perf: model framwork
This commit is contained in:
archer
202
src/pages/api/openapi/chat/chat.ts
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202
src/pages/api/openapi/chat/chat.ts
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@@ -0,0 +1,202 @@
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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, authOpenApiKey, authModel } 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 {
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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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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 authModel({
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userId,
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modelId
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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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// 使用了知识库搜索
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if (model.chat.useKb) {
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const similarity = ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22;
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const { systemPrompts } = await searchKb_openai({
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apiKey,
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isPay: true,
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text: prompts[prompts.length - 1].value,
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similarity,
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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 jsonRes(res, {
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code: 500,
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message: '对不起，你的问题不在知识库中。',
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data: '对不起，你的问题不在知识库中。'
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});
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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(apiKey);
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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: isStream,
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stop: ['.!?。']
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},
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{
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timeout: 180000,
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responseType: isStream ? 'stream' : 'json',
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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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let responseContent = '';
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if (isStream) {
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step = 1;
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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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// 只有使用平台的 key 才计费
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pushChatBill({
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isPay: true,
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chatModel: model.chat.chatModel,
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userId,
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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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@@ -1,7 +1,7 @@
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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 { getOpenAIApi } from '@/service/utils/auth';
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import { axiosConfig, openaiChatFilter, authOpenApiKey } from '@/service/utils/tools';
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import { getOpenAIApi, authOpenApiKey } from '@/service/utils/auth';
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import { axiosConfig, openaiChatFilter } 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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@@ -60,37 +60,38 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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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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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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// 如果有系统提示词，自动插入
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if (model.systemPrompt) {
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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.systemPrompt
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value: model.chat.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({
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model: model.service.chatModel,
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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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// console.log(filterPrompts);
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// 计算温度
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const temperature = modelConstantsData.maxTemperature * (model.temperature / 10);
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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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// 获取 chatAPI
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const chatAPI = getOpenAIApi(apiKey);
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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,
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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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@@ -126,7 +127,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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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: true,
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modelName: model.service.modelName,
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chatModel: model.chat.chatModel,
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userId,
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messages: filterPrompts.concat({ role: 'assistant', content: responseContent })
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});
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@@ -1,20 +1,14 @@
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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 { getOpenAIApi } from '@/service/utils/auth';
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import { authOpenApiKey } from '@/service/utils/tools';
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import { getOpenAIApi, authOpenApiKey } 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 {
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ModelNameEnum,
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modelList,
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ModelVectorSearchModeMap,
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ChatModelEnum
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} from '@/constants/model';
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import { modelList, ModelVectorSearchModeMap, ChatModelEnum } from '@/constants/model';
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import { pushChatBill } from '@/service/events/pushBill';
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import { openaiCreateEmbedding, gpt35StreamResponse } from '@/service/utils/openai';
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import { PgClient } from '@/service/pg';
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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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@@ -59,10 +53,11 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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throw new Error('找不到模型');
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}
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const modelConstantsData = modelList.find((item) => item.model === ModelNameEnum.VECTOR_GPT);
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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('模型已下架');
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throw new Error('model is undefined');
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}
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console.log('laf gpt start');
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// 获取 chatAPI
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@@ -132,62 +127,48 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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prompt.value += ` ${promptResolve}`;
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console.log('prompt resolve success, time:', `${(Date.now() - startTime) / 1000}s`);
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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,
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userId,
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text: prompt.value
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});
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// 读取对话内容
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const prompts = [prompt];
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// 相似度搜索
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const similarity = ModelVectorSearchModeMap[model.search.mode]?.similarity || 0.22;
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const vectorSearch = await PgClient.select<{ id: string; q: string; a: string }>('modelData', {
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fields: ['id', 'q', 'a'],
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order: [{ field: 'vector', mode: `<=> '[${promptVector}]'` }],
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where: [
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['model_id', model._id],
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'AND',
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['user_id', userId],
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'AND',
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`vector <=> '[${promptVector}]' < ${similarity}`
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],
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limit: 30
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// 获取向量匹配到的提示词
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const { systemPrompts } = await searchKb_openai({
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isPay: true,
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apiKey,
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similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22,
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text: prompt.value,
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modelId,
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userId
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});
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const formatRedisPrompt: string[] = vectorSearch.rows.map((item) => `${item.q}\n${item.a}`);
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// system 筛选，最多 2500 tokens
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const systemPrompt = systemPromptFilter({
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model: model.service.chatModel,
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prompts: formatRedisPrompt,
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const filterSystemPrompt = systemPromptFilter({
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model: model.chat.chatModel,
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prompts: systemPrompts,
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maxTokens: 2500
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});
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prompts.unshift({
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obj: 'SYSTEM',
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value: `${model.systemPrompt} 知识库是最新的,下面是知识库内容:${systemPrompt}`
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value: `${model.chat.systemPrompt} 知识库是最新的,下面是知识库内容:${filterSystemPrompt}`
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});
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// 控制上下文 tokens 数量，防止超出
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const filterPrompts = openaiChatFilter({
|
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model: model.service.chatModel,
|
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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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// console.log(filterPrompts);
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// 计算温度
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const temperature = modelConstantsData.maxTemperature * (model.temperature / 10);
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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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// 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,
|
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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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@@ -223,7 +204,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
|
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|
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pushChatBill({
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isPay: true,
|
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modelName: model.service.modelName,
|
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chatModel: model.chat.chatModel,
|
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userId,
|
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messages: filterPrompts.concat({ role: 'assistant', content: responseContent })
|
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});
|
||||
|
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@@ -1,24 +1,14 @@
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import type { NextApiRequest, NextApiResponse } from 'next';
|
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import { connectToDatabase, Model } from '@/service/mongo';
|
||||
import {
|
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axiosConfig,
|
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systemPromptFilter,
|
||||
authOpenApiKey,
|
||||
openaiChatFilter
|
||||
} from '@/service/utils/tools';
|
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import { axiosConfig, systemPromptFilter, openaiChatFilter } from '@/service/utils/tools';
|
||||
import { getOpenAIApi, authOpenApiKey } from '@/service/utils/auth';
|
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import { ChatItemType } from '@/types/chat';
|
||||
import { jsonRes } from '@/service/response';
|
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import { PassThrough } from 'stream';
|
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import {
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modelList,
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ModelVectorSearchModeMap,
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ModelVectorSearchModeEnum,
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ModelDataStatusEnum
|
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} from '@/constants/model';
|
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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 { openaiCreateEmbedding, gpt35StreamResponse } from '@/service/utils/openai';
|
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import dayjs from 'dayjs';
|
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import { PgClient } from '@/service/pg';
|
||||
import { gpt35StreamResponse } from '@/service/utils/openai';
|
||||
import { searchKb_openai } from '@/service/tools/searchKb';
|
||||
|
||||
/* 发送提示词 */
|
||||
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
|
||||
@@ -72,96 +62,86 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
|
||||
throw new Error('无权使用该模型');
|
||||
}
|
||||
|
||||
const modelConstantsData = modelList.find((item) => item.model === model?.service?.modelName);
|
||||
const modelConstantsData = modelList.find((item) => item.chatModel === model.chat.chatModel);
|
||||
if (!modelConstantsData) {
|
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throw new Error('模型初始化异常');
|
||||
}
|
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|
||||
// 获取提示词的向量
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const { vector: promptVector, chatAPI } = await openaiCreateEmbedding({
|
||||
// 获取向量匹配到的提示词
|
||||
const { systemPrompts } = await searchKb_openai({
|
||||
isPay: true,
|
||||
apiKey,
|
||||
userId,
|
||||
text: prompts[prompts.length - 1].value // 取最后一个
|
||||
similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22,
|
||||
text: prompts[prompts.length - 1].value,
|
||||
modelId,
|
||||
userId
|
||||
});
|
||||
|
||||
// 相似度搜素
|
||||
const similarity = ModelVectorSearchModeMap[model.search.mode]?.similarity || 0.22;
|
||||
const vectorSearch = await PgClient.select<{ id: string; q: string; a: string }>('modelData', {
|
||||
fields: ['id', 'q', 'a'],
|
||||
where: [
|
||||
['status', ModelDataStatusEnum.ready],
|
||||
'AND',
|
||||
['model_id', model._id],
|
||||
'AND',
|
||||
`vector <=> '[${promptVector}]' < ${similarity}`
|
||||
],
|
||||
order: [{ field: 'vector', mode: `<=> '[${promptVector}]'` }],
|
||||
limit: 20
|
||||
});
|
||||
|
||||
const formatRedisPrompt: string[] = vectorSearch.rows.map((item) => `${item.q}\n${item.a}`);
|
||||
|
||||
// system 合并
|
||||
if (prompts[0].obj === 'SYSTEM') {
|
||||
formatRedisPrompt.unshift(prompts.shift()?.value || '');
|
||||
systemPrompts.unshift(prompts.shift()?.value || '');
|
||||
}
|
||||
|
||||
/* 高相似度+退出，无法匹配时直接退出 */
|
||||
if (
|
||||
formatRedisPrompt.length === 0 &&
|
||||
model.search.mode === ModelVectorSearchModeEnum.hightSimilarity
|
||||
systemPrompts.length === 0 &&
|
||||
model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity
|
||||
) {
|
||||
return res.send('对不起，你的问题不在知识库中。');
|
||||
return jsonRes(res, {
|
||||
code: 500,
|
||||
message: '对不起，你的问题不在知识库中。',
|
||||
data: '对不起，你的问题不在知识库中。'
|
||||
});
|
||||
}
|
||||
/* 高相似度+无上下文，不添加额外知识 */
|
||||
if (
|
||||
formatRedisPrompt.length === 0 &&
|
||||
model.search.mode === ModelVectorSearchModeEnum.noContext
|
||||
systemPrompts.length === 0 &&
|
||||
model.chat.searchMode === ModelVectorSearchModeEnum.noContext
|
||||
) {
|
||||
prompts.unshift({
|
||||
obj: 'SYSTEM',
|
||||
value: model.systemPrompt
|
||||
value: model.chat.systemPrompt
|
||||
});
|
||||
} else {
|
||||
// 有匹配或者低匹配度模式情况下，添加知识库内容。
|
||||
// 系统提示词过滤，最多 2500 tokens
|
||||
const systemPrompt = systemPromptFilter({
|
||||
model: model.service.chatModel,
|
||||
prompts: formatRedisPrompt,
|
||||
model: model.chat.chatModel,
|
||||
prompts: systemPrompts,
|
||||
maxTokens: 2500
|
||||
});
|
||||
|
||||
prompts.unshift({
|
||||
obj: 'SYSTEM',
|
||||
value: `
|
||||
${model.systemPrompt}
|
||||
${model.chat.systemPrompt}
|
||||
${
|
||||
model.search.mode === ModelVectorSearchModeEnum.hightSimilarity
|
||||
? `你只能从知识库选择内容回答.不在知识库内容拒绝回复`
|
||||
: ''
|
||||
model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity ? `不回答知识库外的内容.` : ''
|
||||
}
|
||||
知识库内容为: 当前时间为${dayjs().format('YYYY/MM/DD HH:mm:ss')}\n${systemPrompt}'
|
||||
知识库内容为: ${systemPrompt}'
|
||||
`
|
||||
});
|
||||
}
|
||||
|
||||
// 控制在 tokens 数量，防止超出
|
||||
const filterPrompts = openaiChatFilter({
|
||||
model: model.service.chatModel,
|
||||
model: model.chat.chatModel,
|
||||
prompts,
|
||||
maxTokens: modelConstantsData.contextMaxToken - 500
|
||||
});
|
||||
|
||||
// console.log(filterPrompts);
|
||||
// 计算温度
|
||||
const temperature = modelConstantsData.maxTemperature * (model.temperature / 10);
|
||||
const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
|
||||
2
|
||||
);
|
||||
const chatAPI = getOpenAIApi(apiKey);
|
||||
|
||||
// 发出请求
|
||||
const chatResponse = await chatAPI.createChatCompletion(
|
||||
{
|
||||
model: model.service.chatModel,
|
||||
temperature,
|
||||
model: model.chat.chatModel,
|
||||
temperature: Number(temperature) || 0,
|
||||
messages: filterPrompts,
|
||||
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
|
||||
@@ -196,7 +176,7 @@ ${
|
||||
|
||||
pushChatBill({
|
||||
isPay: true,
|
||||
modelName: model.service.modelName,
|
||||
chatModel: model.chat.chatModel,
|
||||
userId,
|
||||
messages: filterPrompts.concat({ role: 'assistant', content: responseContent })
|
||||
});
|
||||
|
||||
Reference in New Issue
Block a user