From f97c29b41e6c6981020cc05c329df8527e4f65ba Mon Sep 17 00:00:00 2001 From: archer <545436317@qq.com> Date: Mon, 3 Apr 2023 16:35:48 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20lafgpt=E8=AF=B7=E6=B1=82;fix:=20?= =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E5=8F=91=E9=80=81=E6=8C=89=E9=94=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/pages/api/chat/lafGpt.ts | 281 ++++++++++++++++++++++++++++++++ src/pages/api/chat/vectorGpt.ts | 4 +- src/pages/chat/index.tsx | 14 +- 3 files changed, 293 insertions(+), 6 deletions(-) create mode 100644 src/pages/api/chat/lafGpt.ts diff --git a/src/pages/api/chat/lafGpt.ts b/src/pages/api/chat/lafGpt.ts new file mode 100644 index 000000000..3a37bde89 --- /dev/null +++ b/src/pages/api/chat/lafGpt.ts @@ -0,0 +1,281 @@ +import type { NextApiRequest, NextApiResponse } from 'next'; +import { createParser, ParsedEvent, ReconnectInterval } from 'eventsource-parser'; +import { connectToDatabase } from '@/service/mongo'; +import { getOpenAIApi, authChat } from '@/service/utils/chat'; +import { httpsAgent, openaiChatFilter, systemPromptFilter } from '@/service/utils/tools'; +import { ChatCompletionRequestMessage, ChatCompletionRequestMessageRoleEnum } from 'openai'; +import { ChatItemType } from '@/types/chat'; +import { jsonRes } from '@/service/response'; +import type { ModelSchema } from '@/types/mongoSchema'; +import { PassThrough } from 'stream'; +import { modelList } from '@/constants/model'; +import { pushChatBill } from '@/service/events/pushBill'; +import { connectRedis } from '@/service/redis'; +import { VecModelDataPrefix } from '@/constants/redis'; +import { vectorToBuffer } from '@/utils/tools'; +import { openaiCreateEmbedding } from '@/service/utils/openai'; + +/* 发送提示词 */ +export default async function handler(req: NextApiRequest, res: NextApiResponse) { + let step = 0; // step=1时，表示开始了流响应 + const stream = new PassThrough(); + stream.on('error', () => { + console.log('error: ', 'stream error'); + stream.destroy(); + }); + res.on('close', () => { + stream.destroy(); + }); + res.on('error', () => { + console.log('error: ', 'request error'); + stream.destroy(); + }); + + try { + const { chatId, prompt } = req.body as { + prompt: ChatItemType; + chatId: string; + }; + + const { authorization } = req.headers; + if (!chatId || !prompt) { + throw new Error('缺少参数'); + } + + await connectToDatabase(); + const redis = await connectRedis(); + let startTime = Date.now(); + + const { chat, userApiKey, systemKey, userId } = await authChat(chatId, authorization); + + const model: ModelSchema = chat.modelId; + const modelConstantsData = modelList.find((item) => item.model === model.service.modelName); + if (!modelConstantsData) { + throw new Error('模型加载异常'); + } + // 获取 chatAPI + const chatAPI = getOpenAIApi(userApiKey || systemKey); + + // 请求一次 chatgpt 拆解需求 + const promptResponse = await chatAPI.createChatCompletion( + { + model: model.service.chatModel, + temperature: 0, + // max_tokens: modelConstantsData.maxToken, + messages: [ + { + role: 'system', + 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： + 1. + 2. + 3. + .... + + 下面是一些例子： + 实现一个手机号注册账号的方法 + 发送手机验证码函数： + 1. 从 query 中获取 phone + 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"} + 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 + 4. 数据库添加数据，表为"codes"，内容为 {phone, code} + 注册函数 + 1. 从 body 中获取 phone 和 code + 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"} + 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"} + 4. 添加数据库数据，表为"users" ，内容为{phone, code, createTime} + 5. 删除数据库数据，删除 code 记录 + --------------- + 更新播客记录。传入blogId，blogText，tags，还需要记录更新的时间 + 1. 从 body 中获取 blogId，blogText 和 tags + 2. 校验 blogId 是否为空，为空则返回 {error: "博客ID不能为空"} + 3. 校验 blogText 是否为空，为空则返回 {error: "博客内容不能为空"} + 4. 校验 tags 是否为数组，不是则返回 {error: "标签必须为数组"} + 5. 获取当前时间，记录为 updateTime + 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} + 7. 返回结果 {message: "更新博客记录成功"}` + }, + { + role: 'user', + content: prompt.value + } + ] + }, + { + timeout: 40000, + httpsAgent + } + ); + + const promptResolve = promptResponse.data.choices?.[0]?.message?.content || ''; + if (!promptResolve) { + throw new Error('gpt 异常'); + } + + prompt.value += `\n${promptResolve}`; + console.log('prompt resolve success, time:', `${(Date.now() - startTime) / 1000}s`); + + // 获取提示词的向量 + const { vector: promptVector } = await openaiCreateEmbedding({ + isPay: !userApiKey, + apiKey: userApiKey || systemKey, + userId, + text: prompt.value + }); + + // 读取对话内容 + const prompts = [...chat.content, prompt]; + + // 搜索系统提示词, 按相似度从 redis 中搜出相关的 q 和 text + const redisData: any[] = await redis.sendCommand([ + 'FT.SEARCH', + `idx:${VecModelDataPrefix}:hash`, + `@modelId:{${String( + chat.modelId._id + )}} @vector:[VECTOR_RANGE 0.25 $blob]=>{$YIELD_DISTANCE_AS: score}`, + // `@modelId:{${String(chat.modelId._id)}}=>[KNN 10 @vector $blob AS score]`, + 'RETURN', + '1', + 'text', + 'SORTBY', + 'score', + 'PARAMS', + '2', + 'blob', + vectorToBuffer(promptVector), + 'LIMIT', + '0', + '20', + 'DIALECT', + '2' + ]); + + // 格式化响应值，获取 qa + const formatRedisPrompt = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] + .map((i) => { + if (!redisData[i]) return ''; + const text = (redisData[i][1] as string) || ''; + + if (!text) return ''; + + return text; + }) + .filter((item) => item); + + if (formatRedisPrompt.length === 0) { + throw new Error('对不起，我没有找到你的问题'); + } + + // textArr 筛选，最多 3000 tokens + const systemPrompt = systemPromptFilter(formatRedisPrompt, 3400); + + prompts.unshift({ + obj: 'SYSTEM', + value: `${model.systemPrompt} 知识库内容是最新的，知识库内容为: "${systemPrompt}"` + }); + + // 控制在 tokens 数量，防止超出 + const filterPrompts = openaiChatFilter(prompts, modelConstantsData.contextMaxToken); + + // 格式化文本内容成 chatgpt 格式 + const map = { + Human: ChatCompletionRequestMessageRoleEnum.User, + AI: ChatCompletionRequestMessageRoleEnum.Assistant, + SYSTEM: ChatCompletionRequestMessageRoleEnum.System + }; + const formatPrompts: ChatCompletionRequestMessage[] = filterPrompts.map( + (item: ChatItemType) => ({ + role: map[item.obj], + content: item.value + }) + ); + console.log(formatPrompts); + // 计算温度 + const temperature = modelConstantsData.maxTemperature * (model.temperature / 10); + + // 发出请求 + const chatResponse = await chatAPI.createChatCompletion( + { + model: model.service.chatModel, + temperature: temperature, + // max_tokens: modelConstantsData.maxToken, + messages: formatPrompts, + frequency_penalty: 0.5, // 越大，重复内容越少 + presence_penalty: -0.5, // 越大，越容易出现新内容 + stream: true + }, + { + timeout: 40000, + responseType: 'stream', + httpsAgent + } + ); + + console.log('api response time:', `${(Date.now() - startTime) / 1000}s`); + + // 创建响应流 + res.setHeader('Content-Type', 'text/event-stream;charset-utf-8'); + res.setHeader('Access-Control-Allow-Origin', '*'); + res.setHeader('X-Accel-Buffering', 'no'); + res.setHeader('Cache-Control', 'no-cache, no-transform'); + step = 1; + + let responseContent = ''; + stream.pipe(res); + + const onParse = async (event: ParsedEvent | ReconnectInterval) => { + if (event.type !== 'event') return; + const data = event.data; + if (data === '[DONE]') return; + try { + const json = JSON.parse(data); + const content: string = json?.choices?.[0].delta.content || ''; + if (!content || (responseContent === '' && content === '\n')) return; + + responseContent += content; + // console.log('content:', content) + !stream.destroyed && stream.push(content.replace(/\n/g, '
')); + } catch (error) { + error; + } + }; + + const decoder = new TextDecoder(); + try { + for await (const chunk of chatResponse.data as any) { + if (stream.destroyed) { + // 流被中断了，直接忽略后面的内容 + break; + } + const parser = createParser(onParse); + parser.feed(decoder.decode(chunk)); + } + } catch (error) { + console.log('pipe error', error); + } + // close stream + !stream.destroyed && stream.push(null); + stream.destroy(); + + const promptsContent = formatPrompts.map((item) => item.content).join(''); + // 只有使用平台的 key 才计费 + pushChatBill({ + isPay: !userApiKey, + modelName: model.service.modelName, + userId, + chatId, + text: promptsContent + responseContent + }); + } catch (err: any) { + if (step === 1) { + // 直接结束流 + console.log('error，结束'); + stream.destroy(); + } else { + res.status(500); + jsonRes(res, { + code: 500, + error: err + }); + } + } +} diff --git a/src/pages/api/chat/vectorGpt.ts b/src/pages/api/chat/vectorGpt.ts index ed4a1689e..13b713e62 100644 --- a/src/pages/api/chat/vectorGpt.ts +++ b/src/pages/api/chat/vectorGpt.ts @@ -57,7 +57,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse) // 读取对话内容 const prompts = [...chat.content, prompt]; - // 获取 chatAPI + // 获取提示词的向量 const { vector: promptVector, chatAPI } = await openaiCreateEmbedding({ isPay: !userApiKey, apiKey: userApiKey || systemKey, @@ -71,7 +71,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse) `idx:${VecModelDataPrefix}:hash`, `@modelId:{${String( chat.modelId._id - )}} @vector:[VECTOR_RANGE 0.2 $blob]=>{$YIELD_DISTANCE_AS: score}`, + )}} @vector:[VECTOR_RANGE 0.25 $blob]=>{$YIELD_DISTANCE_AS: score}`, // `@modelId:{${String(chat.modelId._id)}}=>[KNN 10 @vector $blob AS score]`, 'RETURN', '1', diff --git a/src/pages/chat/index.tsx b/src/pages/chat/index.tsx index dc93787eb..2a17b3ea3 100644 --- a/src/pages/chat/index.tsx +++ b/src/pages/chat/index.tsx @@ -120,6 +120,7 @@ const Chat = ({ chatId }: { chatId: string }) => { const urlMap: Record = { [ChatModelNameEnum.GPT35]: '/api/chat/chatGpt', [ChatModelNameEnum.VECTOR_GPT]: '/api/chat/vectorGpt', + // [ChatModelNameEnum.VECTOR_GPT]: '/api/chat/lafGpt', [ChatModelNameEnum.GPT3]: '/api/chat/gpt3' }; @@ -198,7 +199,12 @@ const Chat = ({ chatId }: { chatId: string }) => { .split('\n') .filter((val) => val) .join('\n'); - if (!chatData?.modelId || !val || !ChatBox.current || isChatting) { + + if (!chatData?.modelId || !val || isChatting) { + toast({ + title: '内容为空', + status: 'warning' + }); return; } @@ -453,7 +459,7 @@ const Chat = ({ chatId }: { chatId: string }) => { {/* 发送区 */} { }} /> {/* 发送和等待按键 */} - + {isChatting ? ( { > )} - +