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之前在《在springboot项目中调用openai API及我遇到的问题》这篇博客中，我实现了在springboot中调用openai接口，但是在这里的返回的信息是一次性全部返回的，如果返回的文字比较多，我们可能需要等很久。 所以需要考虑将请求接口响应方式改为流式响应。
目录
openai api文档
码代码！！！
配置
properties
pom文件
1.请求体类
请求体中的信息类
2.响应类
1）响应体主体类
2）Delta类
常量池类
客户端类
websocket后端配置
1）websocket配置类
2）websocket类
ai消息工具类
页面
看结果
openai api文档
查阅openai的api文档，文档中说我们只需要在请求体中添加"stream":true就可以实现流式响应了。
文档中还说当返回值为data: [DONE]时,标识响应结束。
码代码！！！
跟之前一样，为了缩减篇幅，set、get、构造器都省略
配置
properties
openai.key=你的keyopenai.chatgtp.model=gpt-3.5-turbo
openai.gpt4.model=gpt-4-turbo-preview
openai.chatgtp.api.url=/v1/chat/completions
pom文件
我们在项目中引入websocket和webflux 之前使用的RestTemplate并不擅长处理异步流式的请求。所以我们改用web flux。
<!-- websocket依赖--><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-websocket</artifactId></dependency>
<!-- 流式异步响应客户端--><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-webflux</artifactId></dependency>
请求体类
public class ChatRequest {// 使用的模型private String model;// 历史对话记录private List<ChatMessage> messages;private Boolean stream = Boolean.TRUE;@Overridepublic String toString() {try {return ConstValuePool.OBJECT_MAPPER.writeValueAsString(this);} catch (JsonProcessingException e) {throw new RuntimeException(e);}}
}
请求体中的信息类
public class ChatMessage {// 角色private String role;// 消息内容private String content;
}响应类
响应类先看接口的返回格式的示例吧。下面json中的content就是本次响应数据
{"id": "chatcmpl-8uk7ofAZnSJhsHlsQ9mSYwFInuSFq","object": "chat.completion.chunk","created": 1708534364,"model": "gpt-3.5-turbo-0125","system_fingerprint": "fp_cbdb91ce3f","choices": [{"index": 0,"delta": {"content": "吗"},"logprobs": null,"finish_reason": null}]
}
根据json格式，我们构造响应体类如下
1）响应体主体类
public class ChatResponse {private String id;private String object;private Long created;private String model;private String system_fingerprint;// GPT返回的对话列表private List<Choice> choices;public static class Choice {private int index;private Delta delta;private Object logprobs;private Object finish_reason;}
}
2）Delta类
public class Delta {private String role;private String content;
}常量池类
public class ConstValuePool {// openai代理客户端public static WebClient PROXY_OPENAI_CLIENT = null;
}
客户端类
客户端一样还是在钩子函数中生成。
@Component
public class ApiCodeLoadAware implements EnvironmentAware, ApplicationContextAware {Environment environment;@Overridepublic void setEnvironment(Environment environment) {this.environment = environment;}@Overridepublic void setApplicationContext(ApplicationContext applicationContext) throws BeansException {// chatgpt、gpt4HttpClient httpClient = HttpClient.create().proxy(clientProxy ->clientProxy.type(ProxyProvider.Proxy.HTTP) // 设置代理类型.host("127.0.0.1") // 代理主机.port(7890)); // 代理端口ConstValuePool.PROXY_OPENAI_CLIENT = WebClient.builder().clientConnector(new ReactorClientHttpConnector(httpClient)).baseUrl("https://api.openai.com").defaultHeader("Authorization", "Bearer " + environment.getProperty("openai.key")).build();}
}
websocket后端配置
webscoekt具体可以看我之前的博客使用websocket实现服务端主动发送消息到客户端
1）websocket配置类
@Configuration
public class WebsocketConfig {@Beanpublic ServerEndpointExporter getServerEndpointExporter() {return new ServerEndpointExporter();}}
2）websocket类
这里的参数id是为了区分具体是那个websocket需要推送消息，可以通过登录等方式提供给用户
@Component
@ServerEndpoint("/aiWebsocket/{id}")
public class AiWebsocketService {private final Logger logger = LoggerFactory.getLogger(AiWebsocketService.class);private Session session;//存放所有的websocket连接private static Map<String,AiWebsocketService> aiWebSocketServicesMap = new ConcurrentHashMap<>();//建立websocket连接时自动调用@OnOpenpublic void onOpen(Session session,@PathParam("id") String id){this.session = session;aiWebSocketServicesMap.put(id, this);logger.debug("有新的websocket连接进入，当前连接总数为" + aiWebSocketServicesMap.size());}//关闭websocket连接时自动调用@OnClosepublic void onClose(){aiWebSocketServicesMap.remove(this);logger.debug("连接断开，当前连接总数为" + aiWebSocketServicesMap.size());}//websocket接收到消息时自动调用@OnMessagepublic void onMessage(String message){logger.debug("this：" + message);}//通过websocket发送消息public void sendMessage(String message, String id){AiWebsocketService aiWebsocketService = aiWebSocketServicesMap.get(id);if (aiWebsocketService == null) {return;}try {aiWebsocketService.session.getBasicRemote().sendText(message);} catch (IOException e) {logger.debug(this + "发送消息错误:" + e.getClass() + ":" + e.getMessage());}}}
ai消息工具类
@Component
public class ChatGptModelService implements AiModelService{private static final Logger logger = LoggerFactory.getLogger(ChatGptModelService.class);@Value("${openai.chatgtp.api.url}")private String uri;@Value(("${openai.chatgtp.model}"))private String model;@Resourceprivate AiWebsocketService aiWebsocketService;@Overridepublic String answer(String prompt, HttpServletRequest request) throws InterruptedException {HttpSession session = request.getSession();String identity = AiIdentityFlagUtil.getAiIdentity(request);// 获取历史对话列表，chatMessages实现连续对话、chatDialogues便于页面显示List<ChatMessage> chatMessages = (List<ChatMessage>) session.getAttribute(ConstValuePool.CHAT_MESSAGE_DIALOGUES);List<AiDialogue> chatDialogues = (List<AiDialogue>) session.getAttribute(ConstValuePool.CHAT_DIALOGUES);if (chatMessages == null) {chatMessages = new ArrayList<>();chatMessages.add(ChatMessage.createSystemDialogue("You are a helpful assistant."));chatDialogues = new ArrayList<>();session.setAttribute(ConstValuePool.CHAT_DIALOGUES, chatDialogues);session.setAttribute(ConstValuePool.CHAT_MESSAGE_DIALOGUES, chatMessages);}chatMessages.add(new ChatMessage("user", prompt));chatDialogues.add(AiDialogue.createUserDialogue(prompt));ChatRequest chatRequest = new ChatRequest(this.model, chatMessages);logger.debug("发送的请求为：{}",chatRequest);Flux<String> chatResponseFlux = ConstValuePool.PROXY_OPENAI_CLIENT.post().uri(uri).contentType(MediaType.APPLICATION_JSON).bodyValue(chatRequest.toString()).retrieve().bodyToFlux(String.class);// 得到string返回，便于查看结束标志StringBuilder resultBuilder = new StringBuilder();// 设置同步信号量Semaphore semaphore = new Semaphore(0);chatResponseFlux.subscribe(value -> {logger.debug("返回结果：{}", value);if ("[DONE]".equals(value)) {return;}try {ChatResponse chatResponse = ConstValuePool.OBJECT_MAPPER.readValue(value, ChatResponse.class);List<ChatResponse.Choice> choices = chatResponse.getChoices();ChatResponse.Choice choice = choices.get(choices.size() - 1);Delta delta = choice.getDelta();String res = delta.getContent();if (res != null) {resultBuilder.append(res);aiWebsocketService.sendMessage(resultBuilder.toString(), identity);}} catch (JsonProcessingException e) {throw new AiException("chatgpt运行出错",e);}}, // 获得数据，拼接结果，发送给前端error -> {semaphore.release();throw new AiException("chatpgt执行出错",error);}, // 失败释放信号量，并报错semaphore::release// 成功释放信号量);semaphore.acquire();String resString = resultBuilder.toString();logger.debug(resString);chatDialogues.add(AiDialogue.createAssistantDialogue(resString));chatMessages.add(ChatMessage.createAssistantDialogue(resString));// 对话轮数过多删除最早的历史对话，避免大量消耗tokenswhile (chatMessages.size() > ConstValuePool.CHAT_MAX_MESSAGE) {chatMessages.remove(0);}return "";}
}页面
因为我的前端写的不太好，就不展示前端代码了
看结果
能够实现
