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Code Issues Packages Projects Releases Wiki Activity

feat(server): CLIP search integration (#1939)

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Alex
2023-03-18 08:44:42 -05:00
committed by GitHub
parent 0d436db3ea
commit f56eaae019
46 changed files with 673 additions and 773 deletions
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21
server/libs/domain/src/smart-info/smart-info.service.ts
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@@ -1,6 +1,6 @@
import { MACHINE_LEARNING_ENABLED } from '@app/common';
import { Inject, Injectable, Logger } from '@nestjs/common';
import { IAssetJob } from '../job';
import { IAssetJob, IJobRepository, JobName } from '../job';
import { IMachineLearningRepository } from './machine-learning.interface';
import { ISmartInfoRepository } from './smart-info.repository';
@@ -9,6 +9,7 @@ export class SmartInfoService {
private logger = new Logger(SmartInfoService.name);
constructor(
@Inject(IJobRepository) private jobRepository: IJobRepository,
@Inject(ISmartInfoRepository) private repository: ISmartInfoRepository,
@Inject(IMachineLearningRepository) private machineLearning: IMachineLearningRepository,
) {}
@@ -24,6 +25,7 @@ export class SmartInfoService {
const tags = await this.machineLearning.tagImage({ thumbnailPath: asset.resizePath });
if (tags.length > 0) {
await this.repository.upsert({ assetId: asset.id, tags });
await this.jobRepository.queue({ name: JobName.SEARCH_INDEX_ASSET, data: { ids: [asset.id] } });
}
} catch (error: any) {
this.logger.error(`Unable to run image tagging pipeline: ${asset.id}`, error?.stack);
@@ -41,9 +43,26 @@ export class SmartInfoService {
const objects = await this.machineLearning.detectObjects({ thumbnailPath: asset.resizePath });
if (objects.length > 0) {
await this.repository.upsert({ assetId: asset.id, objects });
await this.jobRepository.queue({ name: JobName.SEARCH_INDEX_ASSET, data: { ids: [asset.id] } });
}
} catch (error: any) {
this.logger.error(`Unable run object detection pipeline: ${asset.id}`, error?.stack);
}
}
async handleEncodeClip(data: IAssetJob) {
const { asset } = data;
if (!MACHINE_LEARNING_ENABLED || !asset.resizePath) {
return;
}
try {
const clipEmbedding = await this.machineLearning.encodeImage({ thumbnailPath: asset.resizePath });
await this.repository.upsert({ assetId: asset.id, clipEmbedding: clipEmbedding });
await this.jobRepository.queue({ name: JobName.SEARCH_INDEX_ASSET, data: { ids: [asset.id] } });
} catch (error: any) {
this.logger.error(`Unable run clip encoding pipeline: ${asset.id}`, error?.stack);
}
}
}