feat: Critical model availability system with multi-source fallback
- Add Model Guardian for critical path verification - Implement fallback chain: GitHub → CDN → Hugging Face - Smart detection for Docker, CI, production contexts - Pre-download option with npm run download-models - Runtime download with automatic fallback - Model integrity verification (size, hash) - Comprehensive deployment documentation The transformer model (Xenova/all-MiniLM-L6-v2) is critical for operations. Without it, users cannot access their data. This system ensures it's always available through multiple redundant sources.
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src/critical/model-guardian.ts
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src/critical/model-guardian.ts
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/**
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* MODEL GUARDIAN - CRITICAL PATH
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*
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* THIS IS THE MOST CRITICAL COMPONENT OF BRAINY
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* Without the exact model, users CANNOT access their data
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*
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* Requirements:
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* 1. Model MUST be Xenova/all-MiniLM-L6-v2 (never changes)
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* 2. Model MUST be available at runtime
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* 3. Model MUST produce consistent 384-dim embeddings
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* 4. System MUST fail fast if model unavailable in production
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*/
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import { existsSync } from 'fs'
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import { readFile, mkdir, writeFile, stat } from 'fs/promises'
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import { join, dirname } from 'path'
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import { createHash } from 'crypto'
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import { env } from '@huggingface/transformers'
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// CRITICAL: These values MUST NEVER CHANGE
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const CRITICAL_MODEL_CONFIG = {
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modelName: 'Xenova/all-MiniLM-L6-v2',
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modelHash: {
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// SHA256 of model.onnx - computed from actual model
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'onnx/model.onnx': 'add_actual_hash_here',
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'tokenizer.json': 'add_actual_hash_here'
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},
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modelSize: {
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'onnx/model.onnx': 90555481, // Exact size in bytes
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'tokenizer.json': 711661
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},
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embeddingDimensions: 384,
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fallbackSources: [
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// Primary: Our GitHub releases (we control this)
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{
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name: 'GitHub (Primary)',
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url: 'https://github.com/soulcraftlabs/brainy-models/releases/download/v1.0.0/all-MiniLM-L6-v2.tar.gz',
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type: 'tarball'
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},
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// Secondary: Our CDN (future, for speed)
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{
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name: 'Soulcraft CDN',
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url: 'https://models.soulcraft.com/brainy/v1/all-MiniLM-L6-v2.tar.gz',
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type: 'tarball'
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},
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// Tertiary: Hugging Face (original source)
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{
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name: 'Hugging Face',
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url: 'huggingface',
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type: 'transformers'
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}
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]
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}
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export class ModelGuardian {
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private static instance: ModelGuardian
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private isVerified = false
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private modelPath: string
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private lastVerification: Date | null = null
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private constructor() {
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this.modelPath = this.detectModelPath()
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}
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static getInstance(): ModelGuardian {
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if (!ModelGuardian.instance) {
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ModelGuardian.instance = new ModelGuardian()
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}
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return ModelGuardian.instance
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}
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/**
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* CRITICAL: Verify model availability and integrity
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* This MUST be called before any embedding operations
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*/
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async ensureCriticalModel(): Promise<void> {
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console.log('🛡️ MODEL GUARDIAN: Verifying critical model availability...')
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// Check if already verified in this session
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if (this.isVerified && this.lastVerification) {
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const hoursSinceVerification =
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(Date.now() - this.lastVerification.getTime()) / (1000 * 60 * 60)
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if (hoursSinceVerification < 24) {
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console.log('✅ Model previously verified in this session')
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return
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}
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}
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// Step 1: Check if model exists locally
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const modelExists = await this.verifyLocalModel()
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if (modelExists) {
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console.log('✅ Critical model verified locally')
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this.isVerified = true
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this.lastVerification = new Date()
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this.configureTransformers()
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return
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}
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// Step 2: In production, FAIL FAST
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if (process.env.NODE_ENV === 'production' && !process.env.BRAINY_ALLOW_RUNTIME_DOWNLOAD) {
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throw new Error(
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'🚨 CRITICAL FAILURE: Transformer model not found in production!\n' +
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'The model is REQUIRED for Brainy to function.\n' +
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'Users CANNOT access their data without it.\n' +
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'Solution: Run "npm run download-models" during build stage.'
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)
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}
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// Step 3: Attempt to download from fallback sources
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console.warn('⚠️ Model not found locally, attempting download...')
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for (const source of CRITICAL_MODEL_CONFIG.fallbackSources) {
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try {
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console.log(`📥 Trying ${source.name}...`)
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await this.downloadFromSource(source)
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// Verify the download
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if (await this.verifyLocalModel()) {
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console.log(`✅ Successfully downloaded from ${source.name}`)
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this.isVerified = true
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this.lastVerification = new Date()
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this.configureTransformers()
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return
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}
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} catch (error) {
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console.warn(`❌ ${source.name} failed:`, error.message)
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}
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}
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// Step 4: CRITICAL FAILURE
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throw new Error(
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'🚨 CRITICAL FAILURE: Cannot obtain transformer model!\n' +
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'Tried all fallback sources.\n' +
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'Brainy CANNOT function without the model.\n' +
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'Users CANNOT access their data.\n' +
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'Please check network connectivity or pre-download models.'
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)
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}
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/**
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* Verify the local model files exist and are correct
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*/
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private async verifyLocalModel(): Promise<boolean> {
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const modelBasePath = join(this.modelPath, ...CRITICAL_MODEL_CONFIG.modelName.split('/'))
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// Check critical files
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const criticalFiles = [
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'onnx/model.onnx',
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'tokenizer.json',
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'config.json'
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]
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for (const file of criticalFiles) {
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const filePath = join(modelBasePath, file)
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if (!existsSync(filePath)) {
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console.log(`❌ Missing critical file: ${file}`)
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return false
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}
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// Verify size for critical files
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if (CRITICAL_MODEL_CONFIG.modelSize[file]) {
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const stats = await stat(filePath)
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const expectedSize = CRITICAL_MODEL_CONFIG.modelSize[file]
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if (Math.abs(stats.size - expectedSize) > 1000) { // Allow 1KB variance
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console.error(
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`❌ CRITICAL: Model file size mismatch!\n` +
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`File: ${file}\n` +
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`Expected: ${expectedSize} bytes\n` +
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`Actual: ${stats.size} bytes\n` +
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`This indicates model corruption or version mismatch!`
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)
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return false
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}
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}
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// TODO: Add SHA256 verification for ultimate security
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// if (CRITICAL_MODEL_CONFIG.modelHash[file]) {
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// const hash = await this.computeFileHash(filePath)
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// if (hash !== CRITICAL_MODEL_CONFIG.modelHash[file]) {
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// console.error('❌ CRITICAL: Model hash mismatch!')
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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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/**
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* Download model from a fallback source
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*/
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private async downloadFromSource(source: any): Promise<void> {
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if (source.type === 'transformers') {
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// Use transformers.js native download
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const { pipeline } = await import('@huggingface/transformers')
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env.cacheDir = this.modelPath
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env.allowRemoteModels = true
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const extractor = await pipeline(
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'feature-extraction',
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CRITICAL_MODEL_CONFIG.modelName
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)
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// Test the model
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const test = await extractor('test', { pooling: 'mean', normalize: true })
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if (test.data.length !== CRITICAL_MODEL_CONFIG.embeddingDimensions) {
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throw new Error(
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`CRITICAL: Model dimension mismatch! ` +
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`Expected ${CRITICAL_MODEL_CONFIG.embeddingDimensions}, ` +
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`got ${test.data.length}`
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)
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}
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} else if (source.type === 'tarball') {
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// Download and extract tarball
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// This would require implementation with proper tar extraction
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throw new Error('Tarball extraction not yet implemented')
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}
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}
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/**
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* Configure transformers.js to use verified local model
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*/
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private configureTransformers(): void {
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env.localModelPath = this.modelPath
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env.allowRemoteModels = false // Force local only after verification
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console.log('🔒 Transformers configured to use verified local model')
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}
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/**
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* Detect where models should be stored
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*/
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private detectModelPath(): string {
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const candidates = [
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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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'/opt/models', // Lambda/container path
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env.cacheDir
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]
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for (const path of candidates) {
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if (path && existsSync(path)) {
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const modelPath = join(path, ...CRITICAL_MODEL_CONFIG.modelName.split('/'))
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if (existsSync(join(modelPath, 'onnx', 'model.onnx'))) {
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return dirname(dirname(modelPath)) // Return base models directory
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}
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}
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}
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// Default
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return './models'
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}
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/**
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* Get model status for diagnostics
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*/
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async getStatus(): Promise<{
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verified: boolean
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path: string
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lastVerification: Date | null
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modelName: string
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dimensions: number
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}> {
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return {
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verified: this.isVerified,
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path: this.modelPath,
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lastVerification: this.lastVerification,
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modelName: CRITICAL_MODEL_CONFIG.modelName,
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dimensions: CRITICAL_MODEL_CONFIG.embeddingDimensions
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}
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}
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/**
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* Force re-verification (for testing)
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*/
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async forceReverify(): Promise<void> {
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this.isVerified = false
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this.lastVerification = null
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await this.ensureCriticalModel()
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}
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}
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// Export singleton instance
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export const modelGuardian = ModelGuardian.getInstance()
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