423 lines
13 KiB
Dart
423 lines
13 KiB
Dart
import "dart:async";
|
|
import "dart:collection";
|
|
|
|
import "package:computer/computer.dart";
|
|
import "package:logging/logging.dart";
|
|
import "package:photos/core/cache/lru_map.dart";
|
|
import "package:photos/core/configuration.dart";
|
|
import "package:photos/core/event_bus.dart";
|
|
import "package:photos/db/embeddings_db.dart";
|
|
import "package:photos/db/files_db.dart";
|
|
import "package:photos/events/diff_sync_complete_event.dart";
|
|
import 'package:photos/events/embedding_updated_event.dart';
|
|
import "package:photos/events/file_uploaded_event.dart";
|
|
import "package:photos/models/embedding.dart";
|
|
import "package:photos/models/file/file.dart";
|
|
import "package:photos/services/collections_service.dart";
|
|
import "package:photos/services/semantic_search/embedding_store.dart";
|
|
import "package:photos/services/semantic_search/frameworks/ggml.dart";
|
|
import "package:photos/services/semantic_search/frameworks/ml_framework.dart";
|
|
import 'package:photos/services/semantic_search/frameworks/onnx/onnx.dart';
|
|
import "package:photos/utils/debouncer.dart";
|
|
import "package:photos/utils/device_info.dart";
|
|
import "package:photos/utils/local_settings.dart";
|
|
import "package:photos/utils/thumbnail_util.dart";
|
|
|
|
class SemanticSearchService {
|
|
SemanticSearchService._privateConstructor();
|
|
|
|
static final SemanticSearchService instance =
|
|
SemanticSearchService._privateConstructor();
|
|
static final Computer _computer = Computer.shared();
|
|
static final LRUMap<String, List<double>> _queryCache = LRUMap(20);
|
|
|
|
static const kEmbeddingLength = 512;
|
|
static const kScoreThreshold = 0.23;
|
|
static const kShouldPushEmbeddings = true;
|
|
static const kDebounceDuration = Duration(milliseconds: 4000);
|
|
|
|
final _logger = Logger("SemanticSearchService");
|
|
final _queue = Queue<EnteFile>();
|
|
final _frameworkInitialization = Completer<bool>();
|
|
final _embeddingLoaderDebouncer =
|
|
Debouncer(kDebounceDuration, executionInterval: kDebounceDuration);
|
|
|
|
late Model _currentModel;
|
|
late MLFramework _mlFramework;
|
|
bool _hasInitialized = false;
|
|
bool _isComputingEmbeddings = false;
|
|
bool _isSyncing = false;
|
|
Future<List<EnteFile>>? _ongoingRequest;
|
|
List<Embedding> _cachedEmbeddings = <Embedding>[];
|
|
PendingQuery? _nextQuery;
|
|
Completer<void> _userInteraction = Completer<void>();
|
|
|
|
get hasInitialized => _hasInitialized;
|
|
|
|
void resumeIndexing() {
|
|
_logger.info("Resuming indexing");
|
|
_userInteraction.complete();
|
|
}
|
|
|
|
void pauseIndexing() {
|
|
if (_userInteraction.isCompleted) {
|
|
_logger.info("Pausing indexing");
|
|
_userInteraction = Completer<void>();
|
|
}
|
|
}
|
|
|
|
Future<void> init({bool shouldSyncImmediately = false}) async {
|
|
if (!LocalSettings.instance.hasEnabledMagicSearch()) {
|
|
return;
|
|
}
|
|
if (_hasInitialized) {
|
|
_logger.info("Initialized already");
|
|
return;
|
|
}
|
|
_hasInitialized = true;
|
|
final shouldDownloadOverMobileData =
|
|
Configuration.instance.shouldBackupOverMobileData();
|
|
_currentModel = await _getCurrentModel();
|
|
_mlFramework = _currentModel == Model.onnxClip
|
|
? ONNX(shouldDownloadOverMobileData)
|
|
: GGML(shouldDownloadOverMobileData);
|
|
await EmbeddingsDB.instance.init();
|
|
await EmbeddingStore.instance.init();
|
|
await _loadEmbeddings();
|
|
Bus.instance.on<EmbeddingUpdatedEvent>().listen((event) {
|
|
_embeddingLoaderDebouncer.run(() async {
|
|
await _loadEmbeddings();
|
|
});
|
|
});
|
|
Bus.instance.on<DiffSyncCompleteEvent>().listen((event) {
|
|
// Diff sync is complete, we can now pull embeddings from remote
|
|
unawaited(sync());
|
|
});
|
|
if (Configuration.instance.hasConfiguredAccount() &&
|
|
kShouldPushEmbeddings) {
|
|
unawaited(EmbeddingStore.instance.pushEmbeddings());
|
|
}
|
|
|
|
// ignore: unawaited_futures
|
|
_loadModels().then((v) async {
|
|
_logger.info("Getting text embedding");
|
|
await _getTextEmbedding("warm up text encoder");
|
|
_logger.info("Got text embedding");
|
|
});
|
|
// Adding to queue only on init?
|
|
Bus.instance.on<FileUploadedEvent>().listen((event) async {
|
|
_addToQueue(event.file);
|
|
});
|
|
if (shouldSyncImmediately) {
|
|
unawaited(sync());
|
|
}
|
|
}
|
|
|
|
Future<void> release() async {
|
|
if (_frameworkInitialization.isCompleted) {
|
|
await _mlFramework.release();
|
|
}
|
|
}
|
|
|
|
Future<void> sync() async {
|
|
if (_isSyncing) {
|
|
return;
|
|
}
|
|
_isSyncing = true;
|
|
await EmbeddingStore.instance.pullEmbeddings(_currentModel);
|
|
await _backFill();
|
|
_isSyncing = false;
|
|
}
|
|
|
|
Future<List<EnteFile>> search(String query) async {
|
|
if (!LocalSettings.instance.hasEnabledMagicSearch() ||
|
|
!_frameworkInitialization.isCompleted) {
|
|
return [];
|
|
}
|
|
if (_ongoingRequest == null) {
|
|
_ongoingRequest = _getMatchingFiles(query).then((result) {
|
|
_ongoingRequest = null;
|
|
if (_nextQuery != null) {
|
|
final next = _nextQuery;
|
|
_nextQuery = null;
|
|
search(next!.query).then((nextResult) {
|
|
next.completer.complete(nextResult);
|
|
});
|
|
}
|
|
|
|
return result;
|
|
});
|
|
return _ongoingRequest!;
|
|
} else {
|
|
// If there's an ongoing request, create or replace the nextCompleter.
|
|
_logger.info("Queuing query $query");
|
|
await _nextQuery?.completer.future
|
|
.timeout(const Duration(seconds: 0)); // Cancels the previous future.
|
|
_nextQuery = PendingQuery(query, Completer<List<EnteFile>>());
|
|
return _nextQuery!.completer.future;
|
|
}
|
|
}
|
|
|
|
Future<IndexStatus> getIndexStatus() async {
|
|
return IndexStatus(
|
|
_cachedEmbeddings.length,
|
|
(await _getFileIDsToBeIndexed()).length,
|
|
);
|
|
}
|
|
|
|
InitializationState getFrameworkInitializationState() {
|
|
if (!_hasInitialized) {
|
|
return InitializationState.notInitialized;
|
|
}
|
|
return _mlFramework.initializationState;
|
|
}
|
|
|
|
Future<void> clearIndexes() async {
|
|
await EmbeddingStore.instance.clearEmbeddings(_currentModel);
|
|
_logger.info("Indexes cleared for $_currentModel");
|
|
}
|
|
|
|
Future<void> _loadEmbeddings() async {
|
|
_logger.info("Pulling cached embeddings");
|
|
final startTime = DateTime.now();
|
|
_cachedEmbeddings = await EmbeddingsDB.instance.getAll(_currentModel);
|
|
final endTime = DateTime.now();
|
|
_logger.info(
|
|
"Loading ${_cachedEmbeddings.length} took: ${(endTime.millisecondsSinceEpoch - startTime.millisecondsSinceEpoch)}ms",
|
|
);
|
|
_logger.info("Cached embeddings: " + _cachedEmbeddings.length.toString());
|
|
}
|
|
|
|
Future<void> _backFill() async {
|
|
if (!LocalSettings.instance.hasEnabledMagicSearch() ||
|
|
!MLFramework.kImageEncoderEnabled) {
|
|
return;
|
|
}
|
|
await _frameworkInitialization.future;
|
|
_logger.info("Attempting backfill for image embeddings");
|
|
final fileIDs = await _getFileIDsToBeIndexed();
|
|
final files = await FilesDB.instance.getUploadedFiles(fileIDs);
|
|
_logger.info(files.length.toString() + " to be embedded");
|
|
// await _cacheThumbnails(files);
|
|
_queue.addAll(files);
|
|
unawaited(_pollQueue());
|
|
}
|
|
|
|
Future<void> _cacheThumbnails(List<EnteFile> files) async {
|
|
int counter = 0;
|
|
const batchSize = 100;
|
|
for (var i = 0; i < files.length;) {
|
|
final futures = <Future>[];
|
|
for (var j = 0; j < batchSize && i < files.length; j++, i++) {
|
|
futures.add(getThumbnail(files[i]));
|
|
}
|
|
await Future.wait(futures);
|
|
counter += futures.length;
|
|
_logger.info("$counter/${files.length} thumbnails cached");
|
|
}
|
|
}
|
|
|
|
Future<List<int>> _getFileIDsToBeIndexed() async {
|
|
final uploadedFileIDs = await FilesDB.instance
|
|
.getOwnedFileIDs(Configuration.instance.getUserID()!);
|
|
final embeddedFileIDs = _cachedEmbeddings.map((e) => e.fileID).toSet();
|
|
uploadedFileIDs.removeWhere(
|
|
(id) => embeddedFileIDs.contains(id),
|
|
);
|
|
return uploadedFileIDs;
|
|
}
|
|
|
|
Future<void> clearQueue() async {
|
|
_queue.clear();
|
|
}
|
|
|
|
Future<List<EnteFile>> _getMatchingFiles(String query) async {
|
|
final textEmbedding = await _getTextEmbedding(query);
|
|
|
|
final queryResults = await _getScores(textEmbedding);
|
|
|
|
final filesMap = await FilesDB.instance
|
|
.getFilesFromIDs(queryResults.map((e) => e.id).toList());
|
|
final results = <EnteFile>[];
|
|
|
|
final ignoredCollections =
|
|
CollectionsService.instance.getHiddenCollectionIds();
|
|
for (final result in queryResults) {
|
|
final file = filesMap[result.id];
|
|
if (file != null && !ignoredCollections.contains(file.collectionID)) {
|
|
results.add(filesMap[result.id]!);
|
|
}
|
|
}
|
|
|
|
_logger.info(results.length.toString() + " results");
|
|
|
|
return results;
|
|
}
|
|
|
|
void _addToQueue(EnteFile file) {
|
|
if (!LocalSettings.instance.hasEnabledMagicSearch()) {
|
|
return;
|
|
}
|
|
_logger.info("Adding " + file.toString() + " to the queue");
|
|
_queue.add(file);
|
|
_pollQueue();
|
|
}
|
|
|
|
Future<void> _loadModels() async {
|
|
_logger.info("Initializing ML framework");
|
|
try {
|
|
await _mlFramework.init();
|
|
_frameworkInitialization.complete(true);
|
|
} catch (e, s) {
|
|
_logger.severe("ML framework initialization failed", e, s);
|
|
}
|
|
_logger.info("ML framework initialized");
|
|
}
|
|
|
|
Future<void> _pollQueue() async {
|
|
if (_isComputingEmbeddings) {
|
|
return;
|
|
}
|
|
_isComputingEmbeddings = true;
|
|
|
|
while (_queue.isNotEmpty) {
|
|
await computeImageEmbedding(_queue.removeLast());
|
|
}
|
|
|
|
_isComputingEmbeddings = false;
|
|
}
|
|
|
|
Future<void> computeImageEmbedding(EnteFile file) async {
|
|
if (!MLFramework.kImageEncoderEnabled) {
|
|
return;
|
|
}
|
|
if (!_frameworkInitialization.isCompleted) {
|
|
return;
|
|
}
|
|
if (!_userInteraction.isCompleted) {
|
|
_logger.info("Waiting for user interactions to stop...");
|
|
await _userInteraction.future;
|
|
}
|
|
try {
|
|
final thumbnail = await getThumbnailForUploadedFile(file);
|
|
if (thumbnail == null) {
|
|
_logger.warning("Could not get thumbnail for $file");
|
|
return;
|
|
}
|
|
final filePath = thumbnail.path;
|
|
_logger.info("Running clip over $file");
|
|
final result = await _mlFramework.getImageEmbedding(filePath);
|
|
if (result.length != kEmbeddingLength) {
|
|
_logger.severe("Discovered incorrect embedding for $file - $result");
|
|
return;
|
|
}
|
|
|
|
final embedding = Embedding(
|
|
fileID: file.uploadedFileID!,
|
|
model: _currentModel,
|
|
embedding: result,
|
|
);
|
|
await EmbeddingStore.instance.storeEmbedding(
|
|
file,
|
|
embedding,
|
|
);
|
|
} catch (e, s) {
|
|
_logger.severe(e, s);
|
|
}
|
|
}
|
|
|
|
Future<List<double>> _getTextEmbedding(String query) async {
|
|
_logger.info("Searching for " + query);
|
|
final cachedResult = _queryCache.get(query);
|
|
if (cachedResult != null) {
|
|
return cachedResult;
|
|
}
|
|
try {
|
|
final result = await _mlFramework.getTextEmbedding(query);
|
|
_queryCache.put(query, result);
|
|
return result;
|
|
} catch (e) {
|
|
_logger.severe("Could not get text embedding", e);
|
|
return [];
|
|
}
|
|
}
|
|
|
|
Future<List<QueryResult>> _getScores(List<double> textEmbedding) async {
|
|
final startTime = DateTime.now();
|
|
final List<QueryResult> queryResults = await _computer.compute(
|
|
computeBulkScore,
|
|
param: {
|
|
"imageEmbeddings": _cachedEmbeddings,
|
|
"textEmbedding": textEmbedding,
|
|
},
|
|
taskName: "computeBulkScore",
|
|
);
|
|
final endTime = DateTime.now();
|
|
_logger.info(
|
|
"computingScores took: " +
|
|
(endTime.millisecondsSinceEpoch - startTime.millisecondsSinceEpoch)
|
|
.toString() +
|
|
"ms",
|
|
);
|
|
return queryResults;
|
|
}
|
|
|
|
Future<Model> _getCurrentModel() async {
|
|
if (await isGrapheneOS()) {
|
|
return Model.ggmlClip;
|
|
} else {
|
|
return Model.onnxClip;
|
|
}
|
|
}
|
|
}
|
|
|
|
List<QueryResult> computeBulkScore(Map args) {
|
|
final queryResults = <QueryResult>[];
|
|
final imageEmbeddings = args["imageEmbeddings"] as List<Embedding>;
|
|
final textEmbedding = args["textEmbedding"] as List<double>;
|
|
for (final imageEmbedding in imageEmbeddings) {
|
|
final score = computeScore(
|
|
imageEmbedding.embedding,
|
|
textEmbedding,
|
|
);
|
|
if (score >= SemanticSearchService.kScoreThreshold) {
|
|
queryResults.add(QueryResult(imageEmbedding.fileID, score));
|
|
}
|
|
}
|
|
|
|
queryResults.sort((first, second) => second.score.compareTo(first.score));
|
|
return queryResults;
|
|
}
|
|
|
|
double computeScore(List<double> imageEmbedding, List<double> textEmbedding) {
|
|
assert(
|
|
imageEmbedding.length == textEmbedding.length,
|
|
"The two embeddings should have the same length",
|
|
);
|
|
double score = 0;
|
|
for (int index = 0; index < imageEmbedding.length; index++) {
|
|
score += imageEmbedding[index] * textEmbedding[index];
|
|
}
|
|
return score;
|
|
}
|
|
|
|
class QueryResult {
|
|
final int id;
|
|
final double score;
|
|
|
|
QueryResult(this.id, this.score);
|
|
}
|
|
|
|
class PendingQuery {
|
|
final String query;
|
|
final Completer<List<EnteFile>> completer;
|
|
|
|
PendingQuery(this.query, this.completer);
|
|
}
|
|
|
|
class IndexStatus {
|
|
final int indexedItems, pendingItems;
|
|
|
|
IndexStatus(this.indexedItems, this.pendingItems);
|
|
}
|