Spring AI 1.0 正式发布！AI 开发从未如此简单，不懂它的三层开发模式你就输麻了！

// 新架构下的客户端构建方式
ChatClient chatClient = new ChatClient(
    new OpenAiChatModel("your-api-key"),  // 模型实现与客户端解耦
    new PromptTemplate("您好，请回答：{{question}}")
);

// 新版聊天记忆的使用方式
@Bean
public ChatMemoryRepository chatMemoryRepository() {
    return new JdbcChatMemoryRepository(jdbcTemplate);
}

// 在聊天客户端中应用
chatClient.exchange(
    new ChatMemoryId("user-123"),
    new UserMessage("新的问题")
);

// 构建文档处理流水线
ChatClient chatClient = ChatClient.builder()
    .chatModel(chatModel)
    .advisor(new VectorStoreAdvisor(
        vectorStore,
        embeddingClient,
        List.of(
            new DocumentMetadataFilterPostProcessor(filterCriteria),
            new DocumentRerankingPostProcessor(threshold)
        )
    ))
    .build();

// 旧版函数调用
FunctionCallback.builder()
    .function("getCurrentWeather", new WeatherService())
    .description("获取指定位置的天气情况")
    .inputType(WeatherRequest.class)
    .build()
    
// 新版工具调用
FunctionToolCallback.builder("getCurrentWeather", new WeatherService())
    .description("获取指定位置的天气情况")
    .inputType(WeatherRequest.class)
    .build()

class SmartHomeTools {
    @Tool(description = "控制房间灯光开关")
    public void turnLight(String roomName, boolean on) {
        // 灯光控制逻辑
        log.info("将{}房间的灯光设置为：{}", roomName, on ? "开启" : "关闭");
    }
}

// 注册工具只需一行代码
String response = chatClient.prompt()
    .user("请把客厅的灯打开")
    .tools(new SmartHomeTools())  // 自动识别所有@Tool 注解方法
    .call()
    .content();

// DeepSeek 推理模型示例
public void deepSeekReasonerExample() {
    // 配置推理模型
    DeepSeekChatOptions options = DeepSeekChatOptions.builder()
            .model(DeepSeekApi.ChatModel.DEEPSEEK_REASONER.getValue())
            .temperature(0.1f)  // 低温度确保推理一致性
            .build();
            
    // 创建推理提示
    Prompt prompt = new Prompt("两个数字：9.11 和 9.8，哪个更大？请详细解释", options);
    ChatResponse response = chatModel.call(prompt);

    // 获取完整推理链与最终答案
    DeepSeekAssistantMessage assistantMsg = 
            (DeepSeekAssistantMessage) response.getResult().getOutput();
    
    String reasoning = assistantMsg.getReasoningContent();
    String answer = assistantMsg.getText();
    
    System.out.println("推理过程：" + reasoning);
    System.out.println("最终答案：" + answer);
}

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-deepseek</artifactId>
    <version>1.0.0</version>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-deepseek</artifactId>
    <version>1.0.0</version>
</dependency>

# application.properties
spring.ai.deepseek.api-key=${DEEPSEEK_API_KEY}
spring.ai.deepseek.model=deepseek-chat
spring.ai.deepseek.temperature=0.7

@RestController
public class AiController {
    @Autowired
    private ChatClient chatClient;  // 自动注入配置好的客户端

    @GetMapping("/chat")
    public String chat(@RequestParam String message) {
        return chatClient.prompt().user(message).call().content();
    }
}

@Configuration
public class DeepSeekConfig {
    @Bean
    public ChatModel deepSeekChatModel(@Value("${deepseek.api-key}") String apiKey) {
        // 定制模型参数
        DeepSeekChatOptions options = DeepSeekChatOptions.builder()
                .model(DeepSeekApi.ChatModel.DEEPSEEK_CHAT.getValue())
                .temperature(0.5f)
                .topP(0.95f)
                .build();
        
        return new DeepSeekChatModel(apiKey, options);
    }
    
    @Bean
    public ChatClient deepSeekChatClient(ChatModel deepSeekModel) {
        // 配置完整聊天客户端
        return ChatClient.builder()
            .chatModel(deepSeekModel)
            .defaultSystemPrompt("你是一个专业助手")
            .build();
    }
}

@Service
public class AdvancedDeepSeekService {
    private final DeepSeekApi deepSeekApi;
    
    public AdvancedDeepSeekService(@Value("${deepseek.api-key}") String apiKey) {
        this.deepSeekApi = new DeepSeekApi(apiKey);
    }
    
    // 流式响应处理
    public Flux<String> streamChat(String prompt) {
        DeepSeekChatRequest request = DeepSeekChatRequest.builder()
                .model(DeepSeekApi.ChatModel.DEEPSEEK_CHAT.getValue())
                .messages(List.of(new UserMessage(prompt)))
                .stream(true)
                .temperature(0.3f)
                .build();
        
        // 返回响应式流
        return deepSeekApi.streamingChat(request)
                .filter(chunk -> chunk.getChoices().get(0).getDelta().getContent() != null)
                .map(chunk -> chunk.getChoices().get(0).getDelta().getContent());
    }
}