从事网站类网站建设的,网站制作自己做服务器,asp.net网站开发简明教程,百度注册入口之前在《在springboot项目中调用openai API及我遇到的问题》这篇博客中#xff0c;我实现了在springboot中调用openai接口#xff0c;但是在这里的返回的信息是一次性全部返回的#xff0c;如果返回的文字比较多#xff0c;我们可能需要等很久。 所以需要考虑将请求接口响应… 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
2Delta类
常量池类
客户端类
websocket后端配置
1websocket配置类
2websocket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.modelgpt-3.5-turbo
openai.gpt4.modelgpt-4-turbo-preview
openai.chatgtp.api.url/v1/chat/completionspom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--dependencygroupIdorg.springframework.boot/groupIdartifactIdspring-boot-starter-websocket/artifactId/dependency
!-- 流式异步响应客户端--dependencygroupIdorg.springframework.boot/groupIdartifactIdspring-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 ListChatMessage 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 ListChoice choices;public static class Choice {private int index;private Delta delta;private Object logprobs;private Object finish_reason;}
}2Delta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
1websocket配置类
Configuration
public class WebsocketConfig {Beanpublic ServerEndpointExporter getServerEndpointExporter() {return new ServerEndpointExporter();}}2websocket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 MapString,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ListChatMessage chatMessages (ListChatMessage) session.getAttribute(ConstValuePool.CHAT_MESSAGE_DIALOGUES);ListAiDialogue chatDialogues (ListAiDialogue) 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);FluxString 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);ListChatResponse.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
