<properties>
    <java.version>17</java.version>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
    <spring-boot.version>3.2.0</spring-boot.version>
</properties>
<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-deepseek</artifactId>
        <version>1.0.0</version>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-test</artifactId>
        <scope>test</scope>
    </dependency>
</dependencies>
<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-dependencies</artifactId>
            <version>${spring-boot.version}</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

server:
  port: 8081
spring:
  application:
  ai:
    deepseek: ## 这一行是你选择的LLM模型，如果是openai，这里就填openai， base-url就是填对应厂商的地址
      api-key: "sk-199324596dbb4308afcb77d46GGGGGGG"
      base-url: "https://api.deepseek.com"
      chat:
        options:
          model: deepseek-chat
      embedding:
        enabled: false
注:api-key  请用自己的key

import jakarta.servlet.http.HttpServletResponse;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.PathVariable;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.ResponseBody;
import reactor.core.publisher.Flux;
@RequestMapping("/openai")
@ResponseBody
@Controller
public class DeepSeekChatModelController {
    private final ChatModel deepSeekChatModel;
    // 主要就是这个地方，springboot已经把yml里的配置，生成好一个叫ChatModel的bean，注入进来controller里就可以直接使用了
    public DeepSeekChatModelController(ChatModel chatModel) {
        this.deepSeekChatModel = chatModel;
    }
    // 这个是同步等待LLM的结果，再回复给前端。
    @GetMapping("/simple/chat/{prompt}")
    public String simpleChat (@PathVariable(value = "prompt") String prompt) {
        return deepSeekChatModel.call(new Prompt(prompt)).
                getResult().getOutput().getText();
    }
    /**
     * Stream 流式调用。可以使大模型的输出信息实现打字机效果。
     * 这个就是sse方式回复内容给前端，就不用等所有的内容都收到才给前端
     * @return Flux<String> types.
     */
    @GetMapping("/stream/chat/{prompt}")
    public Flux<String> streamChat (
            @PathVariable(value = "prompt") String prompt,
            HttpServletResponse response) {
        response.setCharacterEncoding("UTF-8");
        Flux<ChatResponse> stream = deepSeekChatModel.stream(new Prompt(prompt));
        return stream.map(resp -> resp.getResult().getOutput().getText());
    }
}

@GetMapping("/simple/chat2")
public String simpleChat2 () {
    Prompt prompt=new Prompt("你好你是谁");
    ChatResponse response=deepSeekChatModel.call(prompt);
    // 获取响应内容
    DeepSeekAssistantMessage deepSeekAssistantMessage = (DeepSeekAssistantMessage) response.getResult().getOutput();
    return deepSeekAssistantMessage.getText();
}

4、浏览器
http://localhost:8081/openai/stream/chat/%E6%9E%97%E8%8A%9D%E5%A4%A9%E6%B0%94

 http://localhost:8081/simple/stream/chat/%E6%9E%97%E8%8A%9D%E5%A4%A9%E6%B0%94