feat: 模型数据管理
feat: 模型数据导入 feat: redis 向量入库 feat: 向量索引 feat: 文件导入模型 perf: 交互 perf: prompt
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archer
241
src/pages/api/chat/vectorGpt.ts
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241
src/pages/api/chat/vectorGpt.ts
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import type { NextApiRequest, NextApiResponse } from 'next';
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import { createParser, ParsedEvent, ReconnectInterval } from 'eventsource-parser';
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import { connectToDatabase, ModelData } from '@/service/mongo';
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import { getOpenAIApi, authChat } from '@/service/utils/chat';
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import { httpsAgent, openaiChatFilter, systemPromptFilter } 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 { connectRedis } from '@/service/redis';
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import { VecModelDataIndex } from '@/constants/redis';
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import { vectorToBuffer } from '@/utils/tools';
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let vectorData = [
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-0.025028639, -0.010407282, 0.026523087, -0.0107438695, -0.006967359, 0.010043768, -0.012043097,
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0.008724345, -0.028919589, -0.0117738275, 0.0050690062, 0.02961969
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].concat(new Array(1524).fill(0));
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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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const redis = await connectRedis();
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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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// 获取 chatAPI
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const chatAPI = getOpenAIApi(userApiKey || systemKey);
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// 把输入的内容转成向量
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const promptVector = await chatAPI
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.createEmbedding(
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{
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model: 'text-embedding-ada-002',
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input: prompt.value
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},
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{
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timeout: 120000,
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httpsAgent
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}
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)
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.then((res) => res?.data?.data?.[0]?.embedding || []);
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const binary = vectorToBuffer(promptVector);
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// 搜索系统提示词, 按相似度从 redis 中搜出前3条不同 dataId 的数据
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const redisData: any[] = await redis.sendCommand([
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'FT.SEARCH',
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`idx:${VecModelDataIndex}`,
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`@modelId:{${String(chat.modelId._id)}} @vector:[VECTOR_RANGE 0.2 $blob]`,
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// `@modelId:{${String(chat.modelId._id)}}=>[KNN 10 @vector $blob AS score]`,
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'RETURN',
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'1',
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'dataId',
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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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binary,
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'DIALECT',
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'2'
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]);
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// 格式化响应值，获取去重后的id
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let formatIds = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
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.map((i) => {
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if (!redisData[i] || !redisData[i][1]) return '';
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return redisData[i][1];
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})
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.filter((item) => item);
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formatIds = Array.from(new Set(formatIds));
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if (formatIds.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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// 从 mongo 中取出原文作为提示词
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const textArr = (
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await Promise.all(
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[2, 4, 6, 8, 10, 12, 14, 16, 18, 20].map((i) => {
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if (!redisData[i] || !redisData[i][1]) return '';
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return ModelData.findById(redisData[i][1])
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.select('text')
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.then((res) => res?.text || '');
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})
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)
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).filter((item) => item);
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// textArr 筛选，最多 3000 tokens
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const systemPrompt = systemPromptFilter(textArr, 2800);
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prompts.unshift({
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obj: 'SYSTEM',
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value: `请根据下面的知识回答问题： ${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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let startTime = Date.now();
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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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},
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{
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timeout: 40000,
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responseType: 'stream',
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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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// 创建响应流
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res.setHeader('Content-Type', 'text/event-stream;charset-utf-8');
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res.setHeader('Access-Control-Allow-Origin', '*');
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res.setHeader('X-Accel-Buffering', 'no');
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res.setHeader('Cache-Control', 'no-cache, no-transform');
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step = 1;
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let responseContent = '';
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stream.pipe(res);
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const onParse = async (event: ParsedEvent | ReconnectInterval) => {
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if (event.type !== 'event') return;
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const data = event.data;
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if (data === '[DONE]') return;
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try {
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const json = JSON.parse(data);
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const content: string = json?.choices?.[0].delta.content || '';
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if (!content || (responseContent === '' && content === '\n')) return;
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responseContent += content;
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// console.log('content:', content)
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!stream.destroyed && stream.push(content.replace(/\n/g, '<br/>'));
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} catch (error) {
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error;
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}
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};
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const decoder = new TextDecoder();
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try {
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for await (const chunk of chatResponse.data as any) {
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if (stream.destroyed) {
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// 流被中断了，直接忽略后面的内容
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break;
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}
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const parser = createParser(onParse);
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parser.feed(decoder.decode(chunk));
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}
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} catch (error) {
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console.log('pipe error', error);
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}
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// close stream
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!stream.destroyed && stream.push(null);
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stream.destroy();
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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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// 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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