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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
84 lines
2.4 KiB
TypeScript
84 lines
2.4 KiB
TypeScript
/* vector crud */
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import { PgVectorCtrl } from './pg';
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import { ObVectorCtrl } from './oceanbase';
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import { getVectorsByText } from '../../core/ai/embedding';
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import { type DelDatasetVectorCtrlProps, type InsertVectorProps } from './controller.d';
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import { type EmbeddingModelItemType } from '@fastgpt/global/core/ai/model.d';
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import { MILVUS_ADDRESS, PG_ADDRESS, OCEANBASE_ADDRESS } from './constants';
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import { MilvusCtrl } from './milvus';
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import { setRedisCache, getRedisCache, delRedisCache, CacheKeyEnum } from '../redis/cache';
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import { throttle } from 'lodash';
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import { retryFn } from '@fastgpt/global/common/system/utils';
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const getVectorObj = () => {
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if (PG_ADDRESS) return new PgVectorCtrl();
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if (OCEANBASE_ADDRESS) return new ObVectorCtrl();
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if (MILVUS_ADDRESS) return new MilvusCtrl();
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return new PgVectorCtrl();
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};
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const getChcheKey = (teamId: string) => `${CacheKeyEnum.team_vector_count}:${teamId}`;
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const onDelCache = throttle((teamId: string) => delRedisCache(getChcheKey(teamId)), 30000, {
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leading: true,
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trailing: true
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});
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const Vector = getVectorObj();
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export const initVectorStore = Vector.init;
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export const recallFromVectorStore = Vector.embRecall;
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export const getVectorDataByTime = Vector.getVectorDataByTime;
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export const getVectorCountByTeamId = async (teamId: string) => {
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const key = getChcheKey(teamId);
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const countStr = await getRedisCache(key);
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if (countStr) {
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return Number(countStr);
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}
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const count = await Vector.getVectorCountByTeamId(teamId);
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await setRedisCache(key, count, 30 * 60);
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return count;
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};
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export const getVectorCountByDatasetId = Vector.getVectorCountByDatasetId;
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export const getVectorCountByCollectionId = Vector.getVectorCountByCollectionId;
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export const insertDatasetDataVector = async ({
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model,
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query,
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...props
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}: InsertVectorProps & {
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query: string;
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model: EmbeddingModelItemType;
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}) => {
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return retryFn(async () => {
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const { vectors, tokens } = await getVectorsByText({
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model,
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input: query,
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type: 'db'
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});
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const { insertId } = await Vector.insert({
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...props,
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vector: vectors[0]
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});
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onDelCache(props.teamId);
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return {
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tokens,
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insertId
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};
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});
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};
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export const deleteDatasetDataVector = async (props: DelDatasetVectorCtrlProps) => {
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const result = await Vector.delete(props);
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onDelCache(props.teamId);
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return result;
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};
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