Spring项目接入DeepSeek的两种超简单的方式分享

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
    <version>0.8.1</version>
</dependency>

spring:
  ai:
    openai:
      base-url: https://api.deepseek.com/v1  # DeepSeek的OpenAI式端点
      api-key: sk-your-deepseek-key-here
      chat.options:
        model: deepseek-chat  # 指定DeepSeek的模型名称

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

	private final ChatClient chatClient;

 // 构造方法注入 ChatClient.Builder，用于构建 ChatClient 实例
	public ChatController(ChatClient.Builder chatClientBuilder) {
		this.chatClient = chatClientBuilder.build();
	}

	@GetMapping("/chat")
	public String generate(@RequestParam(value = "message") String message) {
		log.info("Generating response");
  // 调用 ChatClient 的 prompt 方法生成响应
  // 1. prompt(message): 创建一个包含用户输入消息的 Prompt 对象
  // 2. call(): 调用 ChatClient 与 AI 模型交互以获取响应
  // 3. content(): 提取响应的内容部分
		return chatClient.prompt(message).call().content();
	}

}

ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        OpenAiChatOptions.builder()
            .withModel("deepseek-chat")
            .withTemperature(0.4)
        .build()
    ));

ollama pull deepseek-r1:1.5b
ollama list deepseek

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
    <version>0.8.1</version>
</dependency>

spring:
  ai:
    ollama:
      base-url: http://localhost:11434
      chat:
        model: deepseek-r1:1.5b  # 与本地模型名称对应

@RestController
@RequestMapping("/ai")
public class ChatController {
    private final ChatClient chatClient;

    // 构造方法注入 ChatClient.Builder，用于构建 ChatClient 实例
    public ChatController(ChatClient.Builder chatClient) {
        this.chatClient = chatClient.build();
    }

    	@GetMapping("/chat")
    public ResponseEntity<Flux<String>> chat(@RequestParam(value = "message") String message) {
        try {
            // 调用 ChatClient 生成响应，并以 Flux<String>（响应流）形式返回
            Flux<String> response = chatClient.prompt(message).stream().content();
            return ResponseEntity.ok(response);
        } catch (Exception e) {
            return ResponseEntity.badRequest().build();
        }
    }
}