mirror of
https://github.com/labring/FastGPT.git
synced 2025-07-24 13:53:50 +00:00
feat: 知识库对外api
This commit is contained in:
@@ -31,13 +31,13 @@ const navbarList = [
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icon: 'user',
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link: '/number/setting',
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activeLink: ['/number/setting']
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},
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{
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label: '开发',
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icon: 'develop',
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link: '/openapi',
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activeLink: ['/openapi']
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}
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// {
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// label: '开发',
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// icon: 'develop',
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// link: '/openapi',
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// activeLink: ['/openapi']
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// }
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];
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const Layout = ({ children }: { children: JSX.Element }) => {
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@@ -82,14 +82,14 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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vectorToBuffer(promptVector),
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'LIMIT',
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'0',
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'20',
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'30',
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'DIALECT',
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'2'
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]);
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const formatRedisPrompt: string[] = [];
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// 格式化响应值,获取 qa
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for (let i = 2; i < 42; i += 2) {
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for (let i = 2; i < 61; i += 2) {
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const text = redisData[i]?.[1];
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if (text) {
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formatRedisPrompt.push(text);
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@@ -126,7 +126,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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// 获取提示词的向量
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const { vector: promptVector } = await openaiCreateEmbedding({
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isPay: true,
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apiKey: apiKey,
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apiKey,
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userId,
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text: prompt.value
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});
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210
src/pages/api/openapi/chat/vectorGpt.ts
Normal file
210
src/pages/api/openapi/chat/vectorGpt.ts
Normal file
@@ -0,0 +1,210 @@
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import type { NextApiRequest, NextApiResponse } from 'next';
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import { connectToDatabase, Model } from '@/service/mongo';
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import {
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httpsAgent,
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openaiChatFilter,
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systemPromptFilter,
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authOpenApiKey
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} 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 { 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 { connectRedis } from '@/service/redis';
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import { VecModelDataPrefix } from '@/constants/redis';
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import { vectorToBuffer } from '@/utils/tools';
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import { openaiCreateEmbedding, 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 {
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prompts,
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modelId,
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isStream = true
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} = req.body as {
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prompts: ChatItemType[];
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modelId: string;
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isStream: boolean;
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};
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if (!prompts || !modelId) {
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throw new Error('缺少参数');
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}
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if (!Array.isArray(prompts)) {
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throw new Error('prompts is not array');
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}
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if (prompts.length > 30 || prompts.length === 0) {
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throw new Error('prompts length range 1-30');
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}
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await connectToDatabase();
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const redis = await connectRedis();
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let startTime = Date.now();
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/* 凭证校验 */
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const { apiKey, userId } = await authOpenApiKey(req);
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const model = await Model.findOne({
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_id: modelId,
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userId
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});
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if (!model) {
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throw new Error('无权使用该模型');
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}
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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('模型初始化异常');
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}
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// 获取提示词的向量
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const { vector: promptVector, chatAPI } = await openaiCreateEmbedding({
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isPay: true,
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apiKey,
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userId,
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text: prompts[prompts.length - 1].value // 取最后一个
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});
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// 搜索系统提示词, 按相似度从 redis 中搜出相关的 q 和 text
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const redisData: any[] = await redis.sendCommand([
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'FT.SEARCH',
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`idx:${VecModelDataPrefix}:hash`,
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`@modelId:{${modelId}} @vector:[VECTOR_RANGE 0.24 $blob]=>{$YIELD_DISTANCE_AS: score}`,
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'RETURN',
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'1',
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'text',
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'SORTBY',
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'score',
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'PARAMS',
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'2',
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'blob',
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vectorToBuffer(promptVector),
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'LIMIT',
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'0',
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'30',
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'DIALECT',
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'2'
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]);
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const formatRedisPrompt: string[] = [];
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// 格式化响应值,获取 qa
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for (let i = 2; i < 61; i += 2) {
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const text = redisData[i]?.[1];
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if (text) {
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formatRedisPrompt.push(text);
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}
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}
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if (formatRedisPrompt.length === 0) {
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throw new Error('对不起,我没有找到你的问题');
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}
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// system 合并
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if (prompts[0].obj === 'SYSTEM') {
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formatRedisPrompt.unshift(prompts.shift()?.value || '');
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}
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// textArr 筛选,最多 2800 tokens
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const systemPrompt = systemPromptFilter(formatRedisPrompt, 2800);
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prompts.unshift({
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obj: 'SYSTEM',
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value: `${model.systemPrompt} 知识库内容是最新的,知识库内容为: "${systemPrompt}"`
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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[] = filterPrompts.map(
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(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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);
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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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// 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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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: isStream
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},
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{
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timeout: 120000,
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responseType: isStream ? 'stream' : 'json',
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httpsAgent
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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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let responseContent = '';
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if (isStream) {
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const streamResponse = 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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responseContent = streamResponse.responseContent;
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} else {
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responseContent = chatResponse.data.choices?.[0]?.message?.content || '';
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jsonRes(res, {
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data: responseContent
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});
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}
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const promptsContent = formatPrompts.map((item) => item.content).join('');
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pushChatBill({
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isPay: true,
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modelName: model.service.modelName,
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userId,
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text: promptsContent + responseContent
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});
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// jsonRes(res);
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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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@@ -300,10 +300,9 @@ const Chat = ({ chatId }: { chatId: string }) => {
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// 复制内容
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const onclickCopy = useCallback(
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(chatId: string) => {
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const dom = document.getElementById(chatId);
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const innerText = dom?.innerText;
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innerText && copyData(innerText);
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(value: string) => {
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const val = value.replace(/\n+/g, '\n');
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copyData(val);
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},
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[copyData]
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);
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@@ -434,7 +433,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
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/>
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</MenuButton>
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<MenuList fontSize={'sm'}>
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<MenuItem onClick={() => onclickCopy(`chat${index}`)}>复制</MenuItem>
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<MenuItem onClick={() => onclickCopy(item.value)}>复制</MenuItem>
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<MenuItem onClick={() => delChatRecord(index)}>删除该行</MenuItem>
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</MenuList>
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</Menu>
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@@ -7,7 +7,8 @@ export const openaiError: Record<string, string> = {
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'Bad Gateway': '网关异常,请重试'
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};
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export const openaiError2: Record<string, string> = {
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insufficient_quota: 'API 余额不足'
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insufficient_quota: 'API 余额不足',
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invalid_request_error: '输入参数异常'
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};
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export const proxyError: Record<string, boolean> = {
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ECONNABORTED: true,
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@@ -25,8 +25,11 @@ export const jsonRes = <T = any>(
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msg = error;
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} else if (proxyError[error?.code]) {
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msg = '服务器代理出错';
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} else if (openaiError2[error?.response?.data?.error?.type]) {
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msg = openaiError2[error?.response?.data?.error?.type];
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} else if (error?.response?.data?.error) {
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msg =
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openaiError2[error?.response?.data?.error?.type] ||
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error?.response?.data?.error?.message ||
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'openai 错误';
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} else if (openaiError[error?.response?.statusText]) {
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msg = openaiError[error.response.statusText];
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}
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