DeepSeek_0">SpringAI集成DeepSeek实战教程
引言
Spring AI作为Spring生态系统中的新成员,为开发者提供了便捷的AI集成方案。本文将详细介绍如何在Spring项目中集成DeepSeek模型,实现智能对话等功能。
环境准备
在开始之前,请确保您的开发环境满足以下要求:
- JDK 17或更高版本
- Spring Boot 3.x
- Maven或Gradle构建工具
- DeepSeek API密钥
项目配置
首先,在pom.xml中添加Spring AI的依赖:
<dependencies><!-- Spring AI 核心依赖 --><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-core</artifactId><version>0.8.0</version></dependency><!-- DeepSeek 集成依赖 --><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-deepseek</artifactId><version>0.8.0</version></dependency>
</dependencies>
基础配置类
创建DeepSeek配置类:
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.ai.deepseek.DeepSeekAiClient;
import org.springframework.ai.deepseek.DeepSeekAiProperties;@Configuration
public class DeepSeekConfig {@Beanpublic DeepSeekAiProperties deepSeekAiProperties() {// 配置DeepSeek属性DeepSeekAiProperties properties = new DeepSeekAiProperties();properties.setApiKey("your-api-key-here");properties.setModel("deepseek-chat"); // 设置使用的模型return properties;}@Beanpublic DeepSeekAiClient deepSeekAiClient(DeepSeekAiProperties properties) {// 创建DeepSeek客户端实例return new DeepSeekAiClient(properties);}
}
服务层实现
创建一个服务类来处理与DeepSeek的交互:
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.stereotype.Service;@Service
public class ChatService {private final DeepSeekAiClient aiClient;public ChatService(DeepSeekAiClient aiClient) {this.aiClient = aiClient;}/*** 发送单轮对话请求* @param message 用户输入的消息* @return AI的响应内容*/public String sendMessage(String message) {// 创建用户消息UserMessage userMessage = new UserMessage(message);// 创建prompt对象Prompt prompt = new Prompt(userMessage);// 获取AI响应ChatResponse response = aiClient.generate(prompt);return response.getGeneration().getContent();}/*** 发送多轮对话请求* @param messages 对话历史记录* @return AI的响应内容*/public String sendConversation(List<String> messages) {List<Message> conversationHistory = new ArrayList<>();// 构建对话历史for (String message : messages) {conversationHistory.add(new UserMessage(message));}// 创建带有历史记录的promptPrompt prompt = new Prompt(conversationHistory);ChatResponse response = aiClient.generate(prompt);return response.getGeneration().getContent();}
}
控制器实现
创建REST API接口:
import org.springframework.web.bind.annotation.*;@RestController
@RequestMapping("/api/chat")
public class ChatController {private final ChatService chatService;public ChatController(ChatService chatService) {this.chatService = chatService;}/*** 处理单条消息请求* @param message 用户消息* @return AI响应*/@PostMapping("/message")public ResponseEntity<String> handleMessage(@RequestBody String message) {try {String response = chatService.sendMessage(message);return ResponseEntity.ok(response);} catch (Exception e) {return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("处理消息时发生错误:" + e.getMessage());}}/*** 处理多轮对话请求* @param messages 对话历史* @return AI响应*/@PostMapping("/conversation")public ResponseEntity<String> handleConversation(@RequestBody List<String> messages) {try {String response = chatService.sendConversation(messages);return ResponseEntity.ok(response);} catch (Exception e) {return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("处理对话时发生错误:" + e.getMessage());}}
}
异常处理
添加全局异常处理:
import org.springframework.web.bind.annotation.ControllerAdvice;
import org.springframework.web.bind.annotation.ExceptionHandler;@ControllerAdvice
public class GlobalExceptionHandler {/*** 处理DeepSeek API相关异常*/@ExceptionHandler(DeepSeekApiException.class)public ResponseEntity<String> handleDeepSeekApiException(DeepSeekApiException e) {// 记录错误日志log.error("DeepSeek API错误", e);return ResponseEntity.status(HttpStatus.SERVICE_UNAVAILABLE).body("AI服务暂时不可用,请稍后重试");}/*** 处理其他未预期的异常*/@ExceptionHandler(Exception.class)public ResponseEntity<String> handleGeneralException(Exception e) {log.error("系统错误", e);return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body("系统发生错误,请联系管理员");}
}
使用示例
以下是一个简单的使用示例:
@SpringBootApplication
public class DeepSeekDemoApplication {@Autowiredprivate ChatService chatService;public void demonstrateChat() {// 发送单条消息String response1 = chatService.sendMessage("你好,请介绍一下自己");System.out.println("AI响应:" + response1);// 发送多轮对话List<String> conversation = Arrays.asList("你好,我想学习Java","请推荐一些好的学习资源","这些资源适合初学者吗?");String response2 = chatService.sendConversation(conversation);System.out.println("AI响应:" + response2);}public static void main(String[] args) {SpringApplication.run(DeepSeekDemoApplication.class, args);}
}
总结
通过本文的介绍,我们详细讲解了如何在Spring项目中集成DeepSeek AI服务。从基础配置到具体实现,再到异常处理,覆盖了实际开发中的主要场景。通过使用Spring AI提供的抽象层,我们可以更加便捷地集成和使用AI能力,而不需要直接处理底层的API调用细节。
需要注意的是,在实际开发中,还需要考虑以下几点:
- API密钥的安全存储
- 请求限流和错误重试
- 响应超时处理
- 模型参数优化
- 成本控制