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* Base Storage Class\n *\n * Abstract implementation of ModelStorage interface.\n */\n\nimport type {\n ModelMeta,\n TensorChunk,\n TokenizerData,\n SyncProgress,\n UserAdapter,\n TrainingLogEntry,\n StorageBackend,\n} from '../../types';\n\nexport abstract class BaseStorage {\n abstract readonly backend: StorageBackend;\n\n // Initialization\n abstract initialize(): Promise;\n\n // Model metadata\n abstract getModelMeta(modelId: string): Promise;\n abstract setModelMeta(meta: ModelMeta): Promise;\n abstract deleteModelMeta(modelId: string): Promise;\n abstract listModels(): Promise;\n\n // Tensor chunks\n abstract getTensorChunks(\n modelId: string,\n layerIndex: number\n ): Promise;\n abstract getTensorChunk(\n modelId: string,\n tensorName: string,\n chunkIndex: number\n ): Promise;\n abstract storeTensorChunk(chunk: TensorChunk): Promise;\n abstract deleteTensorChunks(modelId: string): Promise;\n\n // Tokenizer\n abstract getTokenizer(modelId: string): Promise;\n abstract setTokenizer(data: TokenizerData): Promise;\n\n // Sync progress\n abstract getSyncProgress(modelId: string): Promise;\n abstract updateSyncProgress(progress: SyncProgress): Promise;\n abstract clearSyncProgress(modelId: string): Promise;\n\n // User adapters (for RLHF)\n abstract getUserAdapter(\n modelId: string,\n userId: string,\n adapterId?: string\n ): Promise;\n abstract setUserAdapter(adapter: UserAdapter): Promise;\n abstract deleteUserAdapter(adapterId: string): Promise;\n\n // Training log\n abstract addTrainingLogEntry(\n entry: Omit\n ): Promise;\n abstract getTrainingLog(\n adapterId: string,\n limit?: number\n ): Promise;\n\n // Utility methods\n abstract isModelComplete(modelId: string): Promise;\n abstract getStorageUsed(): Promise;\n abstract clear(): Promise;\n\n /**\n * Load all tensor chunks for a layer and combine into Float32Arrays\n */\n async loadLayerWeights(\n modelId: string,\n layerIndex: number\n ): Promise> {\n const chunks = await 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p.chunksDownloaded, 0);\n const total = progress.reduce((sum, p) => sum + p.totalChunks, 0);\n\n return total > 0 ? (downloaded / total) * 100 : 0;\n }\n}\n","/**\n * OPFS Storage Implementation\n *\n * Uses Origin Private File System for model weight storage.\n * Provides best performance for large binary data in browsers.\n */\n\nimport { BaseStorage } from './base-storage';\nimport type { DownloadProgress } from '../../types';\nimport type {\n ModelMeta,\n TensorChunk,\n TokenizerData,\n SyncProgress,\n UserAdapter,\n TrainingLogEntry,\n StorageBackend,\n} from '../../types';\n\nconst DB_NAME = 'edgework-models';\nconst DB_VERSION = 1;\n\ninterface OPFSDatabase {\n models: ModelMeta[];\n tokenizers: Map;\n syncProgress: Map;\n adapters: Map;\n trainingLog: TrainingLogEntry[];\n}\n\nexport class OPFSStorage extends BaseStorage {\n readonly backend: StorageBackend = 'opfs';\n private root: FileSystemDirectoryHandle | null = null;\n private metaDb: OPFSDatabase | null = null;\n private metaFile: FileSystemFileHandle | null = null;\n\n /**\n * Initialize OPFS storage\n */\n async initialize(): Promise {\n if (!('storage' in navigator) || !('getDirectory' in navigator.storage)) {\n throw new Error('OPFS is not supported in this browser');\n }\n\n this.root = await navigator.storage.getDirectory();\n const modelsDir = await this.root.getDirectoryHandle('models', {\n create: true,\n });\n\n // Load or create metadata file\n this.metaFile = await modelsDir.getFileHandle('meta.json', {\n create: true,\n });\n await this.loadMetadata();\n }\n\n private async loadMetadata(): Promise {\n if (!this.metaFile) return;\n\n try {\n const file = await this.metaFile.getFile();\n const text = await file.text();\n if (text) {\n const data = JSON.parse(text);\n this.metaDb = {\n models: data.models || [],\n tokenizers: new Map(Object.entries(data.tokenizers || {})),\n syncProgress: new Map(Object.entries(data.syncProgress || {})),\n adapters: new Map(Object.entries(data.adapters || {})),\n trainingLog: data.trainingLog || [],\n };\n } else {\n this.metaDb = {\n models: [],\n tokenizers: new Map(),\n syncProgress: new Map(),\n adapters: new Map(),\n trainingLog: [],\n };\n }\n } catch {\n this.metaDb = {\n models: [],\n tokenizers: new Map(),\n syncProgress: new Map(),\n adapters: new Map(),\n trainingLog: [],\n };\n }\n }\n\n private async saveMetadata(): Promise {\n if (!this.metaFile || !this.metaDb) return;\n\n const data = {\n models: this.metaDb.models,\n tokenizers: Object.fromEntries(this.metaDb.tokenizers),\n syncProgress: Object.fromEntries(this.metaDb.syncProgress),\n adapters: Object.fromEntries(this.metaDb.adapters),\n trainingLog: this.metaDb.trainingLog,\n };\n\n const writable = await this.metaFile.createWritable();\n await writable.write(JSON.stringify(data));\n await writable.close();\n }\n\n private async getModelDir(\n modelId: string\n ): Promise {\n if (!this.root) throw new Error('Storage not initialized');\n const modelsDir = await this.root.getDirectoryHandle('models', {\n create: true,\n });\n return modelsDir.getDirectoryHandle(modelId, { create: true });\n }\n\n // Model metadata\n async getModelMeta(modelId: string): Promise {\n if (!this.metaDb) return null;\n return this.metaDb.models.find((m) => m.id === modelId) || null;\n }\n\n async setModelMeta(meta: ModelMeta): Promise {\n if (!this.metaDb) return;\n const idx = this.metaDb.models.findIndex((m) => m.id === meta.id);\n if (idx >= 0) {\n this.metaDb.models[idx] = meta;\n } else {\n this.metaDb.models.push(meta);\n }\n await this.saveMetadata();\n }\n\n async deleteModelMeta(modelId: string): Promise {\n if (!this.metaDb) return;\n this.metaDb.models = this.metaDb.models.filter((m) => m.id !== modelId);\n await this.saveMetadata();\n }\n\n async listModels(): Promise {\n return this.metaDb?.models || [];\n }\n\n // Tensor chunks - stored as binary files in OPFS\n async getTensorChunks(\n modelId: string,\n layerIndex: number\n ): Promise {\n const chunks: TensorChunk[] = [];\n try {\n const modelDir = await this.getModelDir(modelId);\n const layerDir = await modelDir.getDirectoryHandle(\n `layer_${layerIndex}`,\n {\n create: false,\n }\n );\n\n // Use values() iterator for better compatibility\n const entries = (layerDir as any).entries() as AsyncIterable<\n [string, FileSystemHandle]\n >;\n for await (const [name, handle] of entries) {\n if (handle.kind === 'file' && name.endsWith('.bin')) {\n const file = await (handle as FileSystemFileHandle).getFile();\n const metaName = name.replace('.bin', '.json');\n const metaHandle = await layerDir.getFileHandle(metaName);\n const metaFile = await metaHandle.getFile();\n const meta = JSON.parse(await metaFile.text());\n\n chunks.push({\n ...meta,\n data: await file.arrayBuffer(),\n });\n }\n }\n } catch {\n // Directory doesn't exist, return empty\n }\n return chunks.sort((a, b) => a.chunkIndex - b.chunkIndex);\n }\n\n async getTensorChunk(\n modelId: string,\n tensorName: string,\n chunkIndex: number\n ): Promise {\n // Parse layer index from tensor name\n const match = tensorName.match(/blk\\.(\\d+)/);\n if (!match) return null;\n const layerIndex = parseInt(match[1], 10);\n\n const chunks = await this.getTensorChunks(modelId, layerIndex);\n return (\n chunks.find(\n (c) => c.tensorName === tensorName && c.chunkIndex === chunkIndex\n ) || null\n );\n }\n\n async storeTensorChunk(chunk: TensorChunk): Promise {\n const modelDir = await this.getModelDir(chunk.modelId);\n const layerDir = await modelDir.getDirectoryHandle(\n `layer_${chunk.layerIndex ?? 'embed'}`,\n { create: true }\n );\n\n // Store binary data\n const binName = `${chunk.tensorName}_${chunk.chunkIndex}.bin`;\n const binHandle = await layerDir.getFileHandle(binName, { create: true });\n const binWritable = await binHandle.createWritable();\n await binWritable.write(chunk.data);\n await binWritable.close();\n\n // Store metadata\n const metaName = `${chunk.tensorName}_${chunk.chunkIndex}.json`;\n const metaHandle = await layerDir.getFileHandle(metaName, { create: true });\n const metaWritable = await metaHandle.createWritable();\n const { data: _, ...meta } = chunk;\n await metaWritable.write(JSON.stringify(meta));\n await metaWritable.close();\n }\n\n async deleteTensorChunks(modelId: string): Promise {\n if (!this.root) return;\n try {\n const modelsDir = await this.root.getDirectoryHandle('models');\n await modelsDir.removeEntry(modelId, { recursive: true });\n } catch {\n // Directory doesn't exist\n }\n }\n\n // Tokenizer\n async getTokenizer(modelId: string): Promise {\n return this.metaDb?.tokenizers.get(modelId) || null;\n }\n\n async setTokenizer(data: TokenizerData): Promise {\n if (!this.metaDb) return;\n this.metaDb.tokenizers.set(data.modelId, data);\n await this.saveMetadata();\n }\n\n // Sync progress\n async getSyncProgress(modelId: string): Promise {\n return this.metaDb?.syncProgress.get(modelId) || [];\n }\n\n async updateSyncProgress(progress: SyncProgress): Promise {\n if (!this.metaDb) return;\n const existing = this.metaDb.syncProgress.get(progress.modelId) || [];\n const idx = existing.findIndex((p) => p.tensorName === progress.tensorName);\n if (idx >= 0) {\n existing[idx] = progress;\n } else {\n existing.push(progress);\n }\n this.metaDb.syncProgress.set(progress.modelId, existing);\n await this.saveMetadata();\n }\n\n async clearSyncProgress(modelId: string): Promise {\n if (!this.metaDb) return;\n this.metaDb.syncProgress.delete(modelId);\n await this.saveMetadata();\n }\n\n // User adapters\n async getUserAdapter(\n modelId: string,\n userId: string,\n adapterId?: string\n ): Promise {\n if (!this.metaDb) return null;\n for (const adapter of this.metaDb.adapters.values()) {\n if (\n adapter.modelId === modelId &&\n adapter.userId === userId &&\n (!adapterId || adapter.id === adapterId)\n ) {\n return adapter;\n }\n }\n return null;\n }\n\n async setUserAdapter(adapter: UserAdapter): Promise {\n if (!this.metaDb) return;\n this.metaDb.adapters.set(adapter.id, adapter);\n await this.saveMetadata();\n }\n\n async deleteUserAdapter(adapterId: string): Promise {\n if (!this.metaDb) return;\n this.metaDb.adapters.delete(adapterId);\n await this.saveMetadata();\n }\n\n // Training log\n async addTrainingLogEntry(\n entry: Omit\n ): Promise {\n if (!this.metaDb) return -1;\n const id = this.metaDb.trainingLog.length;\n this.metaDb.trainingLog.push({ ...entry, id });\n await this.saveMetadata();\n return id;\n }\n\n async getTrainingLog(\n adapterId: string,\n limit = 100\n ): Promise {\n if (!this.metaDb) return [];\n return this.metaDb.trainingLog\n .filter((e) => e.adapterId === adapterId)\n .slice(-limit);\n }\n\n // Utility\n async isModelComplete(modelId: string): Promise {\n const progress = await this.getSyncProgress(modelId);\n if (progress.length === 0) return false;\n return progress.every((p) => p.chunksDownloaded >= p.totalChunks);\n }\n\n async getStorageUsed(): Promise {\n if (!navigator.storage.estimate) return 0;\n const estimate = await navigator.storage.estimate();\n return estimate.usage || 0;\n }\n\n async clear(): Promise {\n if (!this.root) return;\n try {\n await this.root.removeEntry('models', { recursive: true });\n } catch {\n // Directory doesn't exist\n }\n this.metaDb = {\n models: [],\n tokenizers: new Map(),\n syncProgress: new Map(),\n adapters: new Map(),\n trainingLog: [],\n };\n }\n}\n","/**\n * IndexedDB Storage Implementation\n *\n * Fallback storage for browsers without OPFS support.\n * Uses IndexedDB with structured stores for model data.\n */\n\nimport { BaseStorage } from './base-storage';\nimport type {\n ModelMeta,\n TensorChunk,\n TokenizerData,\n SyncProgress,\n UserAdapter,\n TrainingLogEntry,\n StorageBackend,\n} from '../../types';\n\nconst DB_NAME = 'edgework-models';\nconst DB_VERSION = 1;\n\nexport class IndexedDBStorage extends BaseStorage {\n readonly backend: StorageBackend = 'indexeddb';\n private db: IDBDatabase | null = null;\n\n /**\n * Initialize IndexedDB storage\n */\n async initialize(): Promise {\n return new Promise((resolve, reject) => {\n const request = indexedDB.open(DB_NAME, DB_VERSION);\n\n request.onerror = () => reject(request.error);\n request.onsuccess = () => {\n this.db = request.result;\n resolve();\n };\n\n request.onupgradeneeded = (event) => {\n const db = (event.target as IDBOpenDBRequest).result;\n\n // Models store\n if (!db.objectStoreNames.contains('models')) {\n db.createObjectStore('models', { keyPath: 'id' });\n }\n\n // Tensor chunks store\n if (!db.objectStoreNames.contains('tensorChunks')) {\n const store = db.createObjectStore('tensorChunks', { keyPath: 'id' });\n store.createIndex('modelId', 'modelId', { unique: false });\n store.createIndex('layerIndex', ['modelId', 'layerIndex'], {\n unique: false,\n });\n store.createIndex('tensorName', ['modelId', 'tensorName'], {\n unique: false,\n });\n }\n\n // Tokenizers store\n if (!db.objectStoreNames.contains('tokenizers')) {\n db.createObjectStore('tokenizers', { keyPath: 'modelId' });\n }\n\n // Sync progress store\n if (!db.objectStoreNames.contains('syncProgress')) {\n const store = db.createObjectStore('syncProgress', {\n keyPath: ['modelId', 'tensorName'],\n });\n store.createIndex('modelId', 'modelId', { unique: false });\n }\n\n // User adapters store\n if (!db.objectStoreNames.contains('userAdapters')) {\n const store = db.createObjectStore('userAdapters', { keyPath: 'id' });\n store.createIndex('modelUser', ['modelId', 'userId'], {\n unique: false,\n });\n }\n\n // Training log store\n if (!db.objectStoreNames.contains('trainingLog')) {\n const store = db.createObjectStore('trainingLog', {\n keyPath: 'id',\n autoIncrement: true,\n });\n store.createIndex('adapterId', 'adapterId', { unique: false });\n }\n };\n });\n }\n\n private getStore(\n name: string,\n mode: IDBTransactionMode = 'readonly'\n ): IDBObjectStore {\n if (!this.db) throw new Error('Database not initialized');\n return this.db.transaction(name, mode).objectStore(name);\n }\n\n private promisify(request: IDBRequest): Promise {\n return new Promise((resolve, reject) => {\n request.onsuccess = () => resolve(request.result);\n request.onerror = () => reject(request.error);\n });\n }\n\n // Model metadata\n async getModelMeta(modelId: string): Promise {\n const store = this.getStore('models');\n return (await this.promisify(store.get(modelId))) || null;\n }\n\n async setModelMeta(meta: ModelMeta): Promise {\n const store = this.getStore('models', 'readwrite');\n await this.promisify(store.put(meta));\n }\n\n async deleteModelMeta(modelId: string): Promise {\n const store = this.getStore('models', 'readwrite');\n await this.promisify(store.delete(modelId));\n }\n\n async listModels(): Promise {\n const store = this.getStore('models');\n return this.promisify(store.getAll());\n }\n\n // Tensor chunks\n async getTensorChunks(\n modelId: string,\n layerIndex: number\n ): Promise {\n const store = this.getStore('tensorChunks');\n const index = store.index('layerIndex');\n return this.promisify(index.getAll([modelId, layerIndex]));\n }\n\n async getTensorChunk(\n modelId: string,\n tensorName: string,\n chunkIndex: number\n ): Promise {\n const id = `${modelId}:${tensorName}:${chunkIndex}`;\n const store = this.getStore('tensorChunks');\n return (await this.promisify(store.get(id))) || null;\n }\n\n async storeTensorChunk(chunk: TensorChunk): Promise {\n const store = this.getStore('tensorChunks', 'readwrite');\n await this.promisify(store.put(chunk));\n }\n\n async deleteTensorChunks(modelId: string): Promise {\n const store = this.getStore('tensorChunks', 'readwrite');\n const index = store.index('modelId');\n const keys = await this.promisify(index.getAllKeys(modelId));\n for (const key of keys) {\n await this.promisify(store.delete(key));\n }\n }\n\n // Tokenizer\n async getTokenizer(modelId: string): Promise {\n const store = this.getStore('tokenizers');\n return (await this.promisify(store.get(modelId))) || null;\n }\n\n async setTokenizer(data: TokenizerData): Promise {\n const store = this.getStore('tokenizers', 'readwrite');\n await this.promisify(store.put(data));\n }\n\n // Sync progress\n async getSyncProgress(modelId: string): Promise {\n const store = this.getStore('syncProgress');\n const index = store.index('modelId');\n return this.promisify(index.getAll(modelId));\n }\n\n async updateSyncProgress(progress: SyncProgress): Promise {\n const store = this.getStore('syncProgress', 'readwrite');\n await this.promisify(store.put(progress));\n }\n\n async clearSyncProgress(modelId: string): Promise {\n const store = this.getStore('syncProgress', 'readwrite');\n const index = store.index('modelId');\n const keys = await this.promisify(index.getAllKeys(modelId));\n for (const key of keys) {\n await this.promisify(store.delete(key));\n }\n }\n\n // User adapters\n async getUserAdapter(\n modelId: string,\n userId: string,\n adapterId?: string\n ): Promise {\n const store = this.getStore('userAdapters');\n const index = store.index('modelUser');\n const results = await this.promisify(index.getAll([modelId, userId]));\n if (!adapterId) {\n return results[0] || null;\n }\n return (\n (results.find((result) => result.id === adapterId) as UserAdapter) || null\n );\n }\n\n async setUserAdapter(adapter: UserAdapter): Promise {\n const store = this.getStore('userAdapters', 'readwrite');\n await this.promisify(store.put(adapter));\n }\n\n async deleteUserAdapter(adapterId: string): Promise {\n const store = this.getStore('userAdapters', 'readwrite');\n await this.promisify(store.delete(adapterId));\n }\n\n // Training log\n async addTrainingLogEntry(\n entry: Omit\n ): Promise {\n const store = this.getStore('trainingLog', 'readwrite');\n return this.promisify(store.add(entry)) as Promise;\n }\n\n async getTrainingLog(\n adapterId: string,\n limit = 100\n ): Promise {\n const store = this.getStore('trainingLog');\n const index = store.index('adapterId');\n const results = await this.promisify(index.getAll(adapterId));\n return results.slice(-limit);\n }\n\n // Utility\n async isModelComplete(modelId: string): Promise {\n const progress = await this.getSyncProgress(modelId);\n if (progress.length === 0) return false;\n return progress.every((p) => p.chunksDownloaded >= p.totalChunks);\n }\n\n async getStorageUsed(): Promise {\n if (!navigator.storage?.estimate) return 0;\n const estimate = await navigator.storage.estimate();\n return estimate.usage || 0;\n }\n\n async clear(): Promise {\n const stores = [\n 'models',\n 'tensorChunks',\n 'tokenizers',\n 'syncProgress',\n 'userAdapters',\n 'trainingLog',\n ];\n for (const name of stores) {\n const store = this.getStore(name, 'readwrite');\n await this.promisify(store.clear());\n }\n }\n}\n","/**\n * Memory Storage Implementation\n *\n * In-memory storage for testing and Node.js environments.\n * Does not persist data between sessions.\n */\n\nimport { BaseStorage } from './base-storage';\nimport type {\n ModelMeta,\n TensorChunk,\n TokenizerData,\n SyncProgress,\n UserAdapter,\n TrainingLogEntry,\n StorageBackend,\n} from '../../types';\n\nexport class MemoryStorage extends BaseStorage {\n readonly backend: StorageBackend = 'memory';\n\n private models = new Map();\n private tensorChunks = new Map();\n private tokenizers = new Map();\n private syncProgress = new Map();\n private userAdapters = new Map();\n private trainingLog: TrainingLogEntry[] = [];\n private nextLogId = 0;\n\n async initialize(): Promise {\n /* noop - nothing to initialize for memory storage */\n }\n\n // Model metadata\n async getModelMeta(modelId: string): Promise {\n return this.models.get(modelId) || null;\n }\n\n async setModelMeta(meta: ModelMeta): Promise {\n this.models.set(meta.id, meta);\n }\n\n async deleteModelMeta(modelId: string): Promise {\n this.models.delete(modelId);\n }\n\n async listModels(): Promise {\n return Array.from(this.models.values());\n }\n\n // Tensor chunks\n async getTensorChunks(\n modelId: string,\n layerIndex: number\n ): Promise {\n const chunks: TensorChunk[] = [];\n for (const chunk of this.tensorChunks.values()) {\n if (chunk.modelId === modelId && chunk.layerIndex === layerIndex) {\n chunks.push(chunk);\n }\n }\n return chunks.sort((a, b) => a.chunkIndex - b.chunkIndex);\n }\n\n async getTensorChunk(\n modelId: string,\n tensorName: string,\n chunkIndex: number\n ): Promise {\n const id = `${modelId}:${tensorName}:${chunkIndex}`;\n return this.tensorChunks.get(id) || null;\n }\n\n async storeTensorChunk(chunk: TensorChunk): Promise {\n this.tensorChunks.set(chunk.id, chunk);\n }\n\n async deleteTensorChunks(modelId: string): Promise {\n for (const [key, chunk] of this.tensorChunks) {\n if (chunk.modelId === modelId) {\n this.tensorChunks.delete(key);\n }\n }\n }\n\n // Tokenizer\n async getTokenizer(modelId: string): Promise {\n return this.tokenizers.get(modelId) || null;\n }\n\n async setTokenizer(data: TokenizerData): Promise {\n this.tokenizers.set(data.modelId, data);\n }\n\n // Sync progress\n async getSyncProgress(modelId: string): Promise {\n return this.syncProgress.get(modelId) || [];\n }\n\n async updateSyncProgress(progress: SyncProgress): Promise {\n const existing = this.syncProgress.get(progress.modelId) || [];\n const idx = existing.findIndex((p) => p.tensorName === progress.tensorName);\n if (idx >= 0) {\n existing[idx] = progress;\n } else {\n existing.push(progress);\n }\n this.syncProgress.set(progress.modelId, existing);\n }\n\n async clearSyncProgress(modelId: string): Promise {\n this.syncProgress.delete(modelId);\n }\n\n // User adapters\n async getUserAdapter(\n modelId: string,\n userId: string,\n adapterId?: string\n ): Promise {\n for (const adapter of this.userAdapters.values()) {\n if (\n adapter.modelId === modelId &&\n adapter.userId === userId &&\n (!adapterId || adapter.id === adapterId)\n ) {\n return adapter;\n }\n }\n return null;\n }\n\n async setUserAdapter(adapter: UserAdapter): Promise {\n this.userAdapters.set(adapter.id, adapter);\n }\n\n async deleteUserAdapter(adapterId: string): Promise {\n this.userAdapters.delete(adapterId);\n }\n\n // Training log\n async addTrainingLogEntry(\n entry: Omit\n ): Promise {\n const id = this.nextLogId++;\n this.trainingLog.push({ ...entry, id });\n return id;\n }\n\n async getTrainingLog(\n adapterId: string,\n limit = 100\n ): Promise {\n return this.trainingLog\n .filter((e) => e.adapterId === adapterId)\n .slice(-limit);\n }\n\n // Utility\n async isModelComplete(modelId: string): Promise {\n const progress = await this.getSyncProgress(modelId);\n if (progress.length === 0) return false;\n return progress.every((p) => p.chunksDownloaded >= p.totalChunks);\n }\n\n async getStorageUsed(): Promise {\n let size = 0;\n for (const chunk of this.tensorChunks.values()) {\n size += chunk.data.byteLength;\n }\n return size;\n }\n\n async clear(): Promise {\n this.models.clear();\n this.tensorChunks.clear();\n this.tokenizers.clear();\n this.syncProgress.clear();\n this.userAdapters.clear();\n this.trainingLog = [];\n this.nextLogId = 0;\n }\n}\n","/**\n * Storage Factory\n *\n * Creates appropriate storage backend based on environment capabilities.\n */\n\nimport { OPFSStorage } from './opfs-storage';\nimport { IndexedDBStorage } from './indexeddb-storage';\nimport { MemoryStorage } from './memory-storage';\nimport { BaseStorage } from './base-storage';\nimport type { StorageBackend } from '../../types';\n\nexport interface StorageOptions {\n /** Preferred storage backend */\n backend?: StorageBackend;\n /** Force the specified backend even if not optimal */\n force?: boolean;\n}\n\n/**\n * Detect the best available storage backend\n */\nexport function detectBestBackend(): StorageBackend {\n // Check if we're in a browser\n if (typeof window === 'undefined') {\n return 'memory';\n }\n\n // Check for OPFS support (best for large binary data)\n if ('storage' in navigator && 'getDirectory' in navigator.storage) {\n return 'opfs';\n }\n\n // Check for IndexedDB support\n if ('indexedDB' in window) {\n return 'indexeddb';\n }\n\n // Fall back to memory\n return 'memory';\n}\n\n/**\n * Check if a storage backend is available\n */\nexport function isBackendAvailable(backend: StorageBackend): boolean {\n if (typeof window === 'undefined') {\n return backend === 'memory';\n }\n\n switch (backend) {\n case 'opfs':\n return 'storage' in navigator && 'getDirectory' in navigator.storage;\n case 'indexeddb':\n return 'indexedDB' in window;\n case 'memory':\n return true;\n default:\n return false;\n }\n}\n\n/**\n * Create a storage instance\n */\nexport async function createStorage(\n options: StorageOptions = {}\n): Promise {\n const { backend, force = false } = options;\n\n // Determine which backend to use\n let selectedBackend: StorageBackend;\n if (backend && (force || isBackendAvailable(backend))) {\n selectedBackend = backend;\n } else {\n selectedBackend = detectBestBackend();\n }\n\n // Create and initialize the storage\n let storage: BaseStorage;\n switch (selectedBackend) {\n case 'opfs':\n storage = new OPFSStorage() as unknown as BaseStorage;\n break;\n case 'indexeddb':\n storage = new IndexedDBStorage();\n break;\n case 'memory':\n default:\n storage = new MemoryStorage();\n break;\n }\n\n await storage.initialize();\n return storage;\n}\n\n/**\n * Get storage capabilities info\n */\nexport function getStorageCapabilities(): {\n opfs: boolean;\n indexeddb: boolean;\n memory: boolean;\n recommended: StorageBackend;\n} {\n return {\n opfs: isBackendAvailable('opfs'),\n indexeddb: isBackendAvailable('indexeddb'),\n memory: true,\n recommended: detectBestBackend(),\n };\n}\n"]}