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LangChain4j Spring Boot Integration

LangChain4j与Spring Boot集成

To accomplish integration of LangChain4j with Spring Boot applications, follow this comprehensive guidance covering auto-configuration, declarative AI Services, chat models, embedding stores, and production-ready patterns for building AI-powered applications.
要实现LangChain4j与Spring Boot应用的集成，请遵循这份全面指南，内容涵盖自动配置、声明式AI服务、聊天模型、嵌入存储，以及构建AI驱动应用的生产就绪模式。

When to Use

适用场景

To accomplish integration of LangChain4j with Spring Boot when:
  • Integrating LangChain4j into existing Spring Boot applications
  • Building AI-powered microservices with Spring Boot
  • Setting up auto-configuration for AI models and services
  • Creating declarative AI Services with Spring dependency injection
  • Configuring multiple AI providers (OpenAI, Azure, Ollama, etc.)
  • Implementing RAG systems with Spring Boot
  • Setting up observability and monitoring for AI components
  • Building production-ready AI applications with Spring Boot
在以下场景中，可实现LangChain4j与Spring Boot的集成：
  • 将LangChain4j集成到现有Spring Boot应用中
  • 使用Spring Boot构建AI驱动的微服务
  • 为AI模型和服务设置自动配置
  • 借助Spring依赖注入创建声明式AI服务
  • 配置多个AI提供商（OpenAI、Azure、Ollama等）
  • 基于Spring Boot实现RAG系统
  • 为AI组件设置可观测性与监控
  • 使用Spring Boot构建生产就绪的AI应用

Overview

概述

LangChain4j Spring Boot integration provides declarative AI Services through Spring Boot starters, enabling automatic configuration of AI components based on properties. The integration combines the power of Spring dependency injection with LangChain4j's AI capabilities, allowing developers to create AI-powered applications using interface-based definitions with annotations.
LangChain4j与Spring Boot的集成通过Spring Boot Starter提供声明式AI服务，支持基于属性自动配置AI组件。该集成结合了Spring依赖注入的能力与LangChain4j的AI功能，允许开发者通过带注解的接口定义来创建AI驱动的应用。

Core Concepts

核心概念

To accomplish basic setup of LangChain4j with Spring Boot:
Add Dependencies:
xml
<!-- Core LangChain4j -->
<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-spring-boot-starter</artifactId>
    <version>1.8.0</version> // Use latest version
</dependency>

<!-- OpenAI Integration -->
<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
    <version>1.8.0</version>
</dependency>
Configure Properties:
properties
undefined
要完成LangChain4j与Spring Boot的基础设置：
添加依赖：
xml
<!-- Core LangChain4j -->
<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-spring-boot-starter</artifactId>
    <version>1.8.0</version> // Use latest version
</dependency>

<!-- OpenAI Integration -->
<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
    <version>1.8.0</version>
</dependency>
配置属性：
properties
undefined

application.properties

application.properties

langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY} langchain4j.open-ai.chat-model.model-name=gpt-4o-mini langchain4j.open-ai.chat-model.temperature=0.7

**Create Declarative AI Service:**
```java
@AiService
interface CustomerSupportAssistant {
    @SystemMessage("You are a helpful customer support agent for TechCorp.")
    String handleInquiry(String customerMessage);
}
langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY} langchain4j.open-ai.chat-model.model-name=gpt-4o-mini langchain4j.open-ai.chat-model.temperature=0.7

**创建声明式AI服务：**
```java
@AiService
interface CustomerSupportAssistant {
    @SystemMessage("You are a helpful customer support agent for TechCorp.")
    String handleInquiry(String customerMessage);
}

Configuration

配置

To accomplish Spring Boot configuration for LangChain4j:
Property-Based Configuration: Configure AI models through application properties for different providers.
Manual Bean Configuration: For advanced configurations, define beans manually using @Configuration.
Multiple Providers: Support for multiple AI providers with explicit wiring when needed.
要完成LangChain4j的Spring Boot配置：
基于属性的配置： 通过应用属性为不同提供商配置AI模型。
手动Bean配置： 对于高级配置，使用@Configuration手动定义Bean。
多提供商支持： 必要时，支持显式连接多个AI提供商。

Declarative AI Services

声明式AI服务

To accomplish interface-based AI service definitions:
Basic AI Service: Create interfaces with @AiService annotation and define methods with message templates.
Streaming AI Service: Implement streaming responses using Reactor or Project Reactor.
Explicit Wiring: Specify which model to use with @AiService(wiringMode = EXPLICIT, chatModel = "modelBeanName").
要完成基于接口的AI服务定义：
基础AI服务： 创建带有@AiService注解的接口，并定义带消息模板的方法。
流式AI服务： 使用Reactor或Project Reactor实现流式响应。
显式连接： 通过@AiService(wiringMode = EXPLICIT, chatModel = "modelBeanName")指定要使用的模型。

RAG Implementation

RAG实现

To accomplish RAG system implementation:
Embedding Stores: Configure various embedding stores (PostgreSQL/pgvector, Neo4j, Pinecone, etc.).
Document Ingestion: Implement document processing and embedding generation.
Content Retrieval: Set up content retrieval mechanisms for knowledge augmentation.
要完成RAG系统的实现：
嵌入存储： 配置各类嵌入存储（PostgreSQL/pgvector、Neo4j、Pinecone等）。
文档摄入： 实现文档处理与嵌入生成。
内容检索： 设置用于知识增强的内容检索机制。

Tool Integration

工具集成

To accomplish AI tool integration:
Spring Component Tools: Define tools as Spring components with @Tool annotations.
Database Access Tools: Create tools for database operations and business logic.
Tool Registration: Automatically register tools with AI services.
要完成AI工具集成：
Spring组件工具： 将工具定义为带有@Tool注解的Spring组件。
数据库访问工具： 创建用于数据库操作和业务逻辑的工具。
工具注册： 自动将工具注册到AI服务中。

Examples

示例

To understand implementation patterns, refer to the comprehensive examples in references/examples.md.
要了解实现模式，请参考references/examples.md中的完整示例。

Best Practices

最佳实践

To accomplish production-ready AI applications:
  • Use Property-Based Configuration: External configuration over hardcoded values
  • Implement Proper Error Handling: Graceful degradation and meaningful error responses
  • Use Profiles for Different Environments: Separate configurations for development, testing, and production
  • Implement Proper Logging: Debug AI service calls and monitor performance
  • Secure API Keys: Use environment variables and never commit to version control
  • Handle Failures: Implement retry mechanisms and fallback strategies
  • Monitor Performance: Add metrics and health checks for observability
要构建生产就绪的AI应用：
  • 使用基于属性的配置： 优先使用外部配置而非硬编码值
  • 实现完善的错误处理： 优雅降级并返回有意义的错误响应
  • 为不同环境使用配置文件： 为开发、测试和生产环境分别配置
  • 实现完善的日志记录： 调试AI服务调用并监控性能
  • 保护API密钥： 使用环境变量，切勿提交到版本控制系统
  • 处理故障： 实现重试机制与回退策略
  • 监控性能： 添加指标与健康检查以实现可观测性

References

参考资料

For detailed API references, advanced configurations, and additional patterns, refer to:
  • API Reference - Complete API reference and configurations
  • Examples - Comprehensive implementation examples
  • Configuration Guide - Deep dive into configuration options
如需详细的API参考、高级配置及更多模式，请参考：
  • API参考 - 完整的API参考与配置说明
  • 示例 - 全面的实现示例
  • 配置指南 - 配置选项深度解析