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perf: model framwork
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180
src/pages/api/chat/chat.ts
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180
src/pages/api/chat/chat.ts
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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/auth';
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import { axiosConfig, openaiChatFilter, systemPromptFilter } from '@/service/utils/tools';
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import { ChatItemType } from '@/types/chat';
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import { jsonRes } from '@/service/response';
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import { PassThrough } from 'stream';
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import { modelList, ModelVectorSearchModeMap, ModelVectorSearchModeEnum } 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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import { searchKb_openai } from '@/service/tools/searchKb';
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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, modelId } = req.body as {
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prompt: ChatItemType;
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modelId: string;
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chatId: '' | string;
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};
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const { authorization } = req.headers;
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if (!modelId || !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 { model, content, userApiKey, systemKey, userId } = await authChat({
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modelId,
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chatId,
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authorization
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});
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const modelConstantsData = modelList.find((item) => item.chatModel === model.chat.chatModel);
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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 = [...content, prompt];
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// 使用了知识库搜索
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if (model.chat.useKb) {
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const { systemPrompts } = await searchKb_openai({
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apiKey: userApiKey || systemKey,
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isPay: !userApiKey,
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text: prompt.value,
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similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22,
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modelId,
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userId
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});
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// filter system prompt
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if (
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systemPrompts.length === 0 &&
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model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity
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) {
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return res.send('对不起,你的问题不在知识库中。');
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}
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/* 高相似度+无上下文,不添加额外知识,仅用系统提示词 */
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if (
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systemPrompts.length === 0 &&
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model.chat.searchMode === ModelVectorSearchModeEnum.noContext
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) {
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prompts.unshift({
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obj: 'SYSTEM',
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value: model.chat.systemPrompt
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});
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} else {
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// 有匹配情况下,system 添加知识库内容。
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// 系统提示词过滤,最多 2500 tokens
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const filterSystemPrompt = systemPromptFilter({
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model: model.chat.chatModel,
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prompts: systemPrompts,
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maxTokens: 2500
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});
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prompts.unshift({
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obj: 'SYSTEM',
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value: `
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${model.chat.systemPrompt}
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${
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model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity
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? `不回答知识库外的内容.`
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: ''
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}
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知识库内容为: ${filterSystemPrompt}'
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`
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});
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}
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} else {
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// 没有用知识库搜索,仅用系统提示词
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if (model.chat.systemPrompt) {
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prompts.unshift({
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obj: 'SYSTEM',
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value: model.chat.systemPrompt
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});
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}
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}
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// 控制总 tokens 数量,防止超出
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const filterPrompts = openaiChatFilter({
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model: model.chat.chatModel,
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prompts,
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maxTokens: modelConstantsData.contextMaxToken - 500
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});
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// 计算温度
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const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
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2
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);
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// console.log(filterPrompts);
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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.chat.chatModel,
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temperature: Number(temperature) || 0,
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messages: filterPrompts,
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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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...axiosConfig()
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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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// 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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chatModel: model.chat.chatModel,
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userId,
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chatId,
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messages: filterPrompts.concat({ role: 'assistant', content: 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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