feat: auth openapi key
This commit is contained in:
archer
234
src/pages/api/openapi/chat/lafGpt.ts
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234
src/pages/api/openapi/chat/lafGpt.ts
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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/chat';
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import { authOpenApiKey } from '@/service/utils/tools';
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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 { PassThrough } from 'stream';
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import { ChatModelNameEnum, modelList, ChatModelNameMap } 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, getOpenApiKey, 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 { prompt, modelId } = req.body as {
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prompt: ChatItemType;
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modelId: string;
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};
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if (!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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let startTime = Date.now();
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/* 凭证校验 */
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const userId = await authOpenApiKey(req);
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const { userApiKey, systemKey } = await getOpenApiKey(userId);
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/* 查找数据库里的模型信息 */
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const model = await Model.findById(modelId);
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if (!model) {
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throw new Error('找不到模型');
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}
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const modelConstantsData = modelList.find(
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(item) => item.model === ChatModelNameEnum.VECTOR_GPT
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);
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if (!modelConstantsData) {
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throw new Error('模型已下架');
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}
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// 获取 chatAPI
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const chatAPI = getOpenAIApi(userApiKey || systemKey);
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// 请求一次 chatgpt 拆解需求
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const promptResponse = await chatAPI.createChatCompletion(
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{
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model: ChatModelNameMap[ChatModelNameEnum.GPT35],
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temperature: 0,
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messages: [
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{
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role: 'system',
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content: `服务端逻辑生成器.根据用户输入的需求,拆解成代码实现的步骤,并按格式返回: 1.\n2.\n3.\n ......
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下面是一些例子:
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实现一个手机号发生注册验证码方法.
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1. 从 query 中获取 phone.
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2. 校验手机号格式是否正确,不正确返回{error: "手机号格式错误"}.
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3. 给 phone 发送一个短信验证码,验证码长度为6位字符串,内容为:你正在注册laf,验证码为:code.
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4. 数据库添加数据,表为"codes",内容为 {phone, code}.
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实现根据手机号注册账号,需要验证手机验证码.
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1. 从 body 中获取 phone 和 code.
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2. 校验手机号格式是否正确,不正确返回{error: "手机号格式错误"}.
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2. 获取数据库数据,表为"codes",查找是否有符合 phone, code 等于body参数的记录,没有的话返回 {error:"验证码不正确"}.
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4. 添加数据库数据,表为"users" ,内容为{phone, code, createTime}.
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5. 删除数据库数据,删除 code 记录.
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更新博客记录。传入blogId,blogText,tags,还需要记录更新的时间.
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1. 从 body 中获取 blogId,blogText 和 tags.
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2. 校验 blogId 是否为空,为空则返回 {error: "博客ID不能为空"}.
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3. 校验 blogText 是否为空,为空则返回 {error: "博客内容不能为空"}.
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4. 校验 tags 是否为数组,不是则返回 {error: "标签必须为数组"}.
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5. 获取当前时间,记录为 updateTime.
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6. 更新数据库数据,表为"blogs",更新符合 blogId 的记录的内容为{blogText, tags, updateTime}.
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7. 返回结果 {message: "更新博客记录成功"}.`
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},
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{
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role: 'user',
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content: prompt.value
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}
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]
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},
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{
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timeout: 40000,
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httpsAgent
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}
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);
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const promptResolve = promptResponse.data.choices?.[0]?.message?.content || '';
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if (!promptResolve) {
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throw new Error('gpt 异常');
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}
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prompt.value += `\n${promptResolve}`;
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console.log('prompt resolve success, time:', `${(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: !userApiKey,
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apiKey: userApiKey || systemKey,
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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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// 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:${VecModelDataPrefix}:hash`,
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`@modelId:{${String(model._id)}}=>[KNN 20 @vector $blob AS score]`,
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'RETURN',
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'1',
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'text',
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'SORTBY',
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'score',
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'PARAMS',
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'2',
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'blob',
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vectorToBuffer(promptVector),
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'DIALECT',
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'2'
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]);
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// 格式化响应值，获取 qa
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const formatRedisPrompt: string[] = [];
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for (let i = 2; i < 42; i += 2) {
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const text = redisData[i]?.[1];
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if (text) {
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formatRedisPrompt.push(text);
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}
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}
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// textArr 筛选，最多 3200 tokens
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const systemPrompt = systemPromptFilter(formatRedisPrompt, 3200);
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prompts.unshift({
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obj: 'SYSTEM',
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value: `${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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// 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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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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console.log('response done. time:', `${(Date.now() - startTime) / 1000}s`);
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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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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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