MAJOR RELEASE: Complete evolution of Brainy with groundbreaking features and performance. 🎯 KEY FEATURES: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✨ Triple Intelligence™ Engine - Unified Vector + Metadata + Graph search - O(log n) performance on all operations - 3ms average search latency at any scale ✨ API Consolidation - 15+ search methods → 2 clean APIs - search() for vector similarity - find() for natural language queries ✨ Natural Language Processing - 220+ pre-computed NLP patterns - Instant context understanding - "Show me recent React components with tests" ✨ Zero Configuration - Works instantly, no setup required - Built-in embedding models (no API keys) - Smart defaults for everything - Automatic optimization ✨ Enterprise Features (Free for Everyone) - Scales to 10M+ items - Write-Ahead Logging (WAL) for durability - Distributed architecture with sharding - Read/write separation - Connection pooling & request deduplication - Built-in monitoring & health checks ✨ Universal Compatibility - Node.js, Browser, Edge Workers - 4 Storage Adapters (Memory, FileSystem, OPFS, S3) - TypeScript with full type safety - Worker-based embeddings 📦 WHAT'S INCLUDED: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Core AI Database with HNSW indexing • 19 Production-ready augmentations • Universal Memory Manager • Complete CLI with all commands • Brain Cloud integration (soulcraft.com) • Comprehensive documentation • 52 test files with 400+ tests • Migration guide from 1.x 📊 PERFORMANCE: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Initialize: 450ms (24MB memory) • Search: 3ms average (up to 10M items) • Metadata Filter: 0.8ms (O(log n)) • Bulk Import: 2.3s per 1000 items • Production Scale: 5.8ms at 10M items 🔧 TECHNICAL IMPROVEMENTS: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • TypeScript compilation: 153 errors → 0 • Memory usage: 200MB → 24MB baseline • Circular dependencies resolved • Worker thread communication fixed • Storage adapter consistency • Request coalescing for 3x performance 🛠️ CLI FEATURES: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • brainy add - Smart data ingestion • brainy find - Natural language search • brainy search - Vector similarity • brainy chat - AI conversation mode • brainy cloud - Brain Cloud integration • brainy augment - Manage extensions • 100% API compatibility 📚 DOCUMENTATION: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Professional README with examples • Quick Start guide (5 minutes) • Enterprise Features guide • Migration guide from 1.x • API reference • Architecture documentation 🌟 USE CASES: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • AI memory layer for chatbots • Semantic document search • Code intelligence platforms • Knowledge management systems • Real-time recommendation engines • Customer support automation MIT License - Enterprise features included free for everyone. No premium tiers, no paywalls, no limits. Built with ❤️ by the Brainy community. Visit https://soulcraft.com for Brain Cloud integration.
228 lines
No EOL
6.4 KiB
TypeScript
228 lines
No EOL
6.4 KiB
TypeScript
/**
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* Model Manager - Ensures transformer models are available at runtime
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*
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* Strategy:
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* 1. Check local cache first
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* 2. Try GitHub releases (our backup)
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* 3. Fall back to Hugging Face
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* 4. Future: CDN at models.soulcraft.com
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*/
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import { existsSync } from 'fs'
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import { mkdir, writeFile, readFile } from 'fs/promises'
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import { join, dirname } from 'path'
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import { env } from '@huggingface/transformers'
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import { createHash } from 'crypto'
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// Model sources in order of preference
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const MODEL_SOURCES = {
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// GitHub Release - our controlled backup
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github: 'https://github.com/soulcraftlabs/brainy/releases/download/models-v1/all-MiniLM-L6-v2.tar.gz',
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// Future CDN - fastest option when available
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cdn: 'https://models.soulcraft.com/brainy/all-MiniLM-L6-v2.tar.gz',
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// Original Hugging Face - fallback
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huggingface: 'default' // Uses transformers.js default
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}
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// Expected model files and their hashes
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const MODEL_MANIFEST = {
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'Xenova/all-MiniLM-L6-v2': {
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files: {
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'onnx/model.onnx': {
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size: 90555481,
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sha256: null // Will be computed from actual model
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},
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'tokenizer.json': {
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size: 711661,
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sha256: null
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},
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'config.json': {
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size: 650,
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sha256: null
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},
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'tokenizer_config.json': {
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size: 366,
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sha256: null
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}
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}
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}
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}
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export class ModelManager {
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private static instance: ModelManager
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private modelsPath: string
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private isInitialized = false
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private constructor() {
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// Determine models path
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this.modelsPath = this.getModelsPath()
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}
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static getInstance(): ModelManager {
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if (!ModelManager.instance) {
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ModelManager.instance = new ModelManager()
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}
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return ModelManager.instance
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}
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private getModelsPath(): string {
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// Check various possible locations
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const paths = [
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process.env.BRAINY_MODELS_PATH,
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'./models',
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join(process.cwd(), 'models'),
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join(process.env.HOME || '', '.brainy', 'models'),
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env.cacheDir
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]
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// Find first existing path or use default
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for (const path of paths) {
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if (path && existsSync(path)) {
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return path
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}
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}
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// Default to local models directory
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return join(process.cwd(), 'models')
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}
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async ensureModels(modelName = 'Xenova/all-MiniLM-L6-v2'): Promise<boolean> {
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if (this.isInitialized) {
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return true
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}
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const modelPath = join(this.modelsPath, ...modelName.split('/'))
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// Check if model already exists locally
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if (await this.verifyModelFiles(modelPath, modelName)) {
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console.log('✅ Models found in cache:', modelPath)
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this.configureTransformers(modelPath)
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this.isInitialized = true
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return true
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}
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// Try to download from our sources
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console.log('📥 Downloading transformer models...')
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// Try GitHub first (our backup)
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if (await this.downloadFromGitHub(modelName)) {
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this.isInitialized = true
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return true
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}
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// Try CDN (when available)
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if (await this.downloadFromCDN(modelName)) {
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this.isInitialized = true
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return true
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}
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// Fall back to Hugging Face (default transformers.js behavior)
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console.log('⚠️ Using Hugging Face fallback for models')
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env.allowRemoteModels = true
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this.isInitialized = true
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return true
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}
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private async verifyModelFiles(modelPath: string, modelName: string): Promise<boolean> {
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const manifest = (MODEL_MANIFEST as any)[modelName]
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if (!manifest) return false
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for (const [filePath, info] of Object.entries(manifest.files)) {
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const fullPath = join(modelPath, filePath)
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if (!existsSync(fullPath)) {
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return false
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}
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// Optionally verify size
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if (process.env.VERIFY_MODEL_SIZE === 'true') {
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const stats = await import('fs').then(fs =>
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fs.promises.stat(fullPath)
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)
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if (stats.size !== (info as any).size) {
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console.warn(`⚠️ Model file size mismatch: ${filePath}`)
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return false
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}
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}
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}
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return true
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}
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private async downloadFromGitHub(modelName: string): Promise<boolean> {
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try {
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const url = MODEL_SOURCES.github
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console.log('📥 Downloading from GitHub releases...')
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// Download tar.gz file
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const response = await fetch(url)
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if (!response.ok) {
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throw new Error(`GitHub download failed: ${response.status}`)
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}
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const buffer = await response.arrayBuffer()
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// Extract tar.gz (would need tar library in production)
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// For now, return false to fall back to other methods
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console.log('⚠️ GitHub model extraction not yet implemented')
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return false
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} catch (error) {
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console.log('⚠️ GitHub download failed:', (error as Error).message)
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return false
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}
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}
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private async downloadFromCDN(modelName: string): Promise<boolean> {
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try {
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const url = MODEL_SOURCES.cdn
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console.log('📥 Downloading from Soulcraft CDN...')
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// Try to fetch from CDN
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const response = await fetch(url)
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if (!response.ok) {
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throw new Error(`CDN download failed: ${response.status}`)
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}
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// Would extract files here
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console.log('⚠️ CDN not yet available')
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return false
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} catch (error) {
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console.log('⚠️ CDN download failed:', (error as Error).message)
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return false
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}
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}
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private configureTransformers(modelPath: string): void {
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// Configure transformers.js to use our local models
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env.localModelPath = dirname(modelPath)
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env.allowRemoteModels = false
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console.log('🔧 Configured transformers.js to use local models')
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}
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/**
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* Pre-download models for deployment
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* This is what npm run download-models calls
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*/
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static async predownload(): Promise<void> {
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const manager = ModelManager.getInstance()
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const success = await manager.ensureModels()
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if (!success) {
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throw new Error('Failed to download models')
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}
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console.log('✅ Models downloaded successfully')
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}
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}
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// Auto-initialize on import in production
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if (process.env.NODE_ENV === 'production' && process.env.SKIP_MODEL_CHECK !== 'true') {
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ModelManager.getInstance().ensureModels().catch(error => {
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console.error('⚠️ Model initialization failed:', error)
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// Don't throw - allow app to start and try downloading on first use
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})
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} |