perf: keys

client/src/pages/api/openapi/kb/appKbSearch.ts

@@ -96,8 +96,7 @@ export async function appKbSearch({
   // get vector
   const promptVector = await openaiEmbedding({
     userId,
-    input: [prompt.value],
-    type: 'chat'
+    input: [prompt.value]
   });
 
   // search kb

client/src/pages/api/openapi/kb/searchTest.ts

@@ -29,8 +29,7 @@ export default withNextCors(async function handler(req: NextApiRequest, res: Nex
 
     const vector = await openaiEmbedding({
       userId,
-      input: [text],
-      type: 'training'
+      input: [text]
     });
 
     const response: any = await PgClient.query(

client/src/pages/api/openapi/kb/updateData.ts

@@ -21,8 +21,7 @@ export default withNextCors(async function handler(req: NextApiRequest, res: Nex
       if (q) {
         return openaiEmbedding({
           userId,
-          input: [q],
-          type: 'chat'
+          input: [q]
         });
       }
       return [];

client/src/pages/api/openapi/plugin/openaiEmbedding.ts

@@ -6,26 +6,24 @@ import { getOpenAIApi } from '@/service/utils/chat/openai';
 import { embeddingModel } from '@/constants/model';
 import { axiosConfig } from '@/service/utils/tools';
 import { pushGenerateVectorBill } from '@/service/events/pushBill';
-import { ApiKeyType } from '@/service/utils/auth';
 import { OpenAiChatEnum } from '@/constants/model';
 
 type Props = {
   input: string[];
-  type?: ApiKeyType;
 };
 type Response = number[][];
 
 export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
   try {
     const { userId } = await authUser({ req });
-    let { input, type } = req.query as Props;
+    let { input } = req.query as Props;
 
     if (!Array.isArray(input)) {
       throw new Error('缺少参数');
     }
 
     jsonRes<Response>(res, {
-      data: await openaiEmbedding({ userId, input, type, mustPay: true })
+      data: await openaiEmbedding({ userId, input, mustPay: true })
     });
   } catch (err) {
     console.log(err);
@@ -39,14 +37,12 @@ export default withNextCors(async function handler(req: NextApiRequest, res: Nex
 export async function openaiEmbedding({
   userId,
   input,
-  mustPay = false,
-  type = 'chat'
+  mustPay = false
 }: { userId: string; mustPay?: boolean } & Props) {
   const { userOpenAiKey, systemAuthKey } = await getApiKey({
     model: OpenAiChatEnum.GPT35,
     userId,
-    mustPay,
-    type
+    mustPay
   });
 
   // 获取 chatAPI

client/src/pages/api/openapi/text/sensitiveCheck.ts

@@ -33,7 +33,7 @@ export async function sensitiveCheck({ input }: Props) {
   }
 
   const response = await axios({
-    ...axiosConfig(getSystemOpenAiKey('chat')),
+    ...axiosConfig(getSystemOpenAiKey()),
     method: 'POST',
     url: `/moderations`,
     data: {

client/src/pages/api/system/getModels.ts

@@ -7,11 +7,9 @@ import { ChatModelMap, OpenAiChatEnum } from '@/constants/model';
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {
   const chatModelList: ChatModelItemType[] = [];
 
-  if (global.systemEnv.openAIKeys) {
+  if (process.env.OPENAIKEY) {
     chatModelList.push(ChatModelMap[OpenAiChatEnum.GPT3516k]);
     chatModelList.push(ChatModelMap[OpenAiChatEnum.GPT35]);
-  }
-  if (global.systemEnv.gpt4Key) {
     chatModelList.push(ChatModelMap[OpenAiChatEnum.GPT4]);
   }
 

client/src/pages/kb/components/SelectFileModal.tsx

@@ -256,7 +256,7 @@ const SelectFileModal = ({
             )}
             {/* chunk size */}
             {mode === TrainingModeEnum.index && (
-              <Flex w={'100%'} px={5} alignItems={'center'} mt={4}>
+              <Flex mt={5}>
                 <Box w={['70px']} flexShrink={0}>
                   段落长度
                 </Box>

client/src/service/events/generateQA.ts

@@ -55,7 +55,6 @@ export async function generateQA(): Promise<any> {
     const { systemAuthKey } = await getApiKey({
       model: OpenAiChatEnum.GPT35,
       userId,
-      type: 'training',
       mustPay: true
     });
 

client/src/service/events/generateVector.ts

@@ -58,7 +58,6 @@ export async function generateVector(): Promise<any> {
     const vectors = await openaiEmbedding({
       input: dataItems.map((item) => item.q),
       userId,
-      type: 'training',
       mustPay: true
     });
 

client/src/service/models/system.ts

@@ -1,18 +1,6 @@
 import { Schema, model, models } from 'mongoose';
 
 const SystemSchema = new Schema({
-  openAIKeys: {
-    type: String,
-    default: ''
-  },
-  openAITrainingKeys: {
-    type: String,
-    default: ''
-  },
-  gpt4Key: {
-    type: String,
-    default: ''
-  },
   vectorMaxProcess: {
     type: Number,
     default: 10

client/src/service/mongo.ts

@@ -15,9 +15,6 @@ export async function connectToDatabase(): Promise<void> {
   global.qaQueueLen = 0;
   global.vectorQueueLen = 0;
   global.systemEnv = {
-    openAIKeys: process.env.OPENAIKEY || '',
-    openAITrainingKeys: process.env.OPENAI_TRAINING_KEY || '',
-    gpt4Key: process.env.GPT4KEY || '',
     vectorMaxProcess: 10,
     qaMaxProcess: 10,
     pgIvfflatProbe: 10,

client/src/service/utils/auth.ts

@@ -11,7 +11,6 @@ import { ERROR_ENUM } from '../errorCode';
 import { ChatModelType, OpenAiChatEnum } from '@/constants/model';
 import { hashPassword } from '@/service/utils/tools';
 
-export type ApiKeyType = 'training' | 'chat';
 export type AuthType = 'token' | 'root' | 'apikey';
 
 export const parseCookie = (cookie?: string): Promise<string> => {
@@ -163,37 +162,19 @@ export const authUser = async ({
 };
 
 /* random get openai api key */
-export const getSystemOpenAiKey = (type: ApiKeyType) => {
-  const keys = (() => {
-    if (type === 'training') {
-      return global.systemEnv.openAITrainingKeys?.split(',') || [];
-    }
-    return global.systemEnv.openAIKeys?.split(',') || [];
-  })();
-
-  // 纯字符串类型
-  const i = Math.floor(Math.random() * keys.length);
-  return keys[i] || (global.systemEnv.openAIKeys as string);
-};
-export const getGpt4Key = () => {
-  const keys = global.systemEnv.gpt4Key?.split(',') || [];
-
-  // 纯字符串类型
-  const i = Math.floor(Math.random() * keys.length);
-  return keys[i] || (global.systemEnv.openAIKeys as string);
+export const getSystemOpenAiKey = () => {
+  return process.env.OPENAIKEY || '';
 };
 
 /* 获取 api 请求的 key */
 export const getApiKey = async ({
   model,
   userId,
-  mustPay = false,
-  type = 'chat'
+  mustPay = false
 }: {
   model: ChatModelType;
   userId: string;
   mustPay?: boolean;
-  type?: ApiKeyType;
 }) => {
   const user = await User.findById(userId);
   if (!user) {
@@ -203,19 +184,19 @@ export const getApiKey = async ({
   const keyMap = {
     [OpenAiChatEnum.GPT35]: {
       userOpenAiKey: user.openaiKey || '',
-      systemAuthKey: getSystemOpenAiKey(type) as string
+      systemAuthKey: getSystemOpenAiKey()
     },
     [OpenAiChatEnum.GPT3516k]: {
       userOpenAiKey: user.openaiKey || '',
-      systemAuthKey: getSystemOpenAiKey(type) as string
+      systemAuthKey: getSystemOpenAiKey()
     },
     [OpenAiChatEnum.GPT4]: {
       userOpenAiKey: user.openaiKey || '',
-      systemAuthKey: getGpt4Key() as string
+      systemAuthKey: getSystemOpenAiKey()
     },
     [OpenAiChatEnum.GPT432k]: {
       userOpenAiKey: user.openaiKey || '',
-      systemAuthKey: getGpt4Key() as string
+      systemAuthKey: getSystemOpenAiKey()
     }
   };
 

client/src/types/index.d.ts

@@ -23,9 +23,6 @@ declare global {
   var vectorQueueLen: number;
   var OpenAiEncMap: Record<string, Tiktoken>;
   var systemEnv: {
-    openAIKeys: string;
-    openAITrainingKeys: string;
-    gpt4Key: string;
     vectorMaxProcess: number;
     qaMaxProcess: number;
     pgIvfflatProbe: number;