mirror of
https://github.com/labring/FastGPT.git
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feat: lafgpt请求;fix: 修复发送按键
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archer
281
src/pages/api/chat/lafGpt.ts
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281
src/pages/api/chat/lafGpt.ts
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@@ -0,0 +1,281 @@
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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 } 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 { VecModelDataPrefix } from '@/constants/redis';
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import { vectorToBuffer } from '@/utils/tools';
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import { openaiCreateEmbedding } 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 { 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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let startTime = Date.now();
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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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// 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: model.service.chatModel,
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temperature: 0,
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// max_tokens: modelConstantsData.maxToken,
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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，并按下面格式返回：
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1.
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2.
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3.
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....
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下面是一些例子：
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实现一个手机号注册账号的方法
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发送手机验证码函数：
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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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注册函数
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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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---------------
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更新播客记录。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 = [...chat.content, 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(
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chat.modelId._id
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)}} @vector:[VECTOR_RANGE 0.25 $blob]=>{$YIELD_DISTANCE_AS: score}`,
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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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'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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'20',
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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 = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
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.map((i) => {
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if (!redisData[i]) return '';
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const text = (redisData[i][1] as string) || '';
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if (!text) return '';
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return text;
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})
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.filter((item) => item);
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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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// textArr 筛选，最多 3000 tokens
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const systemPrompt = systemPromptFilter(formatRedisPrompt, 3400);
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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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// 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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} 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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@@ -57,7 +57,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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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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// 获取提示词的向量
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const { vector: promptVector, chatAPI } = await openaiCreateEmbedding({
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isPay: !userApiKey,
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apiKey: userApiKey || systemKey,
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@@ -71,7 +71,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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`idx:${VecModelDataPrefix}:hash`,
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`@modelId:{${String(
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chat.modelId._id
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)}} @vector:[VECTOR_RANGE 0.2 $blob]=>{$YIELD_DISTANCE_AS: score}`,
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)}} @vector:[VECTOR_RANGE 0.25 $blob]=>{$YIELD_DISTANCE_AS: score}`,
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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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@@ -120,6 +120,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
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const urlMap: Record<string, string> = {
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[ChatModelNameEnum.GPT35]: '/api/chat/chatGpt',
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[ChatModelNameEnum.VECTOR_GPT]: '/api/chat/vectorGpt',
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// [ChatModelNameEnum.VECTOR_GPT]: '/api/chat/lafGpt',
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[ChatModelNameEnum.GPT3]: '/api/chat/gpt3'
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};
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@@ -198,7 +199,12 @@ const Chat = ({ chatId }: { chatId: string }) => {
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.split('\n')
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.filter((val) => val)
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.join('\n');
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if (!chatData?.modelId || !val || !ChatBox.current || isChatting) {
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if (!chatData?.modelId || !val || isChatting) {
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toast({
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title: '内容为空',
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status: 'warning'
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});
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return;
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}
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@@ -453,7 +459,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
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{/* 发送区 */}
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<Box m={media('20px auto', '0 auto')} w={'100%'} maxW={media('min(750px, 100%)', 'auto')}>
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<Flex
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alignItems={'flex-end'}
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alignItems={'center'}
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py={5}
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position={'relative'}
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boxShadow={`0 0 15px rgba(0,0,0,0.1)`}
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@@ -501,7 +507,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
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}}
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/>
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{/* 发送和等待按键 */}
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<Box px={4} onClick={sendPrompt}>
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<Flex px={4} h={'30px'} alignItems={'flex-end'} onClick={sendPrompt}>
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{isChatting ? (
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<Image
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style={{ transform: 'translateY(4px)' }}
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@@ -520,7 +526,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
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></Icon>
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</Box>
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)}
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</Box>
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</Flex>
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</Flex>
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</Box>
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</Flex>
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