134 lines
4.0 KiB
TypeScript
134 lines
4.0 KiB
TypeScript
import type { NextApiRequest, NextApiResponse } from 'next';
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import { connectToDatabase } from '@/service/mongo';
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import { getOpenAIApi, authChat } from '@/service/utils/chat';
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import { httpsAgent, openaiChatFilter } from '@/service/utils/tools';
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import { ChatCompletionRequestMessage, ChatCompletionRequestMessageRoleEnum } from 'openai';
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import { ChatItemType } from '@/types/chat';
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import { jsonRes } from '@/service/response';
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import type { ModelSchema } from '@/types/mongoSchema';
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import { PassThrough } from 'stream';
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import { modelList } from '@/constants/model';
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import { pushChatBill } from '@/service/events/pushBill';
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import { gpt35StreamResponse } from '@/service/utils/openai';
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/* 发送提示词 */
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export default async function handler(req: NextApiRequest, res: NextApiResponse) {
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let step = 0; // step=1时，表示开始了流响应
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const stream = new PassThrough();
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stream.on('error', () => {
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console.log('error: ', 'stream error');
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stream.destroy();
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});
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res.on('close', () => {
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stream.destroy();
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});
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res.on('error', () => {
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console.log('error: ', 'request error');
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stream.destroy();
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});
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try {
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const { chatId, prompt } = req.body as {
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prompt: ChatItemType;
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chatId: string;
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};
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const { authorization } = req.headers;
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if (!chatId || !prompt) {
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throw new Error('缺少参数');
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}
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await connectToDatabase();
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let startTime = Date.now();
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const { chat, userApiKey, systemKey, userId } = await authChat(chatId, authorization);
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const model: ModelSchema = chat.modelId;
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const modelConstantsData = modelList.find((item) => item.model === model.service.modelName);
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if (!modelConstantsData) {
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throw new Error('模型加载异常');
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}
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// 读取对话内容
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const prompts = [...chat.content, prompt];
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// 如果有系统提示词，自动插入
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if (model.systemPrompt) {
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prompts.unshift({
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obj: 'SYSTEM',
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value: model.systemPrompt
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});
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}
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// 控制在 tokens 数量，防止超出
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// const filterPrompts = openaiChatFilter(prompts, modelConstantsData.contextMaxToken);
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// 格式化文本内容成 chatgpt 格式
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const map = {
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Human: ChatCompletionRequestMessageRoleEnum.User,
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AI: ChatCompletionRequestMessageRoleEnum.Assistant,
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SYSTEM: ChatCompletionRequestMessageRoleEnum.System
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};
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const formatPrompts: ChatCompletionRequestMessage[] = prompts.map((item: ChatItemType) => ({
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role: map[item.obj],
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content: item.value
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}));
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// console.log(formatPrompts);
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// 计算温度
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const temperature = modelConstantsData.maxTemperature * (model.temperature / 10);
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// 获取 chatAPI
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const chatAPI = getOpenAIApi(userApiKey || systemKey);
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// 发出请求
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const chatResponse = await chatAPI.createChatCompletion(
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{
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model: model.service.chatModel,
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temperature: temperature,
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// max_tokens: modelConstantsData.maxToken,
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messages: formatPrompts,
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frequency_penalty: 0.5, // 越大，重复内容越少
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presence_penalty: -0.5, // 越大，越容易出现新内容
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stream: true,
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stop: ['.!?。']
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},
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{
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timeout: 40000,
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responseType: 'stream',
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httpsAgent: httpsAgent(!userApiKey)
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}
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);
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console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
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step = 1;
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const { responseContent } = await gpt35StreamResponse({
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res,
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stream,
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chatResponse
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});
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const promptsContent = formatPrompts.map((item) => item.content).join('');
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// 只有使用平台的 key 才计费
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pushChatBill({
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isPay: !userApiKey,
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modelName: model.service.modelName,
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userId,
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chatId,
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text: promptsContent + responseContent
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});
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} catch (err: any) {
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if (step === 1) {
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// 直接结束流
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console.log('error，结束');
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stream.destroy();
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} else {
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res.status(500);
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jsonRes(res, {
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code: 500,
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error: err
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});
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}
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}
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}
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