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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
105 lines
2.5 KiB
TypeScript
105 lines
2.5 KiB
TypeScript
import { type AppSchema } from '@fastgpt/global/core/app/type';
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import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
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import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
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import { getLLMModel } from '../ai/model';
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import { MongoApp } from './schema';
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export const beforeUpdateAppFormat = <T extends AppSchema['modules'] | undefined>({
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nodes,
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isPlugin
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}: {
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nodes: T;
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isPlugin: boolean;
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}) => {
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if (nodes) {
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// Check dataset maxTokens
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if (isPlugin) {
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let maxTokens = 16000;
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nodes.forEach((item) => {
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if (
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item.flowNodeType === FlowNodeTypeEnum.chatNode ||
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item.flowNodeType === FlowNodeTypeEnum.tools
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) {
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const model =
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item.inputs.find((item) => item.key === NodeInputKeyEnum.aiModel)?.value || '';
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const chatModel = getLLMModel(model);
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const quoteMaxToken = chatModel.quoteMaxToken || 16000;
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maxTokens = Math.max(maxTokens, quoteMaxToken);
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}
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});
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nodes.forEach((item) => {
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if (item.flowNodeType === FlowNodeTypeEnum.datasetSearchNode) {
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item.inputs.forEach((input) => {
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if (input.key === NodeInputKeyEnum.datasetMaxTokens) {
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const val = input.value as number;
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if (val > maxTokens) {
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input.value = maxTokens;
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}
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}
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});
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}
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});
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}
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}
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return {
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nodes
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};
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};
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/* Get apps */
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export async function findAppAndAllChildren({
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teamId,
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appId,
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fields
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}: {
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teamId: string;
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appId: string;
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fields?: string;
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}): Promise<AppSchema[]> {
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const find = async (id: string) => {
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const children = await MongoApp.find(
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{
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teamId,
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parentId: id
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},
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fields
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).lean();
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let apps = children;
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for (const child of children) {
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const grandChildrenIds = await find(child._id);
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apps = apps.concat(grandChildrenIds);
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}
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return apps;
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};
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const [app, childDatasets] = await Promise.all([MongoApp.findById(appId, fields), find(appId)]);
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if (!app) {
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return Promise.reject('Dataset not found');
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}
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return [app, ...childDatasets];
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}
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export const getAppBasicInfoByIds = async ({ teamId, ids }: { teamId: string; ids: string[] }) => {
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const apps = await MongoApp.find(
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{
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teamId,
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_id: { $in: ids }
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},
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'_id name avatar'
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).lean();
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return apps.map((item) => ({
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id: item._id,
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name: item.name,
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avatar: item.avatar
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}));
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};
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