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* update: Add type * fix: update import statement for NextApiRequest type * fix: update imports to use type for LexicalEditor and EditorState * Refactor imports to use 'import type' for type-only imports across multiple files - Updated imports in various components and API files to use 'import type' for better clarity and to optimize TypeScript's type checking. - Ensured consistent usage of type imports in files related to chat, dataset, workflow, and user management. - Improved code readability and maintainability by distinguishing between value and type imports. * refactor: remove old ESLint configuration and add new rules - Deleted the old ESLint configuration file from the app project. - Added a new ESLint configuration file with updated rules and settings. - Changed imports to use type-only imports in various files for better clarity and performance. - Updated TypeScript configuration to remove unnecessary options. - Added an ESLint ignore file to exclude build and dependency directories from linting. * fix: update imports to use 'import type' for type-only imports in schema files
133 lines
2.9 KiB
TypeScript
133 lines
2.9 KiB
TypeScript
/* 模型的知识库 */
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import { connectionMongo, getMongoModel } from '../../../common/mongo';
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const { Schema } = connectionMongo;
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import { type DatasetTrainingSchemaType } from '@fastgpt/global/core/dataset/type';
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import { TrainingModeEnum } from '@fastgpt/global/core/dataset/constants';
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import { DatasetColCollectionName } from '../collection/schema';
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import { DatasetCollectionName } from '../schema';
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import {
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TeamCollectionName,
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TeamMemberCollectionName
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} from '@fastgpt/global/support/user/team/constant';
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import { DatasetDataIndexTypeEnum } from '@fastgpt/global/core/dataset/data/constants';
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export const DatasetTrainingCollectionName = 'dataset_trainings';
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const TrainingDataSchema = new Schema({
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teamId: {
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type: Schema.Types.ObjectId,
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ref: TeamCollectionName,
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required: true
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},
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tmbId: {
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type: Schema.Types.ObjectId,
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ref: TeamMemberCollectionName,
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required: true
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},
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datasetId: {
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type: Schema.Types.ObjectId,
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required: true
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},
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collectionId: {
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type: Schema.Types.ObjectId,
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ref: DatasetColCollectionName,
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required: true
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},
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billId: String,
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mode: {
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type: String,
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enum: Object.values(TrainingModeEnum),
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required: true
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},
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expireAt: {
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// It will be deleted after 7 days
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type: Date,
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default: () => new Date()
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},
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lockTime: {
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type: Date,
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default: () => new Date('2000/1/1')
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},
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retryCount: {
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type: Number,
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default: 5
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},
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model: {
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// ai model
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type: String,
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required: true
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},
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prompt: {
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// qa split prompt
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type: String,
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default: ''
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},
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q: {
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type: String,
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default: ''
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},
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a: {
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type: String,
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default: ''
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},
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chunkIndex: {
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type: Number,
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default: 0
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},
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indexSize: Number,
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weight: {
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type: Number,
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default: 0
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},
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dataId: {
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type: Schema.Types.ObjectId
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},
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indexes: {
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type: [
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{
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type: {
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type: String,
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enum: Object.values(DatasetDataIndexTypeEnum)
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},
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text: {
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type: String,
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required: true
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}
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}
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],
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default: []
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},
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errorMsg: String
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});
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TrainingDataSchema.virtual('dataset', {
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ref: DatasetCollectionName,
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localField: 'datasetId',
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foreignField: '_id',
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justOne: true
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});
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TrainingDataSchema.virtual('collection', {
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ref: DatasetColCollectionName,
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localField: 'collectionId',
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foreignField: '_id',
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justOne: true
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});
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try {
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// lock training data(teamId); delete training data
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TrainingDataSchema.index({ teamId: 1, datasetId: 1 });
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// get training data and sort
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TrainingDataSchema.index({ mode: 1, retryCount: 1, lockTime: 1, weight: -1 });
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TrainingDataSchema.index({ expireAt: 1 }, { expireAfterSeconds: 7 * 24 * 60 * 60 }); // 7 days
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} catch (error) {
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console.log(error);
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}
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export const MongoDatasetTraining = getMongoModel<DatasetTrainingSchemaType>(
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DatasetTrainingCollectionName,
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TrainingDataSchema
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);
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