mirror of
https://github.com/labring/FastGPT.git
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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
89 lines
2.5 KiB
TypeScript
89 lines
2.5 KiB
TypeScript
import { type LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
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import { queryExtension } from '../../ai/functions/queryExtension';
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import { type ChatItemType } from '@fastgpt/global/core/chat/type';
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import { hashStr } from '@fastgpt/global/common/string/tools';
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import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
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export const datasetSearchQueryExtension = async ({
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query,
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extensionModel,
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extensionBg = '',
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histories = []
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}: {
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query: string;
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extensionModel?: LLMModelItemType;
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extensionBg?: string;
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histories?: ChatItemType[];
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}) => {
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const filterSamQuery = (queries: string[]) => {
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const set = new Set<string>();
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const filterSameQueries = queries.filter((item) => {
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// 删除所有的标点符号与空格等，只对文本进行比较
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const str = hashStr(item.replace(/[^\p{L}\p{N}]/gu, ''));
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if (set.has(str)) return false;
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set.add(str);
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return true;
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});
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return filterSameQueries;
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};
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let { queries, rewriteQuery, alreadyExtension } = (() => {
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// concat query
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let rewriteQuery =
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histories.length > 0
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? `${histories
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.map((item) => {
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return `${item.obj}: ${chatValue2RuntimePrompt(item.value).text}`;
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})
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.join('\n')}
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Human: ${query}
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`
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: query;
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/* if query already extension, direct parse */
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try {
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const jsonParse = JSON.parse(query);
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const queries: string[] = Array.isArray(jsonParse) ? filterSamQuery(jsonParse) : [query];
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const alreadyExtension = Array.isArray(jsonParse);
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return {
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queries,
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rewriteQuery: alreadyExtension ? queries.join('\n') : rewriteQuery,
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alreadyExtension: alreadyExtension
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};
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} catch (error) {
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return {
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queries: [query],
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rewriteQuery,
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alreadyExtension: false
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};
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}
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})();
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// ai extension
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const aiExtensionResult = await (async () => {
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if (!extensionModel || alreadyExtension) return;
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const result = await queryExtension({
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chatBg: extensionBg,
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query,
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histories,
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model: extensionModel.model
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});
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if (result.extensionQueries?.length === 0) return;
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return result;
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})();
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const extensionQueries = filterSamQuery(aiExtensionResult?.extensionQueries || []);
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if (aiExtensionResult) {
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queries = filterSamQuery(queries.concat(extensionQueries));
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rewriteQuery = queries.join('\n');
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}
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return {
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extensionQueries,
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concatQueries: queries,
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rewriteQuery,
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aiExtensionResult
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};
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};
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