diff --git a/mobile/apps/photos/CLAUDE.md b/mobile/apps/photos/CLAUDE.md index 7207e65932..a8a780b68b 100644 --- a/mobile/apps/photos/CLAUDE.md +++ b/mobile/apps/photos/CLAUDE.md @@ -196,6 +196,11 @@ lib/ - Ensure documentation reflects the current implementation - Update examples in specs if behavior changes +### 5. Database Methods - BEST PRACTICE +**Prioritize readability in database methods** +- For small result sets (e.g., 1-2 stale entries), prefer filtering in Dart for cleaner, more readable code +- For large datasets, use SQL WHERE clauses for performance - they're much more efficient in SQLite + ## Important Notes - Large service files (some 70k+ lines) - consider file context when editing diff --git a/mobile/apps/photos/assets/ml/text_embeddings.json b/mobile/apps/photos/assets/ml/text_embeddings.json deleted file mode 100644 index 5714cbdc67..0000000000 --- a/mobile/apps/photos/assets/ml/text_embeddings.json +++ /dev/null @@ -1,3530 +0,0 @@ -{ - "version": "1.0.0", - "embeddings": { - "clip_positive": { - "prompt": "Photo of a precious and nostalgic memory radiating warmth, vibrant energy, or quiet beauty — alive with color, light, or emotion", - "vector": [ - -0.00029399772756733, -0.0024989808443933725, 0.0030610354151576757, - -0.04166121035814285, 0.03460526093840599, 0.050619494169950485, - 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0.017915163189172745, -0.010187177918851376, -0.039359841495752335, - -0.05723040550947189, -0.022871773689985275, -0.017335403710603714, - -0.0351995974779129, -0.023725485429167747, -0.027255002409219742, - -0.01102177519351244, 0.019858308136463165, 0.02341330796480179, - 0.02624838799238205, 0.054662905633449554, -0.009282500483095646, - -0.020622823387384415, 0.04590919613838196, -0.05874669924378395, - 0.008250403217971325, 0.019450565800070763, -0.015347662381827831, - 0.01154419407248497, 0.011518710292875767, 0.013614758849143982, - 0.023158468306064606, 0.015226613730192184, -0.03371516242623329, - -0.029478468000888824, 0.018647823482751846, 0.010633145458996296, - 0.0005479032406583428, -0.025146210566163063, -0.03495113179087639, - -0.07967532426118851, 0.01184362918138504, 0.015245726332068443, - 0.006307258270680904, 0.02591709792613983, 0.023298630490899086, - 0.021533871069550514, 0.006549355108290911, -0.010798790492117405, - 0.011047258973121643, -0.019527016207575798, -0.0348237119615078, - -0.009620161727070808, -0.009913226589560509, -0.016322419047355652, - -0.015564274974167347, -0.009199677966535091, -0.023336855694651604, - -0.026057260110974312, -0.0160293560475111, -0.02277621068060398, - 0.005351613275706768, 0.022438550367951393, 0.003586854785680771, - 0.01966717839241028, -0.05842814967036247 - ] - } - } - } -} diff --git a/mobile/apps/photos/lib/core/error-reporting/super_logging.dart b/mobile/apps/photos/lib/core/error-reporting/super_logging.dart index 13cc1cace5..e3d1983d90 100644 --- a/mobile/apps/photos/lib/core/error-reporting/super_logging.dart +++ b/mobile/apps/photos/lib/core/error-reporting/super_logging.dart @@ -230,7 +230,6 @@ class SuperLogging { } catch (e) { $.warning("Failed to initialize log viewer: $e"); } - } if (appConfig.body == null) return; @@ -313,14 +312,14 @@ class SuperLogging { saveLogString(str, rec.error); // Hook for external log viewer (if available) // This allows the log_viewer package to capture logs without creating a dependency - if(_logViewerCallback != null) { - try { - if (_logViewerCallback != null) { - _logViewerCallback!(rec, config.prefix); + if (_logViewerCallback != null) { + try { + if (_logViewerCallback != null) { + _logViewerCallback!(rec, config.prefix); + } + } catch (_) { + // Silently ignore any errors from the log viewer } - } catch (_) { - // Silently ignore any errors from the log viewer - } } } diff --git a/mobile/apps/photos/lib/db/ml/db.dart b/mobile/apps/photos/lib/db/ml/db.dart index 5ad34e77fd..4f9fbfa96d 100644 --- a/mobile/apps/photos/lib/db/ml/db.dart +++ b/mobile/apps/photos/lib/db/ml/db.dart @@ -62,6 +62,7 @@ class MLDataDB with SqlDbBase implements IMLDataDB { createClipEmbeddingsTable, createFileDataTable, createFaceCacheTable, + createTextEmbeddingsCacheTable, ]; // only have a single app-wide reference to the database @@ -1429,6 +1430,56 @@ class MLDataDB with SqlDbBase implements IMLDataDB { Bus.instance.fire(EmbeddingUpdatedEvent()); } + /// WARNING: don't confuse this with [putClip]. If you're not sure, use [putClip] + Future putRepeatedTextEmbeddingCache( + String query, + List embedding, + ) async { + final db = await asyncDB; + await db.execute( + 'INSERT OR REPLACE INTO $textEmbeddingsCacheTable ' + '($textQueryColumn, $embeddingColumn, $mlVersionColumn, $createdAtColumn) ' + 'VALUES (?, ?, ?, ?)', + [ + query, + Float32List.fromList(embedding).buffer.asUint8List(), + clipMlVersion, + DateTime.now().millisecondsSinceEpoch, + ], + ); + } + + /// WARNING: don't confuse this with [getAllClipVectors]. If you're not sure, use [getAllClipVectors] + Future?> getRepeatedTextEmbeddingCache(String query) async { + final db = await asyncDB; + final results = await db.getAll( + 'SELECT $embeddingColumn, $mlVersionColumn, $createdAtColumn ' + 'FROM $textEmbeddingsCacheTable ' + 'WHERE $textQueryColumn = ?', + [query], + ); + + if (results.isEmpty) return null; + + final threeMonthsAgo = + DateTime.now().millisecondsSinceEpoch - (90 * 24 * 60 * 60 * 1000); + + // Find first valid entry + for (final result in results) { + if (result[mlVersionColumn] == clipMlVersion && + result[createdAtColumn] as int > threeMonthsAgo) { + return Float32List.view((result[embeddingColumn] as Uint8List).buffer); + } + } + + // No valid entry found, clean up + await db.execute( + 'DELETE FROM $textEmbeddingsCacheTable WHERE $textQueryColumn = ?', + [query], + ); + return null; + } + @override Future deleteClipEmbeddings(List fileIDs) async { final db = await instance.asyncDB; diff --git a/mobile/apps/photos/lib/db/ml/schema.dart b/mobile/apps/photos/lib/db/ml/schema.dart index d2306cd5d2..96e0560ef6 100644 --- a/mobile/apps/photos/lib/db/ml/schema.dart +++ b/mobile/apps/photos/lib/db/ml/schema.dart @@ -16,6 +16,8 @@ const mlVersionColumn = 'ml_version'; const personIdColumn = 'person_id'; const clusterIDColumn = 'cluster_id'; const personOrClusterIdColumn = 'person_or_cluster_id'; +const textQueryColumn = 'text_query'; +const createdAtColumn = 'created_at'; const createFacesTable = '''CREATE TABLE IF NOT EXISTS $facesTable ( $fileIDColumn INTEGER NOT NULL, @@ -137,3 +139,18 @@ CREATE TABLE IF NOT EXISTS $faceCacheTable ( '''; const deleteFaceCacheTable = 'DELETE FROM $faceCacheTable'; + +// ## TEXT EMBEDDINGS CACHE TABLE +const textEmbeddingsCacheTable = 'text_embeddings_cache'; + +const createTextEmbeddingsCacheTable = ''' +CREATE TABLE IF NOT EXISTS $textEmbeddingsCacheTable ( + $textQueryColumn TEXT NOT NULL, + $embeddingColumn BLOB NOT NULL, + $mlVersionColumn INTEGER NOT NULL, + $createdAtColumn INTEGER NOT NULL, + PRIMARY KEY ($textQueryColumn) +); +'''; + +const deleteTextEmbeddingsCacheTable = 'DELETE FROM $textEmbeddingsCacheTable'; diff --git a/mobile/apps/photos/lib/main.dart b/mobile/apps/photos/lib/main.dart index 0e32959196..a575787c5e 100644 --- a/mobile/apps/photos/lib/main.dart +++ b/mobile/apps/photos/lib/main.dart @@ -1,4 +1,4 @@ - import 'dart:async'; +import 'dart:async'; import 'dart:io'; import "package:adaptive_theme/adaptive_theme.dart"; diff --git a/mobile/apps/photos/lib/service_locator.dart b/mobile/apps/photos/lib/service_locator.dart index 3e8b2cfa35..cee522f1f2 100644 --- a/mobile/apps/photos/lib/service_locator.dart +++ b/mobile/apps/photos/lib/service_locator.dart @@ -19,6 +19,7 @@ import "package:photos/services/smart_albums_service.dart"; import "package:photos/services/smart_memories_service.dart"; import "package:photos/services/storage_bonus_service.dart"; import "package:photos/services/sync/trash_sync_service.dart"; +import "package:photos/services/text_embeddings_cache_service.dart"; import "package:photos/services/update_service.dart"; import "package:photos/utils/local_settings.dart"; import "package:shared_preferences/shared_preferences.dart"; @@ -136,6 +137,12 @@ SmartMemoriesService get smartMemoriesService { return _smartMemoriesService!; } +TextEmbeddingsCacheService? _textEmbeddingsCacheService; +TextEmbeddingsCacheService get textEmbeddingsCacheService { + _textEmbeddingsCacheService ??= TextEmbeddingsCacheService.instance; + return _textEmbeddingsCacheService!; +} + BillingService? _billingService; BillingService get billingService { _billingService ??= BillingService( diff --git a/mobile/apps/photos/lib/services/machine_learning/semantic_search/semantic_search_service.dart b/mobile/apps/photos/lib/services/machine_learning/semantic_search/semantic_search_service.dart index 0977c7e534..f5c3e47cfe 100644 --- a/mobile/apps/photos/lib/services/machine_learning/semantic_search/semantic_search_service.dart +++ b/mobile/apps/photos/lib/services/machine_learning/semantic_search/semantic_search_service.dart @@ -193,15 +193,22 @@ class SemanticSearchService { return results; } - Future>> getMatchingFileIDs( + /// Get matching file IDs for common repeated queries like smart memories and magic cache. + /// WARNING: Use this method carefully - it uses persistent caching which is only + /// beneficial for queries that are repeated across app sessions. + /// For regular user searches, use getMatchingFiles instead. + Future>> getMatchingFileIDsForCommonQueries( Map queryToScore, ) async { final textEmbeddings = >{}; final minimumSimilarityMap = {}; + for (final entry in queryToScore.entries) { final query = entry.key; final score = entry.value; - final textEmbedding = await _getTextEmbedding(query); + // Use cache service instead of _getTextEmbedding + final textEmbedding = + await textEmbeddingsCacheService.getEmbedding(query); textEmbeddings[query] = textEmbedding; minimumSimilarityMap[query] = score; } @@ -210,6 +217,7 @@ class SemanticSearchService { textEmbeddings, minimumSimilarityMap: minimumSimilarityMap, ); + final result = >{}; for (final entry in queryResults.entries) { final query = entry.key; diff --git a/mobile/apps/photos/lib/services/magic_cache_service.dart b/mobile/apps/photos/lib/services/magic_cache_service.dart index fc517cc150..9381a6cf07 100644 --- a/mobile/apps/photos/lib/services/magic_cache_service.dart +++ b/mobile/apps/photos/lib/services/magic_cache_service.dart @@ -401,8 +401,8 @@ class MagicCacheService { for (Prompt prompt in magicPromptsData) { queryToScore[prompt.query] = prompt.minScore; } - final clipResults = - await SemanticSearchService.instance.getMatchingFileIDs(queryToScore); + final clipResults = await SemanticSearchService.instance + .getMatchingFileIDsForCommonQueries(queryToScore); for (Prompt prompt in magicPromptsData) { final List fileUploadedIDs = clipResults[prompt.query] ?? []; if (fileUploadedIDs.isNotEmpty) { diff --git a/mobile/apps/photos/lib/services/smart_memories_service.dart b/mobile/apps/photos/lib/services/smart_memories_service.dart index 29fc9b9cd5..7edf6d0758 100644 --- a/mobile/apps/photos/lib/services/smart_memories_service.dart +++ b/mobile/apps/photos/lib/services/smart_memories_service.dart @@ -37,7 +37,6 @@ import "package:photos/services/location_service.dart"; import "package:photos/services/machine_learning/face_ml/person/person_service.dart"; import "package:photos/services/machine_learning/ml_result.dart"; import "package:photos/services/search_service.dart"; -import "package:photos/utils/text_embeddings_util.dart"; class MemoriesResult { final List memories; @@ -103,18 +102,29 @@ class SmartMemoriesService { 'allImageEmbeddings has ${allImageEmbeddings.length} entries $t', ); - // Load pre-computed text embeddings from assets - final textEmbeddings = await loadTextEmbeddingsFromAssets(); - if (textEmbeddings == null) { - _logger.severe('Failed to load pre-computed text embeddings'); - throw Exception( - 'Failed to load pre-computed text embeddings', + _logger.info('Loading text embeddings via cache service'); + final clipPositiveTextVector = Vector.fromList( + await textEmbeddingsCacheService.getEmbedding( + "Photo of a precious and nostalgic memory radiating warmth, vibrant energy, or quiet beauty — alive with color, light, or emotion", + ), + ); + + final clipPeopleActivityVectors = {}; + for (final activity in PeopleActivity.values) { + final query = activityQuery(activity); + clipPeopleActivityVectors[activity] = Vector.fromList( + await textEmbeddingsCacheService.getEmbedding(query), ); } - _logger.info('Using pre-computed text embeddings from assets'); - final clipPositiveTextVector = textEmbeddings.clipPositiveVector; - final clipPeopleActivityVectors = textEmbeddings.peopleActivityVectors; - final clipMemoryTypeVectors = textEmbeddings.clipMemoryTypeVectors; + + final clipMemoryTypeVectors = {}; + for (final memoryType in ClipMemoryType.values) { + final query = clipQuery(memoryType); + clipMemoryTypeVectors[memoryType] = Vector.fromList( + await textEmbeddingsCacheService.getEmbedding(query), + ); + } + _logger.info('Text embeddings loaded via cache service'); final local = await getLocale(); final languageCode = local?.languageCode ?? "en"; diff --git a/mobile/apps/photos/lib/services/text_embeddings_cache_service.dart b/mobile/apps/photos/lib/services/text_embeddings_cache_service.dart new file mode 100644 index 0000000000..1fa450f751 --- /dev/null +++ b/mobile/apps/photos/lib/services/text_embeddings_cache_service.dart @@ -0,0 +1,29 @@ +import 'package:logging/logging.dart'; +import 'package:photos/db/ml/db.dart'; +import 'package:photos/services/machine_learning/ml_computer.dart'; + +class TextEmbeddingsCacheService { + static final _logger = Logger('TextEmbeddingsCacheService'); + + TextEmbeddingsCacheService._privateConstructor(); + static final instance = TextEmbeddingsCacheService._privateConstructor(); + + Future> getEmbedding(String query) async { + // 1. Check database cache + final dbResult = + await MLDataDB.instance.getRepeatedTextEmbeddingCache(query); + if (dbResult != null) { + _logger.info('Text embedding cache hit for query'); + return dbResult; + } + + // 2. Compute new embedding + _logger.info('Computing new text embedding for query'); + final embedding = await MLComputer.instance.runClipText(query); + + // 3. Store in database cache + await MLDataDB.instance.putRepeatedTextEmbeddingCache(query, embedding); + + return embedding; + } +} diff --git a/mobile/apps/photos/lib/utils/text_embeddings_util.dart b/mobile/apps/photos/lib/utils/text_embeddings_util.dart deleted file mode 100644 index 349c284ed4..0000000000 --- a/mobile/apps/photos/lib/utils/text_embeddings_util.dart +++ /dev/null @@ -1,172 +0,0 @@ -import 'dart:convert'; -import "dart:developer" as dev show log; -import "dart:io" show File; - -import 'package:flutter/services.dart'; -import 'package:logging/logging.dart'; -import 'package:ml_linalg/vector.dart'; -import "package:path_provider/path_provider.dart" - show getExternalStorageDirectory; -import 'package:photos/models/memories/clip_memory.dart'; -import 'package:photos/models/memories/people_memory.dart'; -import "package:photos/services/machine_learning/ml_computer.dart" - show MLComputer; - -final _logger = Logger('TextEmbeddingsUtil'); - -/// Loads pre-computed text embeddings from assets -Future loadTextEmbeddingsFromAssets() async { - try { - _logger.info('Loading text embeddings from assets'); - final jsonString = - await rootBundle.loadString('assets/ml/text_embeddings.json'); - final data = json.decode(jsonString) as Map; - - final embeddings = data['embeddings'] as Map; - - // Parse clip positive embedding - Vector? clipPositiveVector; - final clipPositive = embeddings['clip_positive'] as Map; - final clipPositiveVectorData = - (clipPositive['vector'] as List).cast(); - if (clipPositiveVectorData.isNotEmpty) { - clipPositiveVector = Vector.fromList(clipPositiveVectorData); - } - - // Parse people activities embeddings - final Map peopleActivityVectors = {}; - final peopleActivities = - embeddings['people_activities'] as Map; - for (final activity in PeopleActivity.values) { - final activityName = activity.toString().split('.').last; - if (peopleActivities.containsKey(activityName)) { - final activityData = - peopleActivities[activityName] as Map; - final vector = (activityData['vector'] as List).cast(); - if (vector.isNotEmpty) { - peopleActivityVectors[activity] = Vector.fromList(vector); - } - } - } - - // Parse clip memory types embeddings - final Map clipMemoryTypeVectors = {}; - final clipMemoryTypes = - embeddings['clip_memory_types'] as Map; - for (final memoryType in ClipMemoryType.values) { - final typeName = memoryType.toString().split('.').last; - if (clipMemoryTypes.containsKey(typeName)) { - final typeData = clipMemoryTypes[typeName] as Map; - final vector = (typeData['vector'] as List).cast(); - if (vector.isNotEmpty) { - clipMemoryTypeVectors[memoryType] = Vector.fromList(vector); - } - } - } - - // Check if we have all required embeddings - if (clipPositiveVector == null) { - _logger.severe('Clip positive vector is missing'); - throw Exception('Clip positive vector is missing'); - } - - if (peopleActivityVectors.length != PeopleActivity.values.length) { - _logger.severe('Some people activity vectors are missing'); - throw Exception('Some people activity vectors are missing'); - } - - if (clipMemoryTypeVectors.length != ClipMemoryType.values.length) { - _logger.severe('Some clip memory type vectors are missing'); - throw Exception('Some clip memory type vectors are missing'); - } - - _logger.info('Text embeddings loaded successfully from JSON assets'); - return TextEmbeddings( - clipPositiveVector: clipPositiveVector, - peopleActivityVectors: peopleActivityVectors, - clipMemoryTypeVectors: clipMemoryTypeVectors, - ); - } catch (e, stackTrace) { - _logger.severe('Failed to load text embeddings from JSON', e, stackTrace); - return null; - } -} - -class TextEmbeddings { - final Vector clipPositiveVector; - final Map peopleActivityVectors; - final Map clipMemoryTypeVectors; - - const TextEmbeddings({ - required this.clipPositiveVector, - required this.peopleActivityVectors, - required this.clipMemoryTypeVectors, - }); -} - -/// Helper function to generate text embeddings and save them to a JSON file -/// Run this once to generate the embeddings, then copy the output -/// to assets/ml/text_embeddings.json -Future generateAndSaveTextEmbeddings() async { - final Map embeddingsData = { - 'version': '1.0.0', - 'embeddings': { - 'clip_positive': {}, - 'people_activities': {}, - 'clip_memory_types': {}, - }, - }; - - // Generate clip positive embedding - const String clipPositiveQuery = - 'Photo of a precious and nostalgic memory radiating warmth, vibrant energy, or quiet beauty — alive with color, light, or emotion'; - final clipPositiveVector = - await MLComputer.instance.runClipText(clipPositiveQuery); - embeddingsData['embeddings']['clip_positive'] = { - 'prompt': clipPositiveQuery, - 'vector': clipPositiveVector, - }; - - // Generate people activity embeddings - final peopleActivities = {}; - for (final activity in PeopleActivity.values) { - final activityName = activity.toString().split('.').last; - final prompt = activityQuery(activity); - final vector = await MLComputer.instance.runClipText(prompt); - peopleActivities[activityName] = { - 'prompt': prompt, - 'vector': vector, - }; - } - embeddingsData['embeddings']['people_activities'] = peopleActivities; - - // Generate clip memory type embeddings - final clipMemoryTypes = {}; - for (final memoryType in ClipMemoryType.values) { - final typeName = memoryType.toString().split('.').last; - final prompt = clipQuery(memoryType); - final vector = await MLComputer.instance.runClipText(prompt); - clipMemoryTypes[typeName] = { - 'prompt': prompt, - 'vector': vector, - }; - } - embeddingsData['embeddings']['clip_memory_types'] = clipMemoryTypes; - - // Convert to JSON and log it - final jsonString = const JsonEncoder.withIndent(' ').convert(embeddingsData); - dev.log( - '_generateAndSaveTextEmbeddings: Generated text embeddings JSON', - ); - - final tempDir = await getExternalStorageDirectory(); - final file = File('${tempDir!.path}/text_embeddings.json'); - await file.writeAsString(jsonString); - dev.log( - '_generateAndSaveTextEmbeddings: Saved text embeddings to ${file.path}', - ); - - dev.log( - '_generateAndSaveTextEmbeddings: Text embeddings generation complete! Copy the JSON output above to assets/ml/text_embeddings.json', - ); -} diff --git a/mobile/apps/photos/pubspec.yaml b/mobile/apps/photos/pubspec.yaml index f78a4449c8..968db38e08 100644 --- a/mobile/apps/photos/pubspec.yaml +++ b/mobile/apps/photos/pubspec.yaml @@ -349,7 +349,6 @@ flutter: - assets/image-editor/ - assets/icons/ - assets/launcher_icon/ - - assets/ml/ fonts: - family: Inter fonts: diff --git a/mobile/apps/photos/scripts/internal_changes.txt b/mobile/apps/photos/scripts/internal_changes.txt index 3efc74a4a9..0ad8dc584f 100644 --- a/mobile/apps/photos/scripts/internal_changes.txt +++ b/mobile/apps/photos/scripts/internal_changes.txt @@ -1,2 +1,3 @@ -- Neeraj: Potential fix for ios in-app payment -- Neeraj: (i) Debug option to enable logViewer \ No newline at end of file +- Laurens: text embedding caching for memories and discover +- Neeraj: (i) Debug option to enable logViewer +- Neeraj: Potential fix for ios in-app payment \ No newline at end of file