()) // Add your tools
        .build()
}

// Use the agent
suspend fun executeAgent() {
    val result = agent.execute("Tell me about Kotlin")
    println("Agent result: $result")
}

These examples demonstrate how to:

Integrate with Spring AI and LangChain4j
Build AI agents with the fluent builder pattern
Configure different components like prompt executors, strategies, and tools

Development

Available Make Commands

make build: Build the project
make test: Run tests
make clean: Clean the project
make publish: Publish to Maven Local
make doc: Generate KDoc documentation
make help: Show help message

Running Tests

./gradlew test

Or using the Makefile:

make test

Integration Tests

The project includes comprehensive tests for both Spring AI and LangChain4j integrations:

Spring AI Tests: SpringOpenAiTest demonstrates how to use and test the Spring AI integration with OpenAI models.
LangChain4j Tests:
- Langchain4jLLMClientTest tests the standard LangChain4j LLM client functionality.
- StreamingLangchain4jLLMClientTest tests the streaming capabilities of the LangChain4j LLM client.

These tests serve as additional examples of how to use the integrations in your own projects.

Generating Documentation

./gradlew dokkaGeneratePublicationHtml

Or using the Makefile:

make doc

The documentation is automatically generated and published to GitHub Pages when changes are pushed to the main branch. You can access the latest documentation at:

https://kpavlov.github.io/koog-sauce/

License

This project is licensed under the MIT License - see the LICENSE file for details.