Files
FastGPT/packages/service/core/dataset/collection/controller.ts
Archer 1aebe5f185 V4.8.15 feature (#3331)
* feat: add customize toolkit (#3205)

* chaoyang

* fix-auth

* add toolkit

* add order

* plugin usage

* fix

* delete console:

* Fix: Fix fullscreen preview top positioning and improve Markdown rendering logic (#3247)

* 完成任务:修复全屏预览顶部固定问题,优化 Markdown 渲染逻辑

* 有问题修改

* 问题再修改

* 修正问题

* fix: plugin standalone display issue (#3254)

* 4.8.15 test (#3246)

* o1 config

* perf: system plugin code

* 调整系统插件代码。增加html 渲染安全配置。 (#3258)

* perf: base64 picker

* perf: list app or dataset

* perf: plugin config code

* 小窗适配等问题 (#3257)

* 小窗适配等问题

* git问题

* 小窗剩余问题

* feat: system plugin auth and lock version (#3265)

* feat: system plugin auth and lock version

* update comment

* 4.8.15 test (#3267)

* tmp log

* perf: login direct

* perf: iframe html code

* remove log

* fix: plugin standalone display (#3277)

* refactor: 页面拆分&i18n拆分 (#3281)

* refactor: account组件拆成独立页面

* script: 新增i18n json文件创建脚本

* refactor: 页面i18n拆分

* i18n: add en&hant

* 4.8.15 test (#3285)

* tmp log

* remove log

* fix: watch avatar refresh

* perf: i18n code

* fix(plugin): use intro instead of userguide (#3290)

* Universal SSO (#3292)

* tmp log

* remove log

* feat: common oauth

* readme

* perf: sso provider

* remove sso code

* perf: refresh plugins

* feat: add api dataset (#3272)

* add api-dataset

* fix api-dataset

* fix api dataset

* fix ts

* perf: create collection code (#3301)

* tmp log

* remove log

* perf: i18n change

* update version doc

* feat: question guide from chatId

* perf: create collection code

* fix: request api

* fix: request api

* fix: tts auth and response type (#3303)

* perf: md splitter

* fix: tts auth and response type

* fix: api file dataset (#3307)

* perf: api dataset init (#3310)

* perf: collection schema

* perf: api dataset init

* refactor: 团队管理独立页面 (#3302)

* ui: 团队管理独立页面

* 代码优化

* fix

* perf: sync collection and ui check (#3314)

* perf: sync collection

* remove script

* perf: update api server

* perf: api dataset parent

* perf: team ui

* perf: team 18n

* update team ui

* perf: ui check

* perf: i18n

* fix: debug variables & cronjob & system plugin callback load (#3315)

* fix: debug variables & cronjob & system plugin callback load

* fix type

* fix

* fix

* fix: plugin dataset quote;perf: system variables init (#3316)

* fix: plugin dataset quote

* perf: system variables init

* perf: node templates ui;fix: dataset import ui (#3318)

* fix: dataset import ui

* perf: node templates ui

* perf: ui refresh

* feat:套餐改名和套餐跳转配置 (#3309)

* fixing:except Sidebar

* 去除了多余的代码

* 修正了套餐说明的代码

* 修正了误删除的show_git代码

* 修正了名字部分等代码

* 修正了问题,遗留了其他和ui讨论不一致的部分

* 4.8.15 test (#3319)

* remove log

* pref: bill ui

* pref: bill ui

* perf: log

* html渲染文档 (#3270)

* html渲染文档

* 文档有点小问题

* feat: doc (#3322)

* 集合重训练 (#3282)

* rebaser

* 一点补充

* 小问题

* 其他问题修正,删除集合保留文件的参数还没找到...

* reTraining

* delete uesless

* 删除了一行错误代码

* 集合重训练部分

* fixing

* 删除console代码

* feat: navbar item config (#3326)

* perf: custom navbar code;perf: retraining code;feat: api dataset and dataset api doc (#3329)

* feat: api dataset and dataset api doc

* perf: retraining code

* perf: custom navbar code

* fix: ts (#3330)

* fix: ts

* fix: ts

* retraining ui

* perf: api collection filter

* perf: retrining button

---------

Co-authored-by: heheer <heheer@sealos.io>
Co-authored-by: Jiangween <145003935+Jiangween@users.noreply.github.com>
Co-authored-by: papapatrick <109422393+Patrickill@users.noreply.github.com>
2024-12-06 10:56:53 +08:00

354 lines
8.9 KiB
TypeScript

import { TrainingModeEnum } from '@fastgpt/global/core/dataset/constants';
import type { CreateDatasetCollectionParams } from '@fastgpt/global/core/dataset/api.d';
import { MongoDatasetCollection } from './schema';
import {
CollectionWithDatasetType,
DatasetCollectionSchemaType,
DatasetSchemaType
} from '@fastgpt/global/core/dataset/type';
import { MongoDatasetTraining } from '../training/schema';
import { MongoDatasetData } from '../data/schema';
import { delImgByRelatedId } from '../../../common/file/image/controller';
import { deleteDatasetDataVector } from '../../../common/vectorStore/controller';
import { delFileByFileIdList } from '../../../common/file/gridfs/controller';
import { BucketNameEnum } from '@fastgpt/global/common/file/constants';
import { ClientSession } from '../../../common/mongo';
import { createOrGetCollectionTags } from './utils';
import { rawText2Chunks } from '../read';
import { checkDatasetLimit } from '../../../support/permission/teamLimit';
import { predictDataLimitLength } from '../../../../global/core/dataset/utils';
import { mongoSessionRun } from '../../../common/mongo/sessionRun';
import { createTrainingUsage } from '../../../support/wallet/usage/controller';
import { UsageSourceEnum } from '@fastgpt/global/support/wallet/usage/constants';
import { getLLMModel, getVectorModel } from '../../ai/model';
import { pushDataListToTrainingQueue } from '../training/controller';
import { MongoImage } from '../../../common/file/image/schema';
import { hashStr } from '@fastgpt/global/common/string/tools';
export const createCollectionAndInsertData = async ({
dataset,
rawText,
relatedId,
createCollectionParams,
isQAImport = false,
session
}: {
dataset: DatasetSchemaType;
rawText: string;
relatedId?: string;
createCollectionParams: CreateOneCollectionParams;
isQAImport?: boolean;
session?: ClientSession;
}) => {
const teamId = createCollectionParams.teamId;
const tmbId = createCollectionParams.tmbId;
// Chunk split params
const trainingType = createCollectionParams.trainingType || TrainingModeEnum.chunk;
const chunkSize = createCollectionParams.chunkSize;
const chunkSplitter = createCollectionParams.chunkSplitter;
const qaPrompt = createCollectionParams.qaPrompt;
const usageName = createCollectionParams.name;
// 1. split chunks
const chunks = rawText2Chunks({
rawText,
chunkLen: chunkSize,
overlapRatio: trainingType === TrainingModeEnum.chunk ? 0.2 : 0,
customReg: chunkSplitter ? [chunkSplitter] : [],
isQAImport
});
// 2. auth limit
await checkDatasetLimit({
teamId,
insertLen: predictDataLimitLength(trainingType, chunks)
});
const fn = async (session: ClientSession) => {
// 3. create collection
const { _id: collectionId } = await createOneCollection({
...createCollectionParams,
hashRawText: hashStr(rawText),
rawTextLength: rawText.length,
session
});
// 4. create training bill
const { billId } = await createTrainingUsage({
teamId,
tmbId,
appName: usageName,
billSource: UsageSourceEnum.training,
vectorModel: getVectorModel(dataset.vectorModel)?.name,
agentModel: getLLMModel(dataset.agentModel)?.name,
session
});
// 5. insert to training queue
const insertResults = await pushDataListToTrainingQueue({
teamId,
tmbId,
datasetId: dataset._id,
collectionId,
agentModel: dataset.agentModel,
vectorModel: dataset.vectorModel,
trainingMode: trainingType,
prompt: qaPrompt,
billId,
data: chunks.map((item, index) => ({
...item,
chunkIndex: index
})),
session
});
// 6. remove related image ttl
if (relatedId) {
await MongoImage.updateMany(
{
teamId,
'metadata.relatedId': relatedId
},
{
// Remove expiredTime to avoid ttl expiration
$unset: {
expiredTime: 1
}
},
{
session
}
);
}
return {
collectionId,
insertResults
};
};
if (session) {
return fn(session);
}
return mongoSessionRun(fn);
};
export type CreateOneCollectionParams = CreateDatasetCollectionParams & {
teamId: string;
tmbId: string;
session?: ClientSession;
};
export async function createOneCollection({
teamId,
tmbId,
name,
parentId,
datasetId,
type,
trainingType = TrainingModeEnum.chunk,
chunkSize = 512,
chunkSplitter,
qaPrompt,
fileId,
rawLink,
externalFileId,
externalFileUrl,
apiFileId,
hashRawText,
rawTextLength,
metadata = {},
session,
tags,
createTime
}: CreateOneCollectionParams) {
// Create collection tags
const collectionTags = await createOrGetCollectionTags({ tags, teamId, datasetId, session });
// Create collection
const [collection] = await MongoDatasetCollection.create(
[
{
teamId,
tmbId,
parentId: parentId || null,
datasetId,
name,
type,
trainingType,
chunkSize,
chunkSplitter,
qaPrompt,
metadata,
...(fileId ? { fileId } : {}),
...(rawLink ? { rawLink } : {}),
...(externalFileId ? { externalFileId } : {}),
...(externalFileUrl ? { externalFileUrl } : {}),
...(apiFileId ? { apiFileId } : {}),
rawTextLength,
hashRawText,
tags: collectionTags,
createTime
}
],
{ session }
);
return collection;
}
/* delete collection related images/files */
export const delCollectionRelatedSource = async ({
collections,
session
}: {
collections: (CollectionWithDatasetType | DatasetCollectionSchemaType)[];
session: ClientSession;
}) => {
if (collections.length === 0) return;
const teamId = collections[0].teamId;
if (!teamId) return Promise.reject('teamId is not exist');
const fileIdList = collections.map((item) => item?.fileId || '').filter(Boolean);
const relatedImageIds = collections
.map((item) => item?.metadata?.relatedImgId || '')
.filter(Boolean);
// delete files
await delFileByFileIdList({
bucketName: BucketNameEnum.dataset,
fileIdList
});
// delete images
await delImgByRelatedId({
teamId,
relateIds: relatedImageIds,
session
});
};
/**
* delete collection and it related data
*/
export async function delCollection({
collections,
session,
delRelatedSource
}: {
collections: (CollectionWithDatasetType | DatasetCollectionSchemaType)[];
session: ClientSession;
delRelatedSource: boolean;
}) {
if (collections.length === 0) return;
const teamId = collections[0].teamId;
if (!teamId) return Promise.reject('teamId is not exist');
const datasetIds = Array.from(
new Set(
collections.map((item) => {
if (typeof item.datasetId === 'string') {
return String(item.datasetId);
}
return String(item.datasetId._id);
})
)
);
const collectionIds = collections.map((item) => String(item._id));
// delete training data
await MongoDatasetTraining.deleteMany({
teamId,
datasetIds: { $in: datasetIds },
collectionId: { $in: collectionIds }
});
/* file and imgs */
if (delRelatedSource) {
await delCollectionRelatedSource({ collections, session });
}
// delete dataset.datas
await MongoDatasetData.deleteMany(
{ teamId, datasetIds: { $in: datasetIds }, collectionId: { $in: collectionIds } },
{ session }
);
// delete collections
await MongoDatasetCollection.deleteMany(
{
teamId,
_id: { $in: collectionIds }
},
{ session }
);
// no session delete: delete files, vector data
await deleteDatasetDataVector({ teamId, datasetIds, collectionIds });
}
/**
* delete delOnlyCollection
*/
export async function delOnlyCollection({
collections,
session
}: {
collections: (CollectionWithDatasetType | DatasetCollectionSchemaType)[];
session: ClientSession;
}) {
if (collections.length === 0) return;
const teamId = collections[0].teamId;
if (!teamId) return Promise.reject('teamId is not exist');
const datasetIds = Array.from(
new Set(
collections.map((item) => {
if (typeof item.datasetId === 'string') {
return String(item.datasetId);
}
return String(item.datasetId._id);
})
)
);
const collectionIds = collections.map((item) => String(item._id));
// delete training data
await MongoDatasetTraining.deleteMany({
teamId,
datasetIds: { $in: datasetIds },
collectionId: { $in: collectionIds }
});
// delete dataset.datas
await MongoDatasetData.deleteMany(
{ teamId, datasetIds: { $in: datasetIds }, collectionId: { $in: collectionIds } },
{ session }
);
// delete collections
await MongoDatasetCollection.deleteMany(
{
teamId,
_id: { $in: collectionIds }
},
{ session }
);
// no session delete: delete files, vector data
await deleteDatasetDataVector({ teamId, datasetIds, collectionIds });
}