feat: gpt3流响应

This commit is contained in:
archer
2023-03-25 20:43:03 +08:00
parent 6bba859060
commit 274ece1d91
12 changed files with 163 additions and 76 deletions

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@@ -3,6 +3,8 @@ export enum EmailTypeEnum {
findPassword = 'findPassword'
}
export const PRICE_SCALE = 100000;
export const introPage = `
## 欢迎使用 Fast GPT

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@@ -89,6 +89,8 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
temperature: temperature,
// max_tokens: modelConstantsData.maxToken,
messages: formatPrompts,
frequency_penalty: 0.5, // 越大,重复内容越少
presence_penalty: -0.5, // 越大,越容易出现新内容
stream: true
},
{
@@ -117,7 +119,8 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
try {
const json = JSON.parse(data);
const content: string = json?.choices?.[0].delta.content || '';
if (!content) return;
if (!content || (responseContent === '' && content === '\n')) return;
responseContent += content;
// console.log('content:', content)
!stream.destroyed && stream.push(content.replace(/\n/g, '<br/>'));
@@ -144,7 +147,6 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
stream.destroy();
const promptsContent = formatPrompts.map((item) => item.content).join('');
console.log(`responseLen: ${responseContent.length}`, `promptLen: ${promptsContent.length}`);
// 只有使用平台的 key 才计费
!userApiKey &&
pushBill({

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@@ -1,20 +1,38 @@
// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { createParser, ParsedEvent, ReconnectInterval } from 'eventsource-parser';
import { connectToDatabase } from '@/service/mongo';
import { getOpenAIApi, authChat } from '@/service/utils/chat';
import { ChatItemType } from '@/types/chat';
import { httpsAgent } from '@/service/utils/tools';
import { ChatItemType } from '@/types/chat';
import { jsonRes } from '@/service/response';
import type { ModelSchema } from '@/types/mongoSchema';
import { PassThrough } from 'stream';
import { modelList } from '@/constants/model';
import { pushBill } from '@/service/events/pushChatBill';
/* 发送提示词 */
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
try {
const { prompt, chatId } = req.body as { prompt: ChatItemType[]; chatId: string };
const { authorization } = req.headers;
let step = 0; // step=1时表示开始了流响应
const stream = new PassThrough();
stream.on('error', () => {
console.log('error: ', 'stream error');
stream.destroy();
});
res.on('close', () => {
stream.destroy();
});
res.on('error', () => {
console.log('error: ', 'request error');
stream.destroy();
});
if (!prompt || !chatId) {
try {
const { chatId, prompt } = req.body as {
prompt: ChatItemType;
chatId: string;
};
const { authorization } = req.headers;
if (!chatId || !prompt) {
throw new Error('缺少参数');
}
@@ -22,13 +40,29 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
const { chat, userApiKey, systemKey, userId } = await authChat(chatId, authorization);
const model = chat.modelId;
const model: ModelSchema = chat.modelId;
// 获取 chatAPI
const chatAPI = getOpenAIApi(userApiKey || systemKey);
// 读取对话内容
const prompts = [...chat.content, prompt];
// prompt处理
const formatPrompts = prompt.map((item) => `${item.value}\n\n###\n\n`).join('');
// 上下文长度过滤
const maxContext = model.security.contextMaxLen;
const filterPrompts =
prompts.length > maxContext ? prompts.slice(prompts.length - maxContext) : prompts;
// 格式化文本内容
const map = {
Human: 'Human',
AI: 'AI',
SYSTEM: 'SYSTEM'
};
const formatPrompts: string[] = filterPrompts.map((item: ChatItemType) => item.value);
// 如果有系统提示词,自动插入
if (model.systemPrompt) {
formatPrompts.unshift(`${model.systemPrompt}`);
}
const promptText = formatPrompts.join('</s>');
// 计算温度
const modelConstantsData = modelList.find((item) => item.model === model.service.modelName);
@@ -37,42 +71,95 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
}
const temperature = modelConstantsData.maxTemperature * (model.temperature / 10);
// 发送请求
const response = await chatAPI.createCompletion(
// 获取 chatAPI
const chatAPI = getOpenAIApi(userApiKey || systemKey);
let startTime = Date.now();
// 发出请求
const chatResponse = await chatAPI.createCompletion(
{
model: model.service.modelName,
prompt: formatPrompts,
model: model.service.chatModel,
temperature: temperature,
// max_tokens: modelConstantsData.maxToken,
top_p: 1,
frequency_penalty: 0,
presence_penalty: 0.6,
stop: ['###']
prompt: promptText,
stream: true,
max_tokens: modelConstantsData.maxToken,
presence_penalty: 0, // 越大,越容易出现新内容
frequency_penalty: 0, // 越大,重复内容越少
stop: ['。!?.!.', `</s>`]
},
{
timeout: 40000,
responseType: 'stream',
httpsAgent
}
);
const responseContent = response.data.choices[0]?.text || '';
console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
// 创建响应流
res.setHeader('Content-Type', 'text/event-stream;charset-utf-8');
res.setHeader('Access-Control-Allow-Origin', '*');
res.setHeader('X-Accel-Buffering', 'no');
res.setHeader('Cache-Control', 'no-cache, no-transform');
step = 1;
let responseContent = '';
stream.pipe(res);
const onParse = async (event: ParsedEvent | ReconnectInterval) => {
if (event.type !== 'event') return;
const data = event.data;
if (data === '[DONE]') return;
try {
const json = JSON.parse(data);
const content: string = json?.choices?.[0].text || '';
if (!content || (responseContent === '' && content === '\n')) return;
responseContent += content;
// console.log('content:', content);
!stream.destroyed && stream.push(content.replace(/\n/g, '<br/>'));
} catch (error) {
error;
}
};
const decoder = new TextDecoder();
try {
for await (const chunk of chatResponse.data as any) {
if (stream.destroyed) {
// 流被中断了,直接忽略后面的内容
break;
}
const parser = createParser(onParse);
parser.feed(decoder.decode(chunk));
}
} catch (error) {
console.log('pipe error', error);
}
// close stream
!stream.destroyed && stream.push(null);
stream.destroy();
console.log(`responseLen: ${responseContent.length}`, `promptLen: ${formatPrompts.length}`);
// 只有使用平台的 key 才计费
!userApiKey &&
pushBill({
modelName: model.service.modelName,
userId,
chatId,
text: formatPrompts + responseContent
});
jsonRes(res, {
data: responseContent
text: promptText + responseContent
});
} catch (err: any) {
// console.log(err?.response);
if (step === 1) {
// 直接结束流
console.log('error结束');
stream.destroy();
} else {
res.status(500);
jsonRes(res, {
code: 500,
error: err
});
}
}
}

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@@ -5,7 +5,7 @@ import axios from 'axios';
import { authToken } from '@/service/utils/tools';
import { customAlphabet } from 'nanoid';
import { connectToDatabase, Pay } from '@/service/mongo';
import { PRICE_SCALE } from '@/utils/user';
import { PRICE_SCALE } from '@/constants/common';
const nanoid = customAlphabet('abcdefghijklmnopqrstuvwxyz1234567890', 20);

View File

@@ -197,16 +197,22 @@ const Chat = ({ chatId }: { chatId: string }) => {
[chatId]
);
// chatGPT
const chatGPTPrompt = useCallback(
async (newChatList: ChatSiteItemType[]) => {
// gpt 对话
const gptChatPrompt = useCallback(
async (prompts: ChatSiteItemType) => {
const urlMap: Record<string, string> = {
[ChatModelNameEnum.GPT35]: '/api/chat/chatGpt',
[ChatModelNameEnum.GPT3]: '/api/chat/gpt3'
};
if (!urlMap[chatData.chatModel]) return Promise.reject('找不到模型');
const prompt = {
obj: newChatList[newChatList.length - 1].obj,
value: newChatList[newChatList.length - 1].value
obj: prompts.obj,
value: prompts.value
};
// 流请求,获取数据
const res = await streamFetch({
url: '/api/chat/chatGpt',
url: urlMap[chatData.chatModel],
data: {
prompt,
chatId
@@ -240,7 +246,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
});
} catch (err) {
toast({
title: '存储对话出现异常, 继续对话会导致上下文丢失,请刷新页面',
title: '对话出现异常, 继续对话会导致上下文丢失,请刷新页面',
status: 'warning',
duration: 3000,
isClosable: true
@@ -259,7 +265,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
})
}));
},
[chatId, toast]
[chatData.chatModel, chatId, toast]
);
/**
@@ -272,7 +278,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
.trim()
.split('\n')
.filter((val) => val)
.join('\n\n');
.join('\n');
if (!chatData?.modelId || !val || !ChatBox.current || isChatting) {
return;
}
@@ -301,22 +307,8 @@ const Chat = ({ chatId }: { chatId: string }) => {
resetInputVal('');
scrollToBottom();
const fnMap: { [key: string]: any } = {
[ChatModelNameEnum.GPT35]: chatGPTPrompt,
[ChatModelNameEnum.GPT3]: gpt3ChatPrompt
};
try {
/* 对长度进行限制 */
const maxContext = chatData.secret.contextMaxLen;
const requestPrompt =
newChatList.length > maxContext + 1
? newChatList.slice(newChatList.length - maxContext - 1, -1)
: newChatList.slice(0, -1);
if (typeof fnMap[chatData.chatModel] === 'function') {
await fnMap[chatData.chatModel](requestPrompt);
}
await gptChatPrompt(newChatList[newChatList.length - 2]);
// 如果是 Human 第一次发送,插入历史记录
const humanChat = newChatList.filter((item) => item.obj === 'Human');
@@ -343,15 +335,12 @@ const Chat = ({ chatId }: { chatId: string }) => {
}
}, [
inputVal,
chatData.modelId,
chatData?.modelId,
chatData.history,
chatData.secret.contextMaxLen,
chatData.chatModel,
isChatting,
resetInputVal,
scrollToBottom,
chatGPTPrompt,
gpt3ChatPrompt,
gptChatPrompt,
pushChatHistory,
chatId,
toast

View File

@@ -34,6 +34,7 @@ const CreateModel = ({
onSuccess: Dispatch<ModelSchema>;
}) => {
const [requesting, setRequesting] = useState(false);
const [refresh, setRefresh] = useState(false);
const toast = useToast({
duration: 2000,
position: 'top'
@@ -95,7 +96,10 @@ const CreateModel = ({
<Select
placeholder="选择基础模型类型"
{...register('serviceModelName', {
required: '底层模型不能为空'
required: '底层模型不能为空',
onChange() {
setRefresh(!refresh);
}
})}
>
{modelList.map((item) => (
@@ -110,8 +114,9 @@ const CreateModel = ({
</FormControl>
<Box mt={3} textAlign={'center'} fontSize={'sm'} color={'blackAlpha.600'}>
{formatPrice(
modelList.find((item) => item.model === getValues('serviceModelName'))?.price || 0
) * 1000}
modelList.find((item) => item.model === getValues('serviceModelName'))?.price || 0,
1000
)}
/1K tokens()
</Box>
</ModalBody>

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@@ -107,7 +107,7 @@ const PayModal = ({ onClose }: { onClose: () => void }) => {
{modelList.map((item, i) => (
<Tr key={item.model}>
<Td>{item.name}</Td>
<Td>{formatPrice(item.price) * 1000}</Td>
<Td>{formatPrice(item.price, 1000)}</Td>
</Tr>
))}
</Tbody>

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@@ -28,7 +28,9 @@ export const pushBill = async ({
// 计算价格
const price = unitPrice * tokens.length;
console.log('token len:', tokens.length, 'price: ', `${formatPrice(price)}`);
console.log('token len:', tokens.length);
console.log('text len: ', text.length);
console.log('price: ', `${formatPrice(price)}`);
try {
// 插入 Bill 记录

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@@ -39,9 +39,9 @@ const ModelSchema = new Schema({
},
temperature: {
type: Number,
min: 1,
min: 0,
max: 10,
default: 5
default: 4
},
service: {
company: {

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@@ -1,6 +1,6 @@
import { Schema, model, models } from 'mongoose';
import { hashPassword } from '@/service/utils/tools';
import { PRICE_SCALE } from '@/utils/user';
import { PRICE_SCALE } from '@/constants/common';
const UserSchema = new Schema({
email: {

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@@ -28,11 +28,11 @@ export const jsonRes = <T = any>(
} else if (openaiError[error?.response?.statusText]) {
msg = openaiError[error.response.statusText];
}
// console.log(error?.response);
console.log('error->');
console.log('code:', error.code);
console.log('statusText:', error?.response?.statusText);
console.log('msg:', msg);
error?.response && console.log('chat err:', error?.response);
}
res.json({

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@@ -1,5 +1,5 @@
import { PRICE_SCALE } from '@/constants/common';
const tokenKey = 'fast-gpt-token';
export const PRICE_SCALE = 100000;
export const setToken = (val: string) => {
localStorage.setItem(tokenKey, val);
@@ -14,6 +14,6 @@ export const clearToken = () => {
/**
* 把数据库读取到的price转化成元
*/
export const formatPrice = (val: number) => {
return val / PRICE_SCALE;
export const formatPrice = (val: number, multiple = 1) => {
return Number(((val / PRICE_SCALE) * multiple).toFixed(10));
};