feat: 知识库对外api

This commit is contained in:
archer
2023-04-08 20:27:43 +08:00
parent 9a145f223f
commit 52d00d0562
7 changed files with 230 additions and 17 deletions

View File

@@ -31,13 +31,13 @@ const navbarList = [
icon: 'user',
link: '/number/setting',
activeLink: ['/number/setting']
},
{
label: '开发',
icon: 'develop',
link: '/openapi',
activeLink: ['/openapi']
}
// {
// label: '开发',
// icon: 'develop',
// link: '/openapi',
// activeLink: ['/openapi']
// }
];
const Layout = ({ children }: { children: JSX.Element }) => {

View File

@@ -82,14 +82,14 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
vectorToBuffer(promptVector),
'LIMIT',
'0',
'20',
'30',
'DIALECT',
'2'
]);
const formatRedisPrompt: string[] = [];
// 格式化响应值,获取 qa
for (let i = 2; i < 42; i += 2) {
for (let i = 2; i < 61; i += 2) {
const text = redisData[i]?.[1];
if (text) {
formatRedisPrompt.push(text);

View File

@@ -126,7 +126,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
// 获取提示词的向量
const { vector: promptVector } = await openaiCreateEmbedding({
isPay: true,
apiKey: apiKey,
apiKey,
userId,
text: prompt.value
});

View File

@@ -0,0 +1,210 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { connectToDatabase, Model } from '@/service/mongo';
import {
httpsAgent,
openaiChatFilter,
systemPromptFilter,
authOpenApiKey
} from '@/service/utils/tools';
import { ChatCompletionRequestMessage, ChatCompletionRequestMessageRoleEnum } from 'openai';
import { ChatItemType } from '@/types/chat';
import { jsonRes } from '@/service/response';
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, gpt35StreamResponse } 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 {
prompts,
modelId,
isStream = true
} = req.body as {
prompts: ChatItemType[];
modelId: string;
isStream: boolean;
};
if (!prompts || !modelId) {
throw new Error('缺少参数');
}
if (!Array.isArray(prompts)) {
throw new Error('prompts is not array');
}
if (prompts.length > 30 || prompts.length === 0) {
throw new Error('prompts length range 1-30');
}
await connectToDatabase();
const redis = await connectRedis();
let startTime = Date.now();
/* 凭证校验 */
const { apiKey, userId } = await authOpenApiKey(req);
const model = await Model.findOne({
_id: modelId,
userId
});
if (!model) {
throw new Error('无权使用该模型');
}
const modelConstantsData = modelList.find((item) => item.model === model?.service?.modelName);
if (!modelConstantsData) {
throw new Error('模型初始化异常');
}
// 获取提示词的向量
const { vector: promptVector, chatAPI } = await openaiCreateEmbedding({
isPay: true,
apiKey,
userId,
text: prompts[prompts.length - 1].value // 取最后一个
});
// 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:${VecModelDataPrefix}:hash`,
`@modelId:{${modelId}} @vector:[VECTOR_RANGE 0.24 $blob]=>{$YIELD_DISTANCE_AS: score}`,
'RETURN',
'1',
'text',
'SORTBY',
'score',
'PARAMS',
'2',
'blob',
vectorToBuffer(promptVector),
'LIMIT',
'0',
'30',
'DIALECT',
'2'
]);
const formatRedisPrompt: string[] = [];
// 格式化响应值,获取 qa
for (let i = 2; i < 61; i += 2) {
const text = redisData[i]?.[1];
if (text) {
formatRedisPrompt.push(text);
}
}
if (formatRedisPrompt.length === 0) {
throw new Error('对不起,我没有找到你的问题');
}
// system 合并
if (prompts[0].obj === 'SYSTEM') {
formatRedisPrompt.unshift(prompts.shift()?.value || '');
}
// textArr 筛选,最多 2800 tokens
const systemPrompt = systemPromptFilter(formatRedisPrompt, 2800);
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,
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: isStream
},
{
timeout: 120000,
responseType: isStream ? 'stream' : 'json',
httpsAgent
}
);
console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
step = 1;
let responseContent = '';
if (isStream) {
const streamResponse = await gpt35StreamResponse({
res,
stream,
chatResponse
});
responseContent = streamResponse.responseContent;
} else {
responseContent = chatResponse.data.choices?.[0]?.message?.content || '';
jsonRes(res, {
data: responseContent
});
}
const promptsContent = formatPrompts.map((item) => item.content).join('');
pushChatBill({
isPay: true,
modelName: model.service.modelName,
userId,
text: promptsContent + responseContent
});
// jsonRes(res);
} 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
});
}
}
}

View File

@@ -300,10 +300,9 @@ const Chat = ({ chatId }: { chatId: string }) => {
// 复制内容
const onclickCopy = useCallback(
(chatId: string) => {
const dom = document.getElementById(chatId);
const innerText = dom?.innerText;
innerText && copyData(innerText);
(value: string) => {
const val = value.replace(/\n+/g, '\n');
copyData(val);
},
[copyData]
);
@@ -434,7 +433,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
/>
</MenuButton>
<MenuList fontSize={'sm'}>
<MenuItem onClick={() => onclickCopy(`chat${index}`)}></MenuItem>
<MenuItem onClick={() => onclickCopy(item.value)}></MenuItem>
<MenuItem onClick={() => delChatRecord(index)}></MenuItem>
</MenuList>
</Menu>

View File

@@ -7,7 +7,8 @@ export const openaiError: Record<string, string> = {
'Bad Gateway': '网关异常,请重试'
};
export const openaiError2: Record<string, string> = {
insufficient_quota: 'API 余额不足'
insufficient_quota: 'API 余额不足',
invalid_request_error: '输入参数异常'
};
export const proxyError: Record<string, boolean> = {
ECONNABORTED: true,

View File

@@ -25,8 +25,11 @@ export const jsonRes = <T = any>(
msg = error;
} else if (proxyError[error?.code]) {
msg = '服务器代理出错';
} else if (openaiError2[error?.response?.data?.error?.type]) {
msg = openaiError2[error?.response?.data?.error?.type];
} else if (error?.response?.data?.error) {
msg =
openaiError2[error?.response?.data?.error?.type] ||
error?.response?.data?.error?.message ||
'openai 错误';
} else if (openaiError[error?.response?.statusText]) {
msg = openaiError[error.response.statusText];
}