chatGPT是一款高效强大的语言模型,能够给我们的生活带来极大的改变。无论是学习知识还是工作效率,chatGPT都能为我们提供有力的帮助。它可以帮助我们快速获取所需的知识,同时可以帮助我们提高工作效率,包括写文章、文案、推荐策略、生成代码、写周报,流程图等等。此外,它还可以成为您智能的助手,帮您打理日常事务,如一键预约、贴心提醒等。对于小朋友们来说,他还可以为他们写作文。总之,chatGPT是一个多功能的智能管家,不管您的需求是什么,它都能为您提供强大的支持。欢迎有需要的朋友戳链接体验:Talk-Bot,不喜勿喷,广交益友
废话不多说,直接上代码
这种方式比较常用,我们这里引入github上PlexPt大神封装好的类直接引用即可,地址为:chatgpt-java,也可以自己封装哈
<dependency>
<groupId>com.github.plexpt</groupId>
<artifactId>chatgpt</artifactId>
<version>4.0.7</version>
</dependency>
private static final String OPENAI_API_HOST = "https://api.openai.com/";
@PostMapping(value = "/v1/stream")
public SseEmitter streamEvents(@RequestBody ChatRequest chatRequest) {
SseEmitter sseEmitter = new SseEmitter(-1L);
// 不需要代理的话,注销此行
Proxy proxy = Proxys.http("192.168.1.98", 7890);
ChatGPTStream chatGPTStream = ChatGPTStream.builder()
.timeout(600)
.apiKey("你的openApiKey")
.proxy(proxy)
.apiHost(OPENAI_API_HOST)
.build()
.init();
SseStreamListener listener = new SseStreamListener(sseEmitter);
Message message = Message.of(chatRequest.getInput());
ChatCompletion chatCompletion = ChatCompletion.builder()
.model(ChatCompletion.Model.GPT_3_5_TURBO.getName())
.messages(Arrays.asList(message))
.build();
chatGPTStream.streamChatCompletion(chatCompletion, listener);
listener.setOnComplate(msg -> {
//回答完成,可以做一些事情
sseEmitter.complete();
});
return sseEmitter;
}
前端调用,这里使用fetchEventSource,普通的eventSource不能发送post参数
import { fetchEventSource } from '@microsoft/fetch-event-source';
const reqData = {
id: '111',
input: 'java编码实现快速排序算法',
chatlog: [],
};
const headers = {
'Content-Type': 'application/json',
};
const eventSource = new fetchEventSource('/api/v1/stream', {
method: 'POST',
headers: headers,
body: JSON.stringify(reqData),
onopen(response) {
console.info('eventSource open: ', response);
},
onmessage(event) {
console.log('eventSource msg: ', event.data);
},
onerror(err) {
console.log('eventSource error: ' + err);
},
onclose() {
console.log('eventSource close');
}
});
Message、ChatCompletion、ChatCompletionResponse 类都是根据官方需要的参数封装的实体,这里暂不能提供了,主要看思路吧
<dependency>
<groupId>cn.hutool</groupId>
<artifactId>hutool-all</artifactId>
<version>5.8.22</version>
</dependency>
private static final String OPENAI_API_HOST = "https://api.openai.com/";
private static final Map<String, Integer> API_KEY_MAP = new LinkedHashMap<String, Integer>() {
{
put("你的openApiKey", 5);
put("你的openApiKey", 5);
}
};
@PostMapping("/v1/stream")
public void streamHandler(@RequestBody ChatRequest chatRequest, HttpServletResponse response) throws Exception {
String input = chatRequest.getInput();
//按权重分配key
List<String> weightList = new ArrayList<>(API_KEY_MAP.entrySet().size());
for (Map.Entry<String, Integer> entry : API_KEY_MAP.entrySet()) {
String element = entry.getKey();
Integer weight = entry.getValue();
for (int i = 0; i < weight; i++) {
weightList.add(element);
}
}
// 不需要代理的话,注销此行
proxy = new Proxy(Proxy.Type.HTTP, new InetSocketAddress("192.168.1.98", 7890));
Message message = Message.builder().role(Message.Role.USER).content(input).build();
ChatCompletion chatCompletion = ChatCompletion.builder().messages(Arrays.asList(message)).stream(true).build();
String requestBody = JSONUtil.toJsonStr(chatCompletion);
HttpRequest client = HttpRequest.post(OPENAI_API_HOST + "v1/chat/completions")
.contentType(ContentType.JSON.getValue())
.bearerAuth(RandomUtil.randomEle(weightList))
.keepAlive(true)
.setChunkedStreamingMode(Constants.BUFFER_SIZE)
.setProxy(proxy)
.timeout(300000)
.body(requestBody);
BufferedReader reader = new BufferedReader(new InputStreamReader(client.executeAsync().bodyStream()));
String line;
try {
while ((line = reader.readLine()) != null) {
line = StrUtil.replace(line, "data: ", "");
if (StrUtil.isEmpty(line)) {
continue;
}
if (!StrUtil.equals("[DONE]", line)) {
ChatCompletionResponse chatCompletionResponse;
try {
// 官方错误返回不是一个json格式的,这里兼容下
chatCompletionResponse = JSONUtil.toBean(line, ChatCompletionResponse.class);
} catch (Exception e) {
// 自己打印日志
continue;
}
if (Objects.isNull(chatCompletionResponse) || Objects.isNull(chatCompletionResponse.getChoices()) || chatCompletionResponse.getChoices().isEmpty()) {
continue;
}
if (!StrUtil.equals("stop", chatCompletionResponse.getChoices().get(0).getFinishReason())) {
String content = chatCompletionResponse.getChoices().get(0).getDelta().getContent();
if (StrUtil.isEmpty(content)) {
continue;
}
response.getWriter().write(content);
response.getWriter().flush();
}
}
}
} catch (Exception e) {
// 自己打印日志,line = reader.readLine()这行代码读取会出现超时的情况,所以加了个try catch
}
reader.close();
response.getWriter().close();
}
nginx配置,这三个必须加上
proxy_buffering off;
proxy_http_version 1.1;
chunked_transfer_encoding on;
前端调用,这里使用axios,比较简单
import axios from 'axios';
const reqData = {
id: '111',
input: 'java编码实现快速排序算法',
chatlog: [],
};
const headers = {
'Content-Type': 'application/json',
};
axios.post('/api/v1/stream', reqData , { headers }).then(
function (response) {
console.log(response);
}).catch(function (error) {
console.log(error);
});
这种方式实现起来稍微复杂些,跟SseEmitter实现方式差别不大,感兴趣的可以用chatGPT生成一下,哈哈哈,链接戳:Talk-Bot(请各位大佬手下留情啊!!!!)