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2d3117c5da
* 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
118 lines
2.6 KiB
TypeScript
118 lines
2.6 KiB
TypeScript
import { connectionMongo, getMongoModel } from '../../../common/mongo';
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const { Schema, model, models } = connectionMongo;
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import { type DatasetDataSchemaType } from '@fastgpt/global/core/dataset/type.d';
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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 { DatasetCollectionName } from '../schema';
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import { DatasetColCollectionName } from '../collection/schema';
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import { DatasetDataIndexTypeEnum } from '@fastgpt/global/core/dataset/data/constants';
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export const DatasetDataCollectionName = 'dataset_datas';
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const DatasetDataSchema = 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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ref: DatasetCollectionName,
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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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q: {
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type: String,
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required: true
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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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history: {
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type: [
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{
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q: String,
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a: String,
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updateTime: Date
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}
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]
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},
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indexes: {
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type: [
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{
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// Abandon
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defaultIndex: {
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type: Boolean
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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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default: DatasetDataIndexTypeEnum.custom
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},
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dataId: {
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type: String,
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required: true
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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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updateTime: {
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type: Date,
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default: () => new Date()
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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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rebuilding: Boolean,
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// Abandon
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fullTextToken: String,
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initFullText: Boolean,
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initJieba: Boolean
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});
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try {
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// list collection and count data; list data; delete collection(relate data)
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DatasetDataSchema.index({
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teamId: 1,
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datasetId: 1,
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collectionId: 1,
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chunkIndex: 1,
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updateTime: -1
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});
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// Recall vectors after data matching
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DatasetDataSchema.index({ teamId: 1, datasetId: 1, collectionId: 1, 'indexes.dataId': 1 });
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DatasetDataSchema.index({ updateTime: 1 });
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// rebuild data
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DatasetDataSchema.index({ rebuilding: 1, teamId: 1, datasetId: 1 });
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// 为查询 initJieba 字段不存在的数据添加索引
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DatasetDataSchema.index({ initJieba: 1, updateTime: 1 });
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} catch (error) {
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console.log(error);
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}
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export const MongoDatasetData = getMongoModel<DatasetDataSchemaType>(
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DatasetDataCollectionName,
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DatasetDataSchema
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);
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