289 lines
8.3 KiB
Dart
289 lines
8.3 KiB
Dart
import "dart:async";
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import "dart:collection";
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import "dart:io";
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import "package:clip_ggml/clip_ggml.dart";
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import "package:computer/computer.dart";
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import "package:logging/logging.dart";
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import "package:photos/core/configuration.dart";
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import "package:photos/core/event_bus.dart";
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import "package:photos/db/files_db.dart";
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import "package:photos/events/file_indexed_event.dart";
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import "package:photos/events/file_uploaded_event.dart";
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import "package:photos/events/sync_status_update_event.dart";
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import "package:photos/models/embedding.dart";
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import "package:photos/models/file/file.dart";
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import "package:photos/services/semantic_search/embedding_store.dart";
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import "package:photos/services/semantic_search/model_loader.dart";
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import "package:photos/utils/local_settings.dart";
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import "package:photos/utils/thumbnail_util.dart";
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import "package:shared_preferences/shared_preferences.dart";
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class SemanticSearchService {
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SemanticSearchService._privateConstructor();
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static final SemanticSearchService instance =
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SemanticSearchService._privateConstructor();
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static final Computer _computer = Computer.shared();
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static const kModelName = "ggml-clip";
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static const kEmbeddingLength = 512;
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static const kScoreThreshold = 0.23;
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final _logger = Logger("SemanticSearchService");
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final _queue = Queue<EnteFile>();
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bool hasLoaded = false;
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bool isComputingEmbeddings = false;
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Future<List<EnteFile>>? _ongoingRequest;
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PendingQuery? _nextQuery;
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final _cachedEmbeddings = <Embedding>[];
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Future<void> init(SharedPreferences preferences) async {
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if (Platform.isIOS) {
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return;
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}
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await EmbeddingStore.instance.init(preferences);
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await ModelLoader.instance.init(_computer);
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_cacheEmbeddings();
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Bus.instance.on<SyncStatusUpdate>().listen((event) async {
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if (event.status == SyncStatus.diffSynced) {
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await EmbeddingStore.instance.pullEmbeddings();
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_cacheEmbeddings();
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}
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});
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if (Configuration.instance.hasConfiguredAccount()) {
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EmbeddingStore.instance.pushEmbeddings();
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}
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_loadModels().then((v) {
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startBackFill();
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});
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Bus.instance.on<FileUploadedEvent>().listen((event) async {
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addToQueue(event.file);
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});
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}
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Future<List<EnteFile>> search(String query) async {
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if (Platform.isIOS || !LocalSettings.instance.hasEnabledMagicSearch()) {
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return [];
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}
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if (_ongoingRequest == null) {
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_ongoingRequest = getMatchingFiles(query).then((result) {
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_ongoingRequest = null;
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if (_nextQuery != null) {
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final next = _nextQuery;
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_nextQuery = null;
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search(next!.query).then((nextResult) {
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next.completer.complete(nextResult);
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});
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}
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return result;
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});
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return _ongoingRequest!;
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} else {
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// If there's an ongoing request, create or replace the nextCompleter.
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_nextQuery?.completer.future
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.timeout(const Duration(seconds: 0)); // Cancels the previous future.
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_nextQuery = PendingQuery(query, Completer<List<EnteFile>>());
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return _nextQuery!.completer.future;
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}
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}
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Future<List<EnteFile>> getMatchingFiles(String query) async {
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_logger.info("Searching for " + query);
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var startTime = DateTime.now();
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final textEmbedding = await _computer.compute(
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createTextEmbedding,
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param: {
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"text": query,
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},
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taskName: "createTextEmbedding",
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);
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var endTime = DateTime.now();
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_logger.info(
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"createTextEmbedding took: " +
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(endTime.millisecondsSinceEpoch - startTime.millisecondsSinceEpoch)
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.toString() +
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"ms",
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);
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startTime = DateTime.now();
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final queryResults = <QueryResult>[];
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for (final embedding in _cachedEmbeddings) {
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final score = computeScore({
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"imageEmbedding": embedding.embedding,
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"textEmbedding": textEmbedding,
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});
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if (score >= kScoreThreshold) {
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queryResults.add(QueryResult(embedding.fileID, score));
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}
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}
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queryResults.sort((first, second) => second.score.compareTo(first.score));
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endTime = DateTime.now();
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_logger.info(
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"computingScores took: " +
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(endTime.millisecondsSinceEpoch - startTime.millisecondsSinceEpoch)
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.toString() +
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"ms",
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);
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startTime = DateTime.now();
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final filesMap = await FilesDB.instance
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.getFilesFromIDs(queryResults.map((e) => e.id).toList());
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final results = <EnteFile>[];
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for (final result in queryResults) {
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if (filesMap.containsKey(result.id)) {
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results.add(filesMap[result.id]!);
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}
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}
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endTime = DateTime.now();
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_logger.info(
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"Fetching files took: " +
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(endTime.millisecondsSinceEpoch - startTime.millisecondsSinceEpoch)
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.toString() +
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"ms",
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);
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_logger.info(results.length.toString() + " results");
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return results;
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}
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void addToQueue(EnteFile file) {
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if (!LocalSettings.instance.hasEnabledMagicSearch()) {
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return;
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}
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_logger.info("Adding " + file.toString() + " to the queue");
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_queue.add(file);
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_pollQueue();
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}
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Future<IndexStatus> getIndexStatus() async {
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final embeddings = await FilesDB.instance.getAllEmbeddingsV2();
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return IndexStatus(embeddings.length, _queue.length);
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}
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Future<void> _loadModels() async {
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await ModelLoader.instance.loadImageModel();
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await ModelLoader.instance.loadTextModel();
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hasLoaded = true;
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}
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Future<void> startBackFill() async {
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if (!LocalSettings.instance.hasEnabledMagicSearch()) {
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return;
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}
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final files = await FilesDB.instance.getFilesWithoutEmbeddings();
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final ownerID = Configuration.instance.getUserID();
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files.removeWhere((f) => f.ownerID != ownerID);
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_logger.info(files.length.toString() + " pending to be embedded");
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_queue.addAll(files);
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_pollQueue();
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}
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Future<void> clearQueue() async {
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_queue.clear();
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}
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Future<void> _pollQueue() async {
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if (isComputingEmbeddings) {
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return;
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}
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isComputingEmbeddings = true;
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while (_queue.isNotEmpty) {
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await _computeImageEmbedding(_queue.removeLast());
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}
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isComputingEmbeddings = false;
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}
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Future<void> _computeImageEmbedding(EnteFile file) async {
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if (!hasLoaded) {
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return;
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}
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try {
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final filePath = (await getThumbnailForUploadedFile(file))!.path;
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_logger.info("Running clip over $file");
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final startTime = DateTime.now();
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final result = await _computer.compute(
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createImageEmbedding,
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param: {
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"imagePath": filePath,
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},
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taskName: "createImageEmbedding",
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) as List<double>;
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final endTime = DateTime.now();
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_logger.info(
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"createImageEmbedding took: ${(endTime.millisecondsSinceEpoch - startTime.millisecondsSinceEpoch)}ms",
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);
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if (result.length != kEmbeddingLength) {
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_logger.severe("Discovered incorrect embedding for $file - $result");
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return;
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}
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final embedding = Embedding(
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file.uploadedFileID!,
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kModelName,
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result,
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);
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await EmbeddingStore.instance.storeEmbedding(
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file,
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embedding,
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);
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Bus.instance.fire(FileIndexedEvent());
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_cachedEmbeddings.add(embedding);
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} catch (e, s) {
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_logger.severe(e, s);
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}
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}
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Future<void> _cacheEmbeddings() async {
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final startTime = DateTime.now();
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final embeddings = await FilesDB.instance.getAllEmbeddingsV2();
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_cachedEmbeddings.clear();
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_cachedEmbeddings.addAll(embeddings);
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final endTime = DateTime.now();
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_logger.info(
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"Loading ${embeddings.length} embeddings took: ${(endTime.millisecondsSinceEpoch - startTime.millisecondsSinceEpoch)}ms",
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);
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}
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}
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List<double> createImageEmbedding(Map args) {
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return CLIP.createImageEmbedding(args["imagePath"]);
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}
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List<double> createTextEmbedding(Map args) {
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return CLIP.createTextEmbedding(args["text"]);
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}
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double computeScore(Map args) {
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return CLIP.computeScore(
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args["imageEmbedding"] as List<double>,
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args["textEmbedding"] as List<double>,
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);
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}
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class QueryResult {
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final int id;
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final double score;
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QueryResult(this.id, this.score);
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}
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class PendingQuery {
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final String query;
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final Completer<List<EnteFile>> completer;
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PendingQuery(this.query, this.completer);
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}
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class IndexStatus {
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final int indexedItems, pendingItems;
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IndexStatus(this.indexedItems, this.pendingItems);
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}
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