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https://github.com/KevinMidboe/immich.git
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feat(ml)!: customizable ML settings (#3891)
* consolidated endpoints, added live configuration * added ml settings to server * added settings dashboard * updated deps, fixed typos * simplified modelconfig updated tests * Added ml setting accordion for admin page updated tests * merge `clipText` and `clipVision` * added face distance setting clarified setting * add clip mode in request, dropdown for face models * polished ml settings updated descriptions * update clip field on error * removed unused import * add description for image classification threshold * pin safetensors for arm wheel updated poetry lock * moved dto * set model type only in ml repository * revert form-data package install use fetch instead of axios * added slotted description with link updated facial recognition description clarified effect of disabling tasks * validation before model load * removed unnecessary getconfig call * added migration * updated api updated api updated api --------- Co-authored-by: Alex Tran <alex.tran1502@gmail.com>
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@@ -86,6 +86,7 @@ export interface ISearchRepository {
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deleteAssets(ids: string[]): Promise<void>;
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deleteFaces(ids: string[]): Promise<void>;
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deleteAllFaces(): Promise<number>;
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updateCLIPField(num_dim: number): Promise<void>;
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searchAlbums(query: string, filters: SearchFilter): Promise<SearchResult<AlbumEntity>>;
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searchAssets(query: string, filters: SearchFilter): Promise<SearchResult<AssetEntity>>;
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@@ -121,15 +121,18 @@ export class SearchService {
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await this.configCore.requireFeature(FeatureFlag.SEARCH);
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const query = dto.q || dto.query || '*';
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const hasClip = machineLearning.enabled && machineLearning.clipEncodeEnabled;
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const hasClip = machineLearning.enabled && machineLearning.clip.enabled;
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const strategy = dto.clip && hasClip ? SearchStrategy.CLIP : SearchStrategy.TEXT;
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const filters = { userId: authUser.id, ...dto };
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let assets: SearchResult<AssetEntity>;
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switch (strategy) {
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case SearchStrategy.CLIP:
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const clip = await this.machineLearning.encodeText(machineLearning.url, query);
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assets = await this.searchRepository.vectorSearch(clip, filters);
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const {
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machineLearning: { clip },
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} = await this.configCore.getConfig();
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const embedding = await this.machineLearning.encodeText(machineLearning.url, { text: query }, clip);
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assets = await this.searchRepository.vectorSearch(embedding, filters);
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break;
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case SearchStrategy.TEXT:
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default:
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