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Copy file name to clipboardExpand all lines: docs/topics/kotlin-ai-apps-development-overview.md
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[//]: #(title: Kotlin for AI-powered app development)
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[//]: #(title: Kotlin 用于 AI 驱动的应用开发)
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Kotlin provides a modern and pragmatic foundation for building AI-powered applications.
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It can be used across platforms, integrates well with established AI frameworks, and supports common AI development patterns.
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## Kotlin AI agentic framework – Koog
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## Kotlin AI 代理化框架——Koog
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[Koog](https://github.com/JetBrains/koog) is a Kotlin-based framework for creating and running AI agents locally, without requiring external services.
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Koog is JetBrains' innovative, open-source agentic framework that empowers developers to build AI agents within the JVM ecosystem.
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It provides a pure Kotlin implementation for building intelligent agents that can interact with tools, handle complex workflows, and communicate with users.
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## More use cases
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## 更多使用场景
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There are many other use cases where Kotlin can help with AI development.
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From integrating language models into backend services to building AI-powered user interfaces,
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these examples showcase the versatility of Kotlin in various AI applications.
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### Retrieval-augmented generation
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### 检索增强生成
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Use Kotlin to build retrieval-augmented generation (RAG) pipelines that connect language models to external sources like documentation, vector stores, or APIs.
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For example:
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*[`springAI-demo`](https://github.com/Kotlin/Kotlin-AI-Examples/tree/master/projects/spring-ai/springAI-demo): A Spring Boot app that loads Kotlin standard library docs into a vector store and supports document-based Q&A.
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*[`langchain4j-spring-boot`](https://github.com/Kotlin/Kotlin-AI-Examples/tree/master/projects/langchain4j/langchain4j-spring-boot): A minimal RAG example using LangChain4j.
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### Agent-based applications
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### 基于代理的应用程序
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Build AI agents in Kotlin that reason, plan, and act using language models and tools.
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For example:
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*[`koog`](https://github.com/JetBrains/koog): Shows how to use the Kotlin agentic framework Koog to build AI agents.
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*[`langchain4j-spring-boot`](https://github.com/Kotlin/Kotlin-AI-Examples/tree/master/projects/langchain4j/langchain4j-spring-boot): Includes a simple tool-using agent built with LangChain4j.
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### Chain of thought prompting
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### 思维链提示
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Implement structured prompting techniques that guide language models through multistep reasoning.
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For example:
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*[`LangChain4j_Overview.ipynb`](https://github.com/Kotlin/Kotlin-AI-Examples/blob/master/notebooks/langchain4j/LangChain4j_Overview.ipynb): A Kotlin Notebook demonstrating chain of thought and structured output.
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### LLMs in backend services
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### 后端服务中的 LLM
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Integrate LLMs into business logic or REST APIs using Kotlin and Spring.
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For example:
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*[`spring-ai-examples`](https://github.com/Kotlin/Kotlin-AI-Examples/tree/master/projects/spring-ai/spring-ai-examples): Includes classification, chat, and summarization examples.
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*[`springAI-demo`](https://github.com/Kotlin/Kotlin-AI-Examples/tree/master/projects/spring-ai/springAI-demo): Demonstrates full integration of LLM responses with application logic.
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### Multiplatform user interfaces with AI
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### 具有 AI 的多平台用户界面
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Use Compose Multiplatform to build interactive AI-powered UIs in Kotlin.
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For example:
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*[`mcp-demo`](https://github.com/Kotlin/Kotlin-AI-Examples/tree/master/projects/mcp/mcp-demo): A desktop UI that connects to Claude and OpenAI, and presents responses using Compose Multiplatform.
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## Explore examples
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## 探索示例
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You can explore and run examples from the [Kotlin-AI-Examples](https://github.com/Kotlin/Kotlin-AI-Examples) repository.
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Each project is self-contained. You can use each project as a reference or template for building Kotlin-based AI applications.
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## What's next
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## 下一步做什么
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* Complete the [Build a Kotlin app that uses Spring AI to answer questions based on documents stored in the Qdrant](spring-ai-guide.md)
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tutorial to learn more about using Spring AI with Kotlin in IntelliJ IDEA
Copy file name to clipboardExpand all lines: docs/topics/multiplatform.topic
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简化跨平台项目的开发，并减少为不同平台编写和维护相同代码所花费的时间。
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</description>
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<spotlight>
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<ahref="https://www.jetbrains.com/help/kotlin-multiplatform-dev/quickstart.html"type="cross-platform"summary="Set up and run your first KMP project">Kotlin Multiplatform quickstart</a>
<ahref="https://www.jetbrains.com/help/kotlin-multiplatform-dev/multiplatform-ios-integration-overview.html"summary="Integrate a Kotlin Multiplatform shared module into your iOS app">Set up iOS integration</a>
<ahref="https://www.jetbrains.com/help/kotlin-multiplatform-dev/quickstart.html"summary="Setup and run your first KMP project">Set up and run your first KMP project</a>
<ahref="https://plugins.jetbrains.com/plugin/14936-kotlin-multiplatform"summary="Kotlin Multiplatform plugin for IntelliJ IDEA and Android Studio">Kotlin Multiplatform IDE plugin</a>
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<ahref="https://www.jetbrains.com/help/kotlin-multiplatform-dev/compose-hot-reload.html"summary="Quickly iterate on your UI with Compose Hot Reload">Compose Hot Reload</a>
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<ahref="https://plugins.jetbrains.com/plugin/14936-kotlin-multiplatform"summary="用于 IntelliJ IDEA 与 Android Studio 的 Kotlin 多平台插件">Kotlin 多平台 IDE 插件</a>
## Create a controller to load and search documents
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## 创建一个 controller 来加载并搜索文档
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Create a Spring `@RestController` to search documents and store them in the Qdrant collection:
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{style="tip"}
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## Implement an AI chat endpoint
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## 实现 AI 聊天端点
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Once the documents are loaded, the final step is to add an endpoint that answers questions using the documents in Qdrant through Spring AI's Retrieval-Augmented Generation (RAG) support:
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