mirror of
https://github.com/KevinMidboe/immich.git
synced 2025-10-29 17:40:28 +00:00
feat(server): CLIP search integration (#1939)
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@@ -7,4 +7,6 @@ export interface MachineLearningInput {
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export interface IMachineLearningRepository {
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tagImage(input: MachineLearningInput): Promise<string[]>;
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detectObjects(input: MachineLearningInput): Promise<string[]>;
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encodeImage(input: MachineLearningInput): Promise<number[]>;
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encodeText(input: string): Promise<number[]>;
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}
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@@ -1,5 +1,6 @@
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import { AssetEntity } from '@app/infra/db/entities';
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import { newMachineLearningRepositoryMock, newSmartInfoRepositoryMock } from '../../test';
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import { newJobRepositoryMock, newMachineLearningRepositoryMock, newSmartInfoRepositoryMock } from '../../test';
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import { IJobRepository } from '../job';
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import { IMachineLearningRepository } from './machine-learning.interface';
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import { ISmartInfoRepository } from './smart-info.repository';
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import { SmartInfoService } from './smart-info.service';
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@@ -11,13 +12,15 @@ const asset = {
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describe(SmartInfoService.name, () => {
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let sut: SmartInfoService;
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let jobMock: jest.Mocked<IJobRepository>;
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let smartMock: jest.Mocked<ISmartInfoRepository>;
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let machineMock: jest.Mocked<IMachineLearningRepository>;
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beforeEach(async () => {
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smartMock = newSmartInfoRepositoryMock();
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jobMock = newJobRepositoryMock();
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machineMock = newMachineLearningRepositoryMock();
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sut = new SmartInfoService(smartMock, machineMock);
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sut = new SmartInfoService(jobMock, smartMock, machineMock);
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});
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it('should work', () => {
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@@ -1,6 +1,6 @@
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import { MACHINE_LEARNING_ENABLED } from '@app/common';
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import { Inject, Injectable, Logger } from '@nestjs/common';
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import { IAssetJob } from '../job';
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import { IAssetJob, IJobRepository, JobName } from '../job';
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import { IMachineLearningRepository } from './machine-learning.interface';
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import { ISmartInfoRepository } from './smart-info.repository';
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@@ -9,6 +9,7 @@ export class SmartInfoService {
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private logger = new Logger(SmartInfoService.name);
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constructor(
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@Inject(IJobRepository) private jobRepository: IJobRepository,
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@Inject(ISmartInfoRepository) private repository: ISmartInfoRepository,
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@Inject(IMachineLearningRepository) private machineLearning: IMachineLearningRepository,
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) {}
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@@ -24,6 +25,7 @@ export class SmartInfoService {
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const tags = await this.machineLearning.tagImage({ thumbnailPath: asset.resizePath });
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if (tags.length > 0) {
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await this.repository.upsert({ assetId: asset.id, tags });
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await this.jobRepository.queue({ name: JobName.SEARCH_INDEX_ASSET, data: { ids: [asset.id] } });
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}
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} catch (error: any) {
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this.logger.error(`Unable to run image tagging pipeline: ${asset.id}`, error?.stack);
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@@ -41,9 +43,26 @@ export class SmartInfoService {
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const objects = await this.machineLearning.detectObjects({ thumbnailPath: asset.resizePath });
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if (objects.length > 0) {
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await this.repository.upsert({ assetId: asset.id, objects });
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await this.jobRepository.queue({ name: JobName.SEARCH_INDEX_ASSET, data: { ids: [asset.id] } });
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}
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} catch (error: any) {
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this.logger.error(`Unable run object detection pipeline: ${asset.id}`, error?.stack);
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}
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}
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async handleEncodeClip(data: IAssetJob) {
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const { asset } = data;
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if (!MACHINE_LEARNING_ENABLED || !asset.resizePath) {
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return;
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}
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try {
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const clipEmbedding = await this.machineLearning.encodeImage({ thumbnailPath: asset.resizePath });
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await this.repository.upsert({ assetId: asset.id, clipEmbedding: clipEmbedding });
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await this.jobRepository.queue({ name: JobName.SEARCH_INDEX_ASSET, data: { ids: [asset.id] } });
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} catch (error: any) {
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this.logger.error(`Unable run clip encoding pipeline: ${asset.id}`, error?.stack);
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}
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}
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}
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