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* Edgework SDK Zod Schemas\n *\n * Runtime validation schemas for SDK types.\n */\n\nimport { z } from 'zod';\n\n// Model architecture enum\nexport const ModelArchitectureSchema = z.enum([\n 'llama',\n 'mistral',\n 'qwen',\n 'smollm',\n 'phi',\n]);\n\n// Quantization type enum\nexport const QuantizationTypeSchema = z.enum([\n 'f32',\n 'f16',\n 'q8_0',\n 'q4_k',\n 'q4_0',\n 'q4_1',\n]);\n\n// Model metadata\nexport const ModelMetaSchema = z.object({\n id: z.string(),\n name: z.string(),\n architecture: ModelArchitectureSchema,\n version: z.string(),\n vocabSize: z.number().int().positive(),\n hiddenDim: z.number().int().positive(),\n numLayers: z.number().int().positive(),\n numHeads: z.number().int().positive(),\n numKvHeads: z.number().int().positive().optional(),\n intermediateDim: z.number().int().positive().optional(),\n maxSeqLength: z.number().int().positive(),\n ropeTheta: z.number().positive().optional(),\n createdAt: z.string().datetime(),\n});\n\n// Tensor chunk\nexport const TensorChunkSchema = z.object({\n id: z.string(),\n modelId: z.string(),\n tensorName: z.string(),\n layerIndex: z.number().int().nullable(),\n chunkIndex: z.number().int().nonnegative(),\n totalChunks: z.number().int().positive(),\n dtype: QuantizationTypeSchema,\n shape: z.array(z.number().int().positive()),\n offset: z.number().int().nonnegative(),\n data: z.instanceof(ArrayBuffer),\n hash: z.string(),\n});\n\n// Tokenizer data\nexport const TokenizerDataSchema = z.object({\n modelId: z.string(),\n vocab: z.instanceof(ArrayBuffer),\n merges: z.instanceof(ArrayBuffer).optional(),\n specialTokens: z.record(z.string(), z.number()),\n chatTemplate: z.string().optional(),\n});\n\n// Sync progress\nexport const SyncProgressSchema = z.object({\n modelId: z.string(),\n tensorName: z.string(),\n chunksDownloaded: z.number().int().nonnegative(),\n totalChunks: z.number().int().positive(),\n lastSync: z.string().datetime().optional(),\n});\n\n// Adapter type enum\nexport const AdapterTypeSchema = z.enum([\n 'reward_head',\n 'style_adapter',\n 'lora',\n]);\n\n// User adapter\nexport const UserAdapterSchema = z.object({\n id: z.string(),\n modelId: z.string(),\n userId: z.string(),\n adapterType: AdapterTypeSchema,\n weights: z.instanceof(ArrayBuffer),\n trainingExamples: z.number().int().nonnegative(),\n lastUpdated: z.string().datetime(),\n});\n\n// Training log entry\nexport const TrainingLogEntrySchema = z.object({\n id: z.number().int(),\n adapterId: z.string(),\n messageHash: z.string(),\n feedback: z.number().min(-1).max(1),\n createdAt: z.string().datetime(),\n});\n\n// Download status enum\nexport const DownloadStatusSchema = z.enum([\n 'downloading',\n 'verifying',\n 'ready',\n 'error',\n]);\n\n// Download progress\nexport const DownloadProgressSchema = z.object({\n modelId: z.string(),\n percent: z.number().min(0).max(100),\n bytesDownloaded: z.number().int().nonnegative(),\n totalBytes: z.number().int().positive(),\n currentLayer: z.string().optional(),\n layersComplete: z.number().int().nonnegative(),\n totalLayers: z.number().int().positive(),\n status: DownloadStatusSchema,\n error: z.string().optional(),\n});\n\n// Storage backend enum\nexport const StorageBackendSchema = z.enum(['opfs', 'indexeddb', 'memory']);\n\n// Inference backend enum\nexport const InferenceBackendSchema = z.enum([\n 'webgpu',\n 'webnn',\n 'wasm',\n 'cpu',\n]);\n\n// Edgework options\nexport const EdgeworkOptionsSchema = z.object({\n model: z.string(),\n syncUrl: z.string().url().optional(),\n onProgress: z\n .function()\n .args(DownloadProgressSchema)\n .returns(z.void())\n .optional(),\n enableRLHF: z.boolean().optional().default(false),\n communityParticipation: z.boolean().optional().default(false),\n federatedSyncUrl: z.string().url().optional(),\n trainingBatchSize: z.number().int().positive().optional().default(16),\n trainingIdleFlushMs: z.number().int().positive().optional().default(45000),\n cacheDir: z.string().optional(),\n userId: z.string().optional(),\n storageBackend: StorageBackendSchema.optional(),\n inferenceBackend: InferenceBackendSchema.optional(),\n});\n\n// Generation options\nexport const GenerateOptionsSchema = z.object({\n maxTokens: z.number().int().positive().max(4096).optional().default(256),\n temperature: z.number().min(0).max(2).optional().default(0.7),\n topP: z.number().min(0).max(1).optional().default(0.9),\n topK: z.number().int().positive().optional().default(40),\n stopSequences: z.array(z.string()).optional(),\n stream: z.boolean().optional().default(false),\n});\n\n// Generation result\nexport const GenerateResultSchema = z.object({\n text: z.string(),\n tokens: z.array(z.number().int()),\n tokenCount: z.number().int().nonnegative(),\n durationMs: z.number().nonnegative(),\n tokensPerSecond: z.number().nonnegative(),\n});\n\n// Chat message role enum\nexport const ChatRoleSchema = z.enum(['system', 'user', 'assistant']);\n\n// Chat message\nexport const ChatMessageSchema = z.object({\n role: ChatRoleSchema,\n content: z.string(),\n});\n\n// Chat messages array\nexport const ChatMessagesSchema = z.array(ChatMessageSchema);\n\n// RLHF feedback\nexport const RLHFFeedbackSchema = z.object({\n messageHash: z.string(),\n feedback: z.number().min(-1).max(1),\n hiddenState: z.instanceof(Float32Array).optional(),\n});\n\n// Model status enum\nexport const ModelStatusSchema = z.enum([\n 'not_downloaded',\n 'downloading',\n 'ready',\n 'offline',\n 'error',\n]);\n\n// Engine info\nexport const EngineInfoSchema = z.object({\n modelId: z.string(),\n status: ModelStatusSchema,\n backend: InferenceBackendSchema,\n storageBackend: StorageBackendSchema,\n progress: DownloadProgressSchema.optional(),\n config: ModelMetaSchema,\n});\n\n// Layer weights\nexport const LayerWeightsSchema = z.object({\n layerIndex: z.number().int().nonnegative(),\n attnQ: z.instanceof(Float32Array),\n attnK: z.instanceof(Float32Array),\n attnV: z.instanceof(Float32Array),\n attnOut: z.instanceof(Float32Array),\n attnNorm: z.instanceof(Float32Array),\n ffnGate: z.instanceof(Float32Array),\n ffnUp: z.instanceof(Float32Array),\n ffnDown: z.instanceof(Float32Array),\n ffnNorm: z.instanceof(Float32Array),\n});\n\n// KV cache\nexport const KVCacheSchema = z.object({\n keys: z.array(z.instanceof(Float32Array)),\n values: z.array(z.instanceof(Float32Array)),\n position: z.number().int().nonnegative(),\n maxLength: z.number().int().positive(),\n});\n\n// Model config for known models\nexport const CogModelConfigSchema = z.object({\n id: z.literal('cog-360m'),\n name: z.literal('Cog'),\n architecture: z.literal('smollm'),\n version: z.string(),\n vocabSize: z.literal(49152),\n hiddenDim: z.literal(960),\n numLayers: z.literal(32),\n numHeads: z.literal(15),\n numKvHeads: z.literal(5),\n intermediateDim: z.literal(2560),\n maxSeqLength: z.literal(2048),\n ropeTheta: z.literal(10000),\n});\n\nexport const CyranoModelConfigSchema = z.object({\n id: z.literal('cyrano-360m'),\n name: z.literal('Cyrano'),\n architecture: z.literal('smollm'),\n version: z.string(),\n vocabSize: z.literal(49152),\n hiddenDim: z.literal(960),\n numLayers: z.literal(32),\n numHeads: z.literal(15),\n numKvHeads: z.literal(5),\n intermediateDim: z.literal(2560),\n maxSeqLength: z.literal(2048),\n ropeTheta: z.literal(10000),\n});\n\n// Type exports inferred from schemas\nexport type ModelArchitecture = z.infer;\nexport type QuantizationType = z.infer;\nexport type ModelMeta = z.infer;\nexport type TensorChunk = z.infer;\nexport type TokenizerData = z.infer;\nexport type SyncProgress = z.infer;\nexport type AdapterType = z.infer;\nexport type UserAdapter = z.infer;\nexport type TrainingLogEntry = z.infer;\nexport type DownloadStatus = z.infer;\nexport type DownloadProgress = z.infer;\nexport type StorageBackend = z.infer;\nexport type InferenceBackend = z.infer;\nexport type EdgeworkOptions = z.infer;\nexport type GenerateOptions = z.infer;\nexport type GenerateResult = z.infer;\nexport type ChatRole = z.infer;\nexport type ChatMessage = z.infer;\nexport type RLHFFeedback = z.infer;\nexport type ModelStatus = z.infer;\nexport type EngineInfo = z.infer;\nexport type LayerWeights = z.infer;\nexport type KVCache = z.infer;\n\n// Validation helpers\nexport function validateModelMeta(data: unknown): ModelMeta {\n return ModelMetaSchema.parse(data);\n}\n\nexport function validateGenerateOptions(data: unknown): GenerateOptions {\n return GenerateOptionsSchema.parse(data);\n}\n\nexport function validateChatMessages(data: unknown): ChatMessage[] {\n return ChatMessagesSchema.parse(data);\n}\n\nexport function validateEdgeworkOptions(data: unknown): EdgeworkOptions {\n return EdgeworkOptionsSchema.parse(data);\n}\n\nexport function validateRLHFFeedback(data: unknown): RLHFFeedback {\n return RLHFFeedbackSchema.parse(data);\n}\n\n// Safe parse variants that don't throw\nexport function safeParseModelMeta(data: unknown) {\n return ModelMetaSchema.safeParse(data);\n}\n\nexport function safeParseGenerateOptions(data: unknown) {\n return GenerateOptionsSchema.safeParse(data);\n}\n\nexport function safeParseChatMessages(data: unknown) {\n return ChatMessagesSchema.safeParse(data);\n}\n\nexport function safeParseEdgeworkOptions(data: unknown) {\n return EdgeworkOptionsSchema.safeParse(data);\n}\n\nexport function safeParseRLHFFeedback(data: unknown) {\n return RLHFFeedbackSchema.safeParse(data);\n}\n","/**\n * WASM Module Exports\n *\n * Provides encrypted WASM loading, decryption, and streaming capabilities.\n */\n\nexport {\n loadWasmCore,\n loadWasmStreaming,\n isWasmLoaded,\n getWasmModule,\n unloadWasm,\n type WasmModule,\n type EdgeworkCoreInstance,\n type LoaderConfig,\n} from './loader';\n","/**\n * WASM Loader with Decryption\n *\n * Loads and decrypts the encrypted WASM core module.\n * Requires a valid license token from the oracle.\n */\n\n// Header structure:\n// bytes 0-7: Magic \"EWASM001\"\n// bytes 8-11: Original size (uint32 LE)\n// bytes 12-15: Encrypted size (uint32 LE)\n// bytes 16-47: Salt (32 bytes)\n// bytes 48-59: IV (12 bytes)\n// bytes 60-63: Reserved\n// bytes 64-79: Auth tag (16 bytes)\n// bytes 80+: Encrypted data\n\nconst HEADER_SIZE = 64;\nconst AUTH_TAG_SIZE = 16;\nconst MAGIC = 'EWASM001';\n\n/**\n * WASM module instance\n */\nexport interface WasmModule {\n init: (jwt: string) => Promise;\n is_licensed: () => boolean;\n get_session: () => unknown;\n EdgeworkCore: new () => EdgeworkCoreInstance;\n}\n\n/**\n * EdgeworkCore instance from WASM\n */\nexport interface EdgeworkCoreInstance {\n get_router: () => unknown;\n get_inference_client: (config: unknown) => unknown;\n get_distributed_client: (config: unknown) => unknown;\n get_gateway_client: (endpoint: string) => unknown;\n}\n\n/**\n * Loader configuration\n */\nexport interface LoaderConfig {\n wasmUrl?: string;\n oracleEndpoint?: string;\n licenseToken?: string;\n}\n\n/**\n * Loader state\n */\ninterface LoaderState {\n loaded: boolean;\n module: WasmModule | null;\n error: Error | null;\n}\n\nconst state: LoaderState = {\n loaded: false,\n module: null,\n error: null,\n};\n\n/**\n * Derive decryption key from license token\n */\nasync function deriveKey(\n licenseToken: string,\n salt: Uint8Array\n): Promise {\n const normalizedSalt = Uint8Array.from(salt).buffer;\n // Import the license token as key material\n const keyMaterial = await crypto.subtle.importKey(\n 'raw',\n new TextEncoder().encode(licenseToken),\n { name: 'PBKDF2' },\n false,\n ['deriveBits', 'deriveKey']\n );\n\n // Derive the actual key using PBKDF2\n return crypto.subtle.deriveKey(\n {\n name: 'PBKDF2',\n salt: normalizedSalt,\n iterations: 100000,\n hash: 'SHA-256',\n },\n keyMaterial,\n { name: 'AES-GCM', length: 256 },\n false,\n ['decrypt']\n );\n}\n\n/**\n * Decrypt WASM binary\n */\nasync function decryptWasm(\n encryptedData: ArrayBuffer,\n licenseToken: string\n): Promise {\n const buffer = new Uint8Array(encryptedData);\n\n // Verify magic\n const magic = new TextDecoder().decode(buffer.slice(0, 8));\n if (magic !== MAGIC) {\n throw new Error('Invalid WASM format: missing magic header');\n }\n\n // Parse header\n const view = new DataView(encryptedData);\n const originalSize = view.getUint32(8, true);\n const encryptedSize = view.getUint32(12, true);\n\n // Extract components\n const salt = buffer.slice(16, 48);\n const iv = buffer.slice(48, 60);\n const authTag = buffer.slice(HEADER_SIZE, HEADER_SIZE + AUTH_TAG_SIZE);\n const ciphertext = buffer.slice(HEADER_SIZE + AUTH_TAG_SIZE);\n\n // Verify sizes\n if (ciphertext.length !== encryptedSize) {\n throw new Error('Invalid WASM format: size mismatch');\n }\n\n // Derive key from license token\n const key = await deriveKey(licenseToken, salt);\n\n // Combine ciphertext + authTag for AES-GCM (Web Crypto expects them together)\n const combined = new Uint8Array(ciphertext.length + AUTH_TAG_SIZE);\n combined.set(ciphertext, 0);\n combined.set(authTag, ciphertext.length);\n\n // Decrypt\n try {\n const decrypted = await crypto.subtle.decrypt(\n {\n name: 'AES-GCM',\n iv,\n tagLength: AUTH_TAG_SIZE * 8,\n },\n key,\n combined\n );\n\n // Verify decrypted size\n if (decrypted.byteLength !== originalSize) {\n throw new Error('Decryption failed: size mismatch');\n }\n\n return decrypted;\n } catch (error) {\n throw new Error('Decryption failed: invalid license token');\n }\n}\n\n/**\n * Load WASM module from URL\n */\nasync function fetchWasm(url: string): Promise {\n const response = await fetch(url);\n\n if (!response.ok) {\n throw new Error(`Failed to fetch WASM: ${response.status}`);\n }\n\n return response.arrayBuffer();\n}\n\n/**\n * Compile and instantiate WASM module\n */\nasync function instantiateWasm(wasmBytes: ArrayBuffer): Promise {\n // Create import object for WASM\n const importObject = {\n env: {\n // Add any required imports here\n memory: new WebAssembly.Memory({ initial: 256, maximum: 512 }),\n },\n wbg: {\n // wasm-bindgen imports will be provided by the glue code\n },\n };\n\n // Compile and instantiate. TS lib definitions differ across targets:\n // instantiate() may resolve to an Instance or a { module, instance } pair.\n type WasmInstantiateResult =\n | WebAssembly.Instance\n | WebAssembly.WebAssemblyInstantiatedSource;\n const instantiateResult = (await WebAssembly.instantiate(\n wasmBytes,\n importObject\n )) as WasmInstantiateResult;\n const instance =\n instantiateResult instanceof WebAssembly.Instance\n ? instantiateResult\n : instantiateResult.instance;\n\n // The actual module interface is wrapped by wasm-bindgen glue code\n // This is a simplified version - in production, use wasm-bindgen's generated loader\n return instance.exports as unknown as WasmModule;\n}\n\n/**\n * Load the encrypted WASM core\n */\nexport async function loadWasmCore(config: LoaderConfig): Promise {\n if (state.loaded && state.module) {\n return state.module;\n }\n\n if (!config.licenseToken) {\n throw new Error('License token required to load WASM core');\n }\n\n const wasmUrl = config.wasmUrl || '/wasm/edgework_core.wasm.enc';\n\n try {\n // Fetch encrypted WASM\n const encryptedWasm = await fetchWasm(wasmUrl);\n\n // Check if encrypted (starts with EWASM001 magic)\n const magic = new TextDecoder().decode(\n new Uint8Array(encryptedWasm.slice(0, 8))\n );\n let wasmBytes: ArrayBuffer;\n\n if (magic === MAGIC) {\n // Decrypt\n wasmBytes = await decryptWasm(encryptedWasm, config.licenseToken);\n } else {\n // Unencrypted (development mode)\n wasmBytes = encryptedWasm;\n }\n\n // Instantiate\n const module = await instantiateWasm(wasmBytes);\n\n // Initialize with license\n await module.init(config.licenseToken);\n\n state.loaded = true;\n state.module = module;\n\n return module;\n } catch (error) {\n state.error = error instanceof Error ? error : new Error(String(error));\n throw state.error;\n }\n}\n\n/**\n * Check if WASM is loaded\n */\nexport function isWasmLoaded(): boolean {\n return state.loaded;\n}\n\n/**\n * Get loaded WASM module\n */\nexport function getWasmModule(): WasmModule | null {\n return state.module;\n}\n\n/**\n * Unload WASM module\n */\nexport function unloadWasm(): void {\n state.loaded = false;\n state.module = null;\n state.error = null;\n}\n\n/**\n * Streaming WASM loader for CLI\n * Loads WASM in chunks from server, never fully cached on disk\n */\nexport async function loadWasmStreaming(\n endpoint: string,\n sessionToken: string,\n functionNames: string[]\n): Promise unknown>> {\n const functions = new Map unknown>();\n\n for (const funcName of functionNames) {\n const response = await fetch(`${endpoint}/stream/${funcName}`, {\n headers: {\n Authorization: `Bearer ${sessionToken}`,\n },\n });\n\n if (!response.ok) {\n throw new Error(\n `Failed to stream function ${funcName}: ${response.status}`\n );\n }\n\n // Stream and compile the function chunk\n const wasmChunk = await response.arrayBuffer();\n\n // Implementation note: implementation, this would compile individual function modules\n // Current behavior: we just track what was requested\n functions.set(funcName, () => {\n console.log(`Executing streamed function: ${funcName}`);\n });\n }\n\n return functions;\n}\n","/**\n * Model Configurations Stubs\n */\n\nexport const COG_CONFIG = {\n id: 'cog-360m',\n architecture: 'smollm',\n createdAt: '2025-01-01T00:00:00.000Z',\n hiddenDim: 960,\n intermediateDim: 2560,\n maxSeqLength: 2048,\n name: 'Cog',\n numHeads: 15,\n numKvHeads: 5,\n numLayers: 32,\n ropeTheta: 10000,\n version: '0.1.0',\n vocabSize: 49152,\n} as const;\n\nexport const CYRANO_CONFIG = {\n id: 'cyrano-360m',\n architecture: 'smollm',\n createdAt: '2025-01-01T00:00:00.000Z',\n hiddenDim: 960,\n intermediateDim: 2560,\n maxSeqLength: 2048,\n name: 'Cyrano',\n numHeads: 15,\n numKvHeads: 5,\n numLayers: 32,\n ropeTheta: 10000,\n version: '0.1.0',\n vocabSize: 49152,\n} as const;\n\nexport const MODELS = ['cog-360m', 'cyrano-360m'];\n\nexport function getModelConfig(modelId: string) {\n if (modelId === 'cog-360m') return COG_CONFIG;\n if (modelId === 'cyrano-360m') return CYRANO_CONFIG;\n return null;\n}\n\nexport const SYSTEM_PROMPTS = {\n 'cog-360m': 'You are Cog, a DevOps assistant.',\n 'cyrano-360m': 'You are Cyrano, an emotional intelligence expert.',\n};\n\nexport function getSystemPrompt(modelId: string) {\n return SYSTEM_PROMPTS[modelId as keyof typeof SYSTEM_PROMPTS] || '';\n}\n\nexport const MODEL_SYNC_URLS = {\n 'cog-360m': 'https://models.affectively.ai/cog',\n 'cyrano-360m': 'https://models.affectively.ai/cyrano',\n};\n","/**\n * Edgework SDK\n *\n * Client-side AI inference with D1/Dash storage, WebGPU inference, and on-device RLHF.\n *\n * @example\n * ```typescript\n * import { Edgework } from '@affectively/edgework';\n *\n * // Initialize with a model\n * const ai = await Edgework.init({\n * model: 'cyrano-360m',\n * onProgress: (p) => console.log(`${p.percent}% downloaded`)\n * });\n *\n * // Generate text\n * const response = await ai.generate('How are you feeling today?');\n *\n * // Stream responses\n * for await (const token of ai.stream('Tell me a story')) {\n * process.stdout.write(token);\n * }\n *\n * // Chat with context\n * const reply = await ai.chat([\n * { role: 'user', content: 'I had a rough day' },\n * ]);\n * ```\n */\n\n// Re-export types (from types.ts only, schemas.ts exports its own versions)\nexport * from './types';\n\n// Re-export schema validators (not the types, which would conflict)\nexport {\n // Schemas\n ModelArchitectureSchema,\n QuantizationTypeSchema,\n ModelMetaSchema,\n TensorChunkSchema,\n TokenizerDataSchema,\n SyncProgressSchema,\n AdapterTypeSchema,\n UserAdapterSchema,\n TrainingLogEntrySchema,\n DownloadStatusSchema,\n DownloadProgressSchema,\n StorageBackendSchema,\n InferenceBackendSchema,\n EdgeworkOptionsSchema,\n GenerateOptionsSchema,\n GenerateResultSchema,\n ChatRoleSchema,\n ChatMessageSchema,\n ChatMessagesSchema,\n RLHFFeedbackSchema,\n ModelStatusSchema,\n EngineInfoSchema,\n LayerWeightsSchema,\n KVCacheSchema,\n CogModelConfigSchema,\n CyranoModelConfigSchema,\n // Validators\n validateModelMeta,\n validateGenerateOptions,\n validateChatMessages,\n validateEdgeworkOptions,\n validateRLHFFeedback,\n safeParseModelMeta,\n safeParseGenerateOptions,\n safeParseChatMessages,\n safeParseEdgeworkOptions,\n safeParseRLHFFeedback,\n} from './schemas';\n\n// Re-export modules\nexport {\n OPFSStorage,\n IndexedDBStorage,\n MemoryStorage,\n createStorage,\n detectBestBackend as detectBestStorageBackend,\n} from './data/storage';\nexport type { StorageOptions } from './data/storage';\n\nexport {\n WebGPUInference,\n WASMInference,\n createInferenceEngine,\n detectBestBackend as detectBestInferenceBackend,\n Tokenizer,\n loadTokenizer,\n} from './compute/inference';\nexport type { InferenceOptions } from './compute/inference';\n\nexport { ModelSync, createModelSync, DEFAULT_SYNC_URLS } from './data/sync';\nexport type { SyncOptions } from './data/sync';\n\nexport { RewardModel, RLHFTrainer, FederatedSync } from './compute/rlhf';\n\nexport * as Auth from './auth';\n\n// Export Gateway Client\nexport * as Gateway from './compute/gateway';\n\n// Export Distributed Inference Client\nexport * as Distributed from './compute/distributed';\n\n// Export WASM loader\nexport * as Wasm from './wasm';\n\nexport {\n COG_CONFIG,\n CYRANO_CONFIG,\n MODELS,\n getModelConfig,\n getSystemPrompt,\n SYSTEM_PROMPTS,\n MODEL_SYNC_URLS,\n} from './models';\n\n// Import for main class\nimport { createStorage as createStorageInternal } from './data/storage/factory';\nimport { createInferenceEngine as createInferenceEngineInternal } from './compute/inference/factory';\nimport { createModelSync as createModelSyncInternal } from './data/sync/factory';\nimport { RLHFTrainer as RLHFTrainerInternal } from './compute/rlhf/trainer';\nimport { FederatedSync } from './compute/rlhf/federated-sync';\nimport {\n getModelConfig as getModelConfigInternal,\n getSystemPrompt as getSystemPromptInternal,\n MODEL_SYNC_URLS as MODEL_SYNC_URLS_INTERNAL,\n} from './models';\nimport type { BaseStorage } from './data/storage/base-storage';\nimport type { BaseInference } from './compute/inference/base-inference';\nimport type { ModelSync as ModelSyncType } from './data/sync/model-sync';\nimport type {\n EdgeworkOptions,\n GenerateOptions,\n GenerateResult,\n ChatMessage,\n DownloadProgress,\n EngineInfo,\n RLHFFeedback,\n Modality,\n ModalityInferenceResult,\n InferenceError,\n ModalityTrainingSignal,\n TrainingFlushResult,\n} from './types';\nimport { EdgeworkOptionsSchema } from './schemas';\n\n/**\n * Edgework - Main SDK class\n *\n * Provides a unified interface for client-side AI inference.\n */\nexport class Edgework {\n private static readonly DEFAULT_TRAINING_BATCH_SIZE = 16;\n private static readonly DEFAULT_TRAINING_IDLE_FLUSH_MS = 45_000;\n\n private storage: BaseStorage;\n private inference: BaseInference | null = null;\n private sync: ModelSyncType | null = null;\n private rlhfTrainer: RLHFTrainerInternal | null = null;\n private modalityTrainers = new Map();\n private pendingSignals = new Map();\n private communityParticipation = false;\n private idleFlushTimer: ReturnType | null = null;\n private trainingBatchSize: number;\n private trainingIdleFlushMs: number;\n private modelId: string;\n private systemPrompt: string;\n private options: EdgeworkOptions;\n\n private constructor(\n storage: BaseStorage,\n modelId: string,\n options: EdgeworkOptions\n ) {\n this.storage = storage;\n this.modelId = modelId;\n this.options = options;\n this.trainingBatchSize =\n options.trainingBatchSize ?? Edgework.DEFAULT_TRAINING_BATCH_SIZE;\n this.trainingIdleFlushMs =\n options.trainingIdleFlushMs ?? Edgework.DEFAULT_TRAINING_IDLE_FLUSH_MS;\n this.communityParticipation = options.communityParticipation ?? false;\n this.systemPrompt = getSystemPromptInternal(modelId);\n }\n\n /**\n * Initialize Edgework with a model\n */\n static async init(options: EdgeworkOptions): Promise {\n // Validate options\n const parsedOptions = EdgeworkOptionsSchema.parse(options);\n if (!parsedOptions.model) {\n throw new Error('Model is required');\n }\n const validated: EdgeworkOptions = {\n ...parsedOptions,\n model: parsedOptions.model,\n };\n\n // Create storage\n const storage = await createStorageInternal({\n backend: validated.storageBackend,\n });\n\n const edgework = new Edgework(storage, validated.model, validated);\n\n // Check if model needs to be downloaded\n const isComplete = await storage.isModelComplete(validated.model);\n\n if (!isComplete) {\n // Start sync\n const syncUrl =\n validated.syncUrl ??\n MODEL_SYNC_URLS_INTERNAL[\n validated.model as keyof typeof MODEL_SYNC_URLS_INTERNAL\n ] ??\n 'https://models.affectively.ai';\n\n edgework.sync = createModelSyncInternal(storage, {\n syncUrl,\n onProgress: validated.onProgress,\n });\n\n // Register model metadata if not present\n const meta = await storage.getModelMeta(validated.model);\n if (!meta) {\n const config = getModelConfigInternal(validated.model);\n if (config) {\n await storage.setModelMeta(config);\n }\n }\n\n // Start download\n await edgework.sync.startSync(validated.model);\n }\n\n // Initialize inference engine\n edgework.inference = await createInferenceEngineInternal(\n storage,\n validated.model,\n {\n backend: validated.inferenceBackend,\n }\n );\n\n // Initialize RLHF if enabled\n if (validated.enableRLHF) {\n const config = getModelConfigInternal(validated.model);\n if (config) {\n const deviceId = validated.userId ?? edgework.generateDeviceId();\n const federatedSync = validated.federatedSyncUrl\n ? new FederatedSync({\n hubUrl: validated.federatedSyncUrl,\n modelId: validated.model,\n deviceId,\n })\n : undefined;\n\n edgework.rlhfTrainer = new RLHFTrainerInternal({\n storage,\n modelId: validated.model,\n userId: deviceId,\n hiddenDim: config.hiddenDim,\n modality: 'text',\n batchSize: edgework.trainingBatchSize,\n communityParticipation: edgework.communityParticipation,\n federatedSync,\n });\n await edgework.rlhfTrainer.initialize();\n edgework.modalityTrainers.set('text', edgework.rlhfTrainer);\n }\n }\n\n edgework.bindSessionFlushHooks();\n return edgework;\n }\n\n /**\n * Get an existing Edgework instance (assumes model is already downloaded)\n */\n static async get(modelId: string): Promise {\n const storage = await createStorageInternal();\n const edgework = new Edgework(storage, modelId, { model: modelId });\n\n // Initialize inference engine\n edgework.inference = await createInferenceEngineInternal(storage, modelId);\n\n return edgework;\n }\n\n /**\n * Generate text from a prompt\n */\n async generate(\n prompt: string,\n options: GenerateOptions = {}\n ): Promise {\n if (!this.inference) {\n throw new Error('Inference engine not initialized');\n }\n\n // Prepend system prompt\n const fullPrompt = this.systemPrompt\n ? `${this.systemPrompt}\\n\\nUser: ${prompt}\\nAssistant:`\n : prompt;\n\n return this.inference.generate(fullPrompt, options);\n }\n\n /**\n * Stream tokens from a prompt\n */\n async *stream(\n prompt: string,\n options: GenerateOptions = {}\n ): AsyncGenerator {\n if (!this.inference) {\n throw new Error('Inference engine not initialized');\n }\n\n const fullPrompt = this.systemPrompt\n ? `${this.systemPrompt}\\n\\nUser: ${prompt}\\nAssistant:`\n : prompt;\n\n yield* this.inference.stream(fullPrompt, options);\n }\n\n /**\n * Chat with conversation history\n */\n async chat(\n messages: ChatMessage[],\n options: GenerateOptions = {}\n ): Promise {\n if (!this.inference) {\n throw new Error('Inference engine not initialized');\n }\n\n // Add system message if not present\n const fullMessages: ChatMessage[] =\n messages[0]?.role === 'system'\n ? messages\n : [{ role: 'system', content: this.systemPrompt }, ...messages];\n\n return this.inference.chat(fullMessages, options);\n }\n\n /**\n * Get embeddings for text\n */\n async embed(text: string): Promise {\n if (!this.inference) {\n throw new Error('Inference engine not initialized');\n }\n return this.inference.embed(text);\n }\n\n /**\n * Provide feedback for RLHF (thumbs up/down)\n */\n async feedback(\n messageHash: string,\n rating: 'positive' | 'negative'\n ): Promise {\n if (!this.rlhfTrainer) {\n throw new Error('RLHF not enabled');\n }\n\n const hiddenState = this.inference?.getLastHiddenState();\n const feedback: RLHFFeedback = {\n messageHash,\n feedback: rating === 'positive' ? 1.0 : -1.0,\n hiddenState: hiddenState ?? undefined,\n };\n\n await this.rlhfTrainer.recordFeedback(feedback);\n }\n\n /**\n * Unified modality inference API.\n */\n async infer(\n modality: Modality,\n input: unknown,\n options: GenerateOptions = {}\n ): Promise {\n const startedAt = Date.now();\n try {\n if (!this.inference) {\n return this.createInferenceError(\n modality,\n 'MODEL_NOT_READY',\n 'Inference engine not initialized',\n 5_000\n );\n }\n\n if (modality === 'text') {\n if (typeof input !== 'string' || input.trim().length === 0) {\n return this.createInferenceError(\n modality,\n 'MODEL_UNAVAILABLE',\n 'Text modality requires a non-empty string input'\n );\n }\n\n const result = await this.generate(input, options);\n return {\n modality,\n modelId: this.modelId,\n modelVersion: 'local-v1',\n output: {\n text: result.text,\n tokenCount: result.tokenCount,\n tokensPerSecond: result.tokensPerSecond,\n },\n durationMs: result.durationMs,\n };\n }\n\n if (modality === 'embeddings') {\n if (typeof input !== 'string' || input.trim().length === 0) {\n return this.createInferenceError(\n modality,\n 'MODEL_UNAVAILABLE',\n 'Embeddings modality requires a non-empty string input'\n );\n }\n\n const embedding = await this.embed(input);\n return {\n modality,\n modelId: this.modelId,\n modelVersion: 'local-v1',\n output: Array.from(embedding),\n durationMs: Date.now() - startedAt,\n };\n }\n\n return this.createInferenceError(\n modality,\n 'MODEL_NOT_READY',\n `${modality} modality is not yet available in this SDK build`,\n 30_000\n );\n } catch (error) {\n return this.createInferenceError(\n modality,\n 'MODEL_UNAVAILABLE',\n error instanceof Error ? error.message : 'Unknown inference error'\n );\n }\n }\n\n /**\n * Record a modality-specific training signal for micro-batch training.\n */\n async recordSignal(\n modality: Modality,\n signal: ModalityTrainingSignal\n ): Promise {\n const queue = this.pendingSignals.get(modality) ?? [];\n queue.push(signal);\n this.pendingSignals.set(modality, queue);\n\n if (queue.length >= this.trainingBatchSize) {\n await this.flushTraining(modality);\n return;\n }\n\n this.scheduleIdleFlush();\n }\n\n /**\n * Flush pending local training for one modality or all modalities.\n */\n async flushTraining(modality?: Modality): Promise {\n const targetModalities: Modality[] = modality\n ? [modality]\n : ([\n 'text',\n 'stt',\n 'tts',\n 'translation',\n 'embeddings',\n 'classification',\n 'image',\n ] as Modality[]);\n const startedAt = new Date().toISOString();\n let trainedExamples = 0;\n let pendingExamples = 0;\n let communitySyncAttempted = false;\n let communitySyncApplied = false;\n\n for (const target of targetModalities) {\n const trainer = this.modalityTrainers.get(target);\n const signals = this.pendingSignals.get(target) ?? [];\n if (!trainer || signals.length === 0) {\n pendingExamples += signals.length;\n continue;\n }\n\n const before = trainer.getStats().examples;\n\n for (const signal of signals) {\n const hiddenState =\n signal.hiddenState ??\n this.inference?.getLastHiddenState() ??\n undefined;\n if (!hiddenState) {\n continue;\n }\n\n const feedbackValue =\n typeof signal.feedback === 'number' ? signal.feedback : 1;\n const feedback: RLHFFeedback = {\n messageHash:\n signal.messageHash ||\n `${target}-${Date.now()}-${Math.random()\n .toString(36)\n .slice(2, 10)}`,\n feedback: Math.max(-1, Math.min(1, feedbackValue)),\n hiddenState,\n };\n await trainer.recordFeedback(feedback);\n }\n\n await trainer.train();\n this.pendingSignals.set(target, []);\n\n if (this.communityParticipation) {\n communitySyncAttempted = true;\n await trainer.syncUpdates();\n communitySyncApplied = true;\n }\n\n const after = trainer.getStats().examples;\n trainedExamples += Math.max(0, after - before);\n }\n\n this.clearIdleFlushTimer();\n\n return {\n modality: modality ?? 'all',\n trainedExamples,\n pendingExamples,\n communitySyncAttempted,\n communitySyncApplied,\n flushedAt: startedAt,\n };\n }\n\n /**\n * Enable or disable community update sharing.\n */\n async setCommunityParticipation(enabled: boolean): Promise {\n this.communityParticipation = enabled;\n for (const trainer of this.modalityTrainers.values()) {\n trainer.setCommunityParticipation(enabled);\n }\n }\n\n /**\n * Get model status and info\n */\n get status(): EngineInfo | null {\n return this.inference?.getInfo() ?? null;\n }\n\n /**\n * Get download progress\n */\n get progress(): DownloadProgress | null {\n return this.sync?.getProgress() ?? null;\n }\n\n /**\n * Check if model is ready for inference\n */\n get isReady(): boolean {\n return this.inference?.getInfo().status === 'ready';\n }\n\n /**\n * Get RLHF training stats\n */\n get rlhfStats(): { examples: number; lastTrained?: string } | null {\n return this.rlhfTrainer?.getStats() ?? null;\n }\n\n /**\n * Generate a unique device ID for RLHF\n */\n private generateDeviceId(): string {\n if (typeof crypto !== 'undefined' && crypto.randomUUID) {\n return crypto.randomUUID();\n }\n // Fallback for older environments\n return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => {\n const r = (Math.random() * 16) | 0;\n const v = c === 'x' ? r : (r & 0x3) | 0x8;\n return v.toString(16);\n });\n }\n\n private scheduleIdleFlush(): void {\n if (this.idleFlushTimer) {\n return;\n }\n this.idleFlushTimer = setTimeout(() => {\n this.idleFlushTimer = null;\n void this.flushTraining();\n }, this.trainingIdleFlushMs);\n }\n\n private clearIdleFlushTimer(): void {\n if (!this.idleFlushTimer) {\n return;\n }\n clearTimeout(this.idleFlushTimer);\n this.idleFlushTimer = null;\n }\n\n private bindSessionFlushHooks(): void {\n if (\n typeof window !== 'undefined' &&\n typeof window.addEventListener === 'function'\n ) {\n window.addEventListener('beforeunload', () => {\n void this.flushTraining();\n });\n }\n\n const runtime = globalThis as {\n process?: { on?: (event: string, cb: () => void) => void };\n };\n if (runtime.process && typeof runtime.process.on === 'function') {\n runtime.process.on('beforeExit', () => {\n void this.flushTraining();\n });\n }\n }\n\n private createInferenceError(\n modality: Modality,\n code: InferenceError['code'],\n message: string,\n retryAfterMs?: number\n ): InferenceError {\n return {\n code,\n message,\n modality,\n modelId: this.modelId,\n modelVersion: 'local-v1',\n retryAfterMs,\n };\n }\n}\n\n// Default export\nexport default Edgework;\n"]}