别再手写 HTTP 客户端调 AI 了！Spring AI 官方出手，一行代码搞定多模型切换

    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
            <version>1.0.0-M5</version>
        </dependency>
    </dependencies>
    <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>org.springframework.ai</groupId>
                <artifactId>spring-ai-bom</artifactId>
                <version>1.0.0-M5</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
        </dependencies>
    </dependencyManagement>

server:
  port: 8080
spring:
  ai:
    openai:
      api-key: sk-xxxxxxxxxxxxxxxxxxxxxxxx # 生产环境建议使用环境变量或配置中心注入
      base-url: https://api.siliconflow.cn
      chat:
        options:
          model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
          temperature: 0.7 # 控制生成随机性的核心参数

@Slf4j
@RestController
@RequestMapping("/ai")
public class DemoController {

    @Resource
    private OpenAiChatModel chatModel;

    @GetMapping("/generate")
    public String generate(@RequestParam(value = "message", defaultValue = "hello") String message) {
        String response = chatModel.call(message);
        log.info("response: {}", response);
        return response;
    }

}

@RestController
public class ChatDeepSeekController {

    private final ChatClient chatClient;

    public ChatDeepSeekController(ChatClient.Builder chatClientBuilder) {
        this.chatClient = chatClientBuilder.build();
    }

    @GetMapping("/chat")
    public String chat(@RequestParam(value = "msg", defaultValue = "给我讲个笑话") String message) {
        return this.chatClient.prompt()
                .user(message)
                .call()
                .content();
    }
}

@Configuration
public class AIConfig {

    @Bean
    public ChatClient chatClient(ChatClient.Builder builder) {
        return builder.defaultSystem("你是一名资深的全栈开发者，精通Java技术栈。你的名字叫zxb。").build();
    }
}

@GetMapping(value = "/chat/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE + ";charset=UTF-8")
public Flux<String> chatStream(@RequestParam(name = "msg", defaultValue = "你好，你是谁？会什么？") String message) {
	return chatClient.prompt().user(message).stream().content();
}

@GetMapping("/openai")
public String openai(@RequestParam("msg") String msg) {
    ChatResponse response = chatModel.call(
            new Prompt(
                    msg,
                    OpenAiChatOptions.builder()
                            .model("deepseek-chat")
                            .temperature(0.8)
                            .build()
            )
    );
    return response.getResult().getOutput().getContent();
}

@GetMapping(value = "/prompt", produces = MediaType.TEXT_EVENT_STREAM_VALUE + ";charset=UTF-8")
public Flux<String> prompt(@RequestParam("name") String name, @RequestParam("voice") String voice) {
    String userText = """
            给我推荐xx的至少三种美食
            """;
    UserMessage userMessage = new UserMessage(userText);

    String systemText = """
            你是一个美食咨询助手，可以帮助人们查询美食信息。
            你的名字是{name}
            你应该用你的名字和{voice}的饮食习惯回复用户的请求。
            """;
    SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemText);
    // 动态替换占位符
    Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", name, "voice", voice));

    Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
    return chatClient.prompt(prompt).stream().content();
}

@Configuration
public class CalculatorService {

    @Bean
    @Description("执行加法运算，输入两个整数并返回它们的和")
    public Function<AddOperation, Integer> addOperation() {
        return request -> request.a + request.b;
    }

    @Bean
    @Description("执行乘法运算，输入两个整数并返回它们的积")
    public Function<MulOperation, Integer> mulOperation() {
        return request -> request.m * request.n;
    }

    // 使用 Record 定义类型安全的数据结构
    public record AddOperation(int a, int b) {}
    public record MulOperation(int m, int n) {}
}

spring:
  ai:
    ollama:
      base-url: http://localhost:11434
      chat:
        options:
          model: deepseek-r1:7b
          temperature: 0.7

@Resource
private OllamaChatModel ollamaChatModel;

@GetMapping("/api/test")
public String generateTest(@RequestParam(value = "message", defaultValue = "hello") String message) {
    String response = this.ollamaChatModel.call(message);
    System.out.println("response: " + response);
    return response;
}