前言
随着AI大模型的快速普及，越来越多的Java开发者希望将AI能力集成到自己的项目中。本文手把手带你用Spring Boot接入DeepSeek API，实现一个具备AI对话能力的后端服务。
一、环境准备
- JDK 17+
- Spring Boot 3.x
- Maven 3.8+
- DeepSeek API Key（到 platform.deepseek.com 免费申请）
二、添加依赖
<dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-webflux</artifactId>
</dependency>
<dependency><groupId>com.fasterxml.jackson.core</groupId><artifactId>jackson-databind</artifactId>
</dependency>三、配置 application.yml
deepseek:api-key: sk-xxxxxxxxxxxxxxxxbase-url: https://api.deepseek.commodel: deepseek-chatmax-tokens: 2048四、创建配置类
@Configuration
@ConfigurationProperties(prefix = "deepseek")
@Data
public class DeepSeekConfig {private String apiKey;private String baseUrl;private String model;private Integer maxTokens;@Beanpublic WebClient deepSeekWebClient() {return WebClient.builder().baseUrl(baseUrl).defaultHeader("Authorization", "Bearer " + apiKey).defaultHeader("Content-Type", "application/json").build();}
}五、定义请求/响应实体
// 请求体
@Data
public class ChatRequest {private String model;private List<Message> messages;@JsonProperty("max_tokens")private Integer maxTokens;private Boolean stream = false;@Data@AllArgsConstructorpublic static class Message {private String role; // system / user / assistantprivate String content;}
}// 响应体
@Data
public class ChatResponse {private String id;private List<Choice> choices;@Datapublic static class Choice {private Message message;}@Datapublic static class Message {private String role;private String content;}
}六、实现Service层
@Service
@RequiredArgsConstructor
public class DeepSeekService {private final WebClient deepSeekWebClient;private final DeepSeekConfig config;public String chat(String userMessage) {ChatRequest request = new ChatRequest();request.setModel(config.getModel());request.setMaxTokens(config.getMaxTokens());request.setMessages(List.of(new ChatRequest.Message("system", "你是一个专业Java开发助手"),new ChatRequest.Message("user", userMessage)));ChatResponse response = deepSeekWebClient.post().uri("/v1/chat/completions").bodyValue(request).retrieve().bodyToMono(ChatResponse.class).block();return response.getChoices().get(0).getMessage().getContent();}
}七、实现Controller层
@RestController
@RequestMapping("/api/ai")
@RequiredArgsConstructor
public class AiController {private final DeepSeekService deepSeekService;@PostMapping("/chat")public ResponseEntity<String> chat(@RequestBody Map<String, String> body) {String message = body.get("message");String reply = deepSeekService.chat(message);return ResponseEntity.ok(reply);}
}八、测试
curl -X POST http://localhost:8080/api/ai/chat \-H "Content-Type: application/json" \-d '{"message": "帮我写一个Spring Boot分页查询的示例"}'总结
通过以上步骤，我们完成了Spring Boot整合DeepSeek API的全流程。后续可以进一步扩展：
- 支持流式输出（SSE）
- 加入对话历史管理
- 集成Redis缓存常用回答
- 接入其他大模型（通义千问、文心一言等）
本文由AI辅助创作。