mirror of
https://github.com/labring/FastGPT.git
synced 2025-07-23 21:13:50 +00:00
278 lines
9.4 KiB
TypeScript
278 lines
9.4 KiB
TypeScript
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，并按格式返回： 1.\n2.\n3.\n ......
|
||
|
||
下面是一些例子：
|
||
实现一个手机号注册账号的方法，包含两个函数
|
||
* 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
|
||
---------------
|
||
更新博客记录。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, '<br/>'));
|
||
} 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
|
||
});
|
||
}
|
||
}
|
||
}
|