Spring AI vs LangChain4j：Java AI开发框架完整对比指南

🎯 框架概述

Spring AI 1.0 (2025年5月正式发布)

Spring AI是Spring生态系统的官方AI框架，专为Java开发者设计，提供了统一的AI模型访问接口和丰富的企业级功能。

核心理念：

• 🔄 模型可移植性：一套代码支持20+种AI模型
• 🏗️ Spring生态集成：无缝融入Spring Boot/Cloud
• 🛡️ 企业级特性：完整的可观测性和安全性
• 📊 结构化输出：类型安全的Java对象映射

LangChain4j (持续更新中)

LangChain4j是LangChain的Java实现，专注于构建LLM驱动的应用程序，提供了丰富的工具链和模块化设计。

核心理念：

• 🧩 模块化设计：灵活的组件组合
• 🔗 链式编程：直观的工作流构建
• 🌐 广泛兼容：支持多种LLM提供商
• 🛠️ 工具丰富：内置大量实用工具

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🆕 Spring AI 1.0 最新特性

1. ChatClient - 统一对话接口

@RestController
public class ChatController {
    
    private final ChatClient chatClient;
    
    public ChatController(ChatClient.Builder chatClientBuilder) {
        this.chatClient = chatClientBuilder.build();
    }
    
    @GetMapping("/chat")
    public String chat(@RequestParam String message) {
        return chatClient.prompt()
            .user(message)
            .call()
            .content();
    }
    
    // 结构化输出
    @GetMapping("/analyze")
    public ProductAnalysis analyzeProduct(@RequestParam String product) {
        return chatClient.prompt()
            .user("分析产品: " + product)
            .call()
            .entity(ProductAnalysis.class);
    }
}

record ProductAnalysis(String name, double price, List<String> features) {}

2. RAG (检索增强生成) 支持

@Configuration
public class RagConfiguration {
    
    @Bean
    public VectorStore vectorStore() {
        return new PgVectorStore(jdbcTemplate, embeddingModel);
    }
    
    @Bean
    public DocumentReader documentReader() {
        return new PdfDocumentReader(new ClassPathResource("docs/"));
    }
    
    @Bean
    public QuestionAnswerAdvisor qaAdvisor(VectorStore vectorStore) {
        return new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults());
    }
}

@Service
public class RagService {
    
    private final ChatClient chatClient;
    
    public RagService(ChatClient.Builder builder, QuestionAnswerAdvisor qaAdvisor) {
        this.chatClient = builder
            .defaultAdvisors(qaAdvisor)
            .build();
    }
    
    public String askWithContext(String question) {
        return chatClient.prompt()
            .user(question)
            .call()
            .content();
    }
}

3. 工具调用 (Function Calling)

@Component
public class WeatherService {
    
    @Tool("获取指定城市的天气信息")
    public String getWeather(String city) {
        // 调用天气API
        return "北京今天晴天，温度25°C";
    }
    
    @Tool("发送邮件通知")
    public String sendEmail(String to, String subject, String content) {
        // 发送邮件逻辑
        return "邮件已发送";
    }
}

@Service
public class AssistantService {
    
    private final ChatClient chatClient;
    
    public AssistantService(ChatClient.Builder builder, WeatherService weatherService) {
        this.chatClient = builder
            .defaultFunctions("getWeather", "sendEmail")
            .build();
    }
}

4. 对话记忆管理

@Configuration
public class MemoryConfiguration {
    
    @Bean
    public ChatMemory chatMemory() {
        return new InMemoryChatMemory();
    }
    
    @Bean
    public MessageChatMemoryAdvisor memoryAdvisor(ChatMemory chatMemory) {
        return new MessageChatMemoryAdvisor(chatMemory);
    }
}

@Service
public class ConversationService {
    
    private final ChatClient chatClient;
    
    public ConversationService(ChatClient.Builder builder, 
                              MessageChatMemoryAdvisor memoryAdvisor) {
        this.chatClient = builder
            .defaultAdvisors(memoryAdvisor)
            .build();
    }
    
    public String chat(String conversationId, String message) {
        return chatClient.prompt()
            .user(message)
            .advisors(advisorSpec -> advisorSpec
                .param(CHAT_MEMORY_CONVERSATION_ID_KEY, conversationId))
            .call()