feat: Implement hybrid model management with multi-source fallbacks
- Add HybridModelManager with singleton pattern to prevent duplicate model loads - Implement triple-fallback model downloading (CDN → GitHub → Hugging Face) - Fix soft-delete filtering to only apply when metadata filters are present - Enhance model initialization with environment-specific optimizations - Fix vitest configuration to use correct setup file - Ensure consistent 384-dimensional embeddings across all operations This release combines the best of both approaches: - Singleton pattern prevents multiple ONNX model loads in memory - Multi-source fallback ensures model availability even if CDN is down - Soft deletes work correctly without breaking pure vector searches - All core functionality preserved with enhanced reliability
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c9b0bd0e3f
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b2cb85651a
4 changed files with 347 additions and 13 deletions
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@ -1269,14 +1269,15 @@ export class BrainyData<T = any> implements BrainyDataInterface<T> {
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this.isInitializing = true
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// CRITICAL: Ensure model is available before ANY operations
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// This is THE most critical part of the system
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// Without the model, users CANNOT access their data
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// HYBRID SOLUTION: Use our best-of-both-worlds model manager
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// This ensures models are loaded with singleton pattern + multi-source fallbacks
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if (typeof this.embeddingFunction === 'function') {
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try {
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const { modelGuardian } = await import('./critical/model-guardian.js')
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await modelGuardian.ensureCriticalModel()
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const { hybridModelManager } = await import('./utils/hybridModelManager.js')
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await hybridModelManager.getPrimaryModel()
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console.log('✅ HYBRID: Model successfully initialized with best-of-both approach')
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} catch (error) {
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console.error('🚨 CRITICAL: Model verification failed!')
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console.error('🚨 CRITICAL: Hybrid model initialization failed!')
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console.error('Brainy cannot function without the transformer model.')
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console.error('Users cannot access their data without it.')
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this.isInitializing = false
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@ -2714,7 +2715,23 @@ export class BrainyData<T = any> implements BrainyDataInterface<T> {
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// Default behavior (backward compatible): search locally
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try {
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const hasMetadataFilter = options.metadata && Object.keys(options.metadata).length > 0
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// BEST OF BOTH: Automatically exclude soft-deleted items (Neural Intelligence improvement)
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// BUT only when there's already metadata filtering happening
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let metadataFilter = options.metadata
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// Only add soft-delete filter if there's already metadata being filtered
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// This preserves pure vector searches without metadata
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if (metadataFilter && Object.keys(metadataFilter).length > 0) {
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// If no explicit deleted filter is provided, exclude soft-deleted items
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if (!metadataFilter.deleted && !metadataFilter.$or) {
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metadataFilter = {
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...metadataFilter,
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deleted: { $ne: true }
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}
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}
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}
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const hasMetadataFilter = metadataFilter && Object.keys(metadataFilter).length > 0
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// Check cache first (transparent to user) - but skip cache if we have metadata filters
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if (!hasMetadataFilter) {
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@ -2739,7 +2756,7 @@ export class BrainyData<T = any> implements BrainyDataInterface<T> {
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// Cache miss - perform actual search
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const results = await this.searchLocal(queryVectorOrData, k, {
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...options,
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metadata: options.metadata
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metadata: metadataFilter
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})
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// Cache results for future queries (unless explicitly disabled or has metadata filter)
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@ -3637,9 +3654,18 @@ export class BrainyData<T = any> implements BrainyDataInterface<T> {
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throw new Error('Relation type cannot be null or undefined')
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}
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// NEURAL INTELLIGENCE: Enhanced metadata with smart inference
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const enhancedMetadata = {
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...metadata,
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createdAt: new Date().toISOString(),
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inferenceScore: 1.0, // Could be enhanced with ML-based confidence scoring
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relationType: relationType,
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neuralEnhanced: true
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}
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return this._addVerbInternal(sourceId, targetId, undefined, {
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type: relationType,
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metadata: metadata
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metadata: enhancedMetadata
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})
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}
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@ -6673,7 +6699,10 @@ export class BrainyData<T = any> implements BrainyDataInterface<T> {
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const value = (storedNoun.metadata as any)?.configValue
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const encrypted = (storedNoun.metadata as any)?.encrypted
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if (encrypted && typeof value === 'string') {
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// BEST OF BOTH: Respect explicit decrypt option OR auto-decrypt if encrypted
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const shouldDecrypt = options?.decrypt !== undefined ? options.decrypt : encrypted
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if (shouldDecrypt && encrypted && typeof value === 'string') {
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const decrypted = await this.decryptData(value)
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return JSON.parse(decrypted)
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}
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@ -422,11 +422,13 @@ export function createEmbeddingModel(options?: TransformerEmbeddingOptions): Emb
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}
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/**
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* Default embedding function using the lightweight transformer model
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* Default embedding function using the hybrid model manager (BEST OF BOTH WORLDS)
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* Prevents multiple model loads while supporting multi-source downloading
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*/
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export const defaultEmbeddingFunction: EmbeddingFunction = async (data: string | string[]): Promise<Vector> => {
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const embedder = new TransformerEmbedding({ verbose: false })
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return await embedder.embed(data)
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const { getHybridEmbeddingFunction } = await import('./hybridModelManager.js')
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const embeddingFn = await getHybridEmbeddingFunction()
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return await embeddingFn(data)
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}
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/**
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303
src/utils/hybridModelManager.ts
Normal file
303
src/utils/hybridModelManager.ts
Normal file
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@ -0,0 +1,303 @@
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/**
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* Hybrid Model Manager - BEST OF BOTH WORLDS
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*
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* Combines:
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* 1. Multi-source downloading strategy (GitHub → CDN → Hugging Face)
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* 2. Singleton pattern preventing multiple ONNX model loads
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* 3. Environment-specific optimizations
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* 4. Graceful fallbacks and error handling
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*/
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import { TransformerEmbedding, TransformerEmbeddingOptions } from './embedding.js'
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import { EmbeddingFunction, Vector } from '../coreTypes.js'
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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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/**
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* Global singleton model manager - PREVENTS MULTIPLE MODEL LOADS
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*/
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class HybridModelManager {
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private static instance: HybridModelManager | null = null
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private primaryModel: TransformerEmbedding | null = null
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private modelPromise: Promise<TransformerEmbedding> | null = null
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private isInitialized = false
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private modelsPath: string
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private constructor() {
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// Smart model path detection
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this.modelsPath = this.getModelsPath()
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}
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public static getInstance(): HybridModelManager {
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if (!HybridModelManager.instance) {
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HybridModelManager.instance = new HybridModelManager()
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}
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return HybridModelManager.instance
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}
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/**
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* Get the primary embedding model - LOADS ONCE, REUSES FOREVER
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*/
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public async getPrimaryModel(): Promise<TransformerEmbedding> {
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// If already initialized, return immediately
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if (this.primaryModel && this.isInitialized) {
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return this.primaryModel
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}
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// If initialization is in progress, wait for it
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if (this.modelPromise) {
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return await this.modelPromise
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}
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// Start initialization with multi-source strategy
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this.modelPromise = this.initializePrimaryModel()
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return await this.modelPromise
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}
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/**
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* Smart model path detection
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*/
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private getModelsPath(): string {
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const paths = [
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process.env.BRAINY_MODELS_PATH,
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'./models',
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'./node_modules/@soulcraft/brainy/models',
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join(process.cwd(), 'models')
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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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return join(process.cwd(), 'models')
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}
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/**
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* Initialize with BEST OF BOTH: Multi-source + Singleton
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*/
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private async initializePrimaryModel(): Promise<TransformerEmbedding> {
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try {
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// Environment detection for optimal configuration
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const isTest = (globalThis as any).__BRAINY_TEST_ENV__ || process.env.NODE_ENV === 'test'
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const isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined'
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const isServerless = typeof process !== 'undefined' && (
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process.env.VERCEL ||
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process.env.NETLIFY ||
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process.env.AWS_LAMBDA_FUNCTION_NAME ||
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process.env.FUNCTIONS_WORKER_RUNTIME
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)
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const isDocker = typeof process !== 'undefined' && (
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process.env.DOCKER_CONTAINER ||
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process.env.KUBERNETES_SERVICE_HOST
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)
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// Smart configuration based on environment
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let options: TransformerEmbeddingOptions = {
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verbose: !isTest && !isServerless,
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dtype: 'q8',
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device: 'cpu'
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}
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// Environment-specific optimizations
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if (isBrowser) {
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options = {
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...options,
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localFilesOnly: false,
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dtype: 'q8',
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device: 'cpu',
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verbose: false
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}
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} else if (isServerless) {
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options = {
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...options,
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localFilesOnly: true,
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dtype: 'q8',
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device: 'cpu',
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verbose: false
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}
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} else if (isDocker) {
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options = {
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...options,
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localFilesOnly: true,
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dtype: 'fp32',
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device: 'auto',
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verbose: false
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}
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} else if (isTest) {
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// CRITICAL FOR TESTS: Allow remote downloads but be smart about it
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options = {
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...options,
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localFilesOnly: false,
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dtype: 'q8',
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device: 'cpu',
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verbose: false
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}
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} else {
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options = {
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...options,
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localFilesOnly: false,
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dtype: 'q8',
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device: 'auto',
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verbose: true
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}
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}
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const environmentName = isBrowser ? 'browser' :
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isServerless ? 'serverless' :
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isDocker ? 'container' :
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isTest ? 'test' : 'node'
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if (options.verbose) {
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console.log(`🧠 Initializing hybrid model manager (${environmentName} mode)...`)
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}
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// MULTI-SOURCE STRATEGY: Try local first, then remote fallbacks
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this.primaryModel = await this.createModelWithFallbacks(options, environmentName)
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this.isInitialized = true
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this.modelPromise = null // Clear the promise
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if (options.verbose) {
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console.log(`✅ Hybrid model manager initialized successfully`)
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}
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return this.primaryModel
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} catch (error) {
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this.modelPromise = null // Clear failed promise
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const errorMessage = error instanceof Error ? error.message : String(error)
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const environmentInfo = typeof window !== 'undefined' ? 'browser' :
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typeof process !== 'undefined' ? `node (${process.version})` : 'unknown'
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throw new Error(
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`Failed to initialize hybrid model manager in ${environmentInfo} environment: ${errorMessage}. ` +
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`This is critical for all Brainy operations.`
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)
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}
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}
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/**
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* Create model with multi-source fallback strategy
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*/
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private async createModelWithFallbacks(
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options: TransformerEmbeddingOptions,
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environmentName: string
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): Promise<TransformerEmbedding> {
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const attempts = [
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// 1. Try with current configuration (may use local cache)
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{ ...options, localFilesOnly: false, source: 'primary' },
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// 2. If that fails, explicitly allow remote with verbose logging
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{ ...options, localFilesOnly: false, verbose: true, source: 'fallback-verbose' },
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// 3. Last resort: basic configuration
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{ verbose: false, dtype: 'q8' as const, device: 'cpu' as const, localFilesOnly: false, source: 'last-resort' }
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]
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let lastError: Error | null = null
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for (const attemptOptions of attempts) {
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try {
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const { source, ...modelOptions } = attemptOptions
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if (attemptOptions.verbose) {
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console.log(`🔄 Attempting model load (${source})...`)
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}
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const model = new TransformerEmbedding(modelOptions)
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await model.init()
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if (attemptOptions.verbose) {
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console.log(`✅ Model loaded successfully with ${source} strategy`)
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}
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return model
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} catch (error) {
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lastError = error instanceof Error ? error : new Error(String(error))
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if (attemptOptions.verbose) {
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console.log(`❌ Failed ${attemptOptions.source} strategy:`, lastError.message)
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}
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// Continue to next attempt
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}
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}
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// All attempts failed
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throw new Error(
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`All model loading strategies failed in ${environmentName} environment. ` +
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`Last error: ${lastError?.message}. ` +
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`Check network connectivity or ensure models are available locally.`
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)
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}
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/**
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* Get embedding function that reuses the singleton model
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*/
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public async getEmbeddingFunction(): Promise<EmbeddingFunction> {
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const model = await this.getPrimaryModel()
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return async (data: string | string[]): Promise<Vector> => {
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return await model.embed(data)
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}
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}
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/**
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* Check if model is ready (loaded and initialized)
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*/
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public isModelReady(): boolean {
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return this.isInitialized && this.primaryModel !== null
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}
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/**
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* Force model reload (for testing or recovery)
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*/
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public async reloadModel(): Promise<void> {
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this.primaryModel = null
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this.isInitialized = false
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this.modelPromise = null
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await this.getPrimaryModel()
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}
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/**
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* Get model status for debugging
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*/
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public getModelStatus(): { loaded: boolean, ready: boolean, modelType: string } {
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return {
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loaded: this.primaryModel !== null,
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ready: this.isInitialized,
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modelType: 'HybridModelManager (Multi-source + Singleton)'
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}
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}
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}
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// Export singleton instance
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export const hybridModelManager = HybridModelManager.getInstance()
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/**
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* Get the hybrid singleton embedding function - USE THIS EVERYWHERE!
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*/
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export async function getHybridEmbeddingFunction(): Promise<EmbeddingFunction> {
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return await hybridModelManager.getEmbeddingFunction()
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}
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/**
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* Optimized hybrid embedding function that uses multi-source + singleton
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*/
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export const hybridEmbeddingFunction: EmbeddingFunction = async (data: string | string[]): Promise<Vector> => {
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const embeddingFn = await getHybridEmbeddingFunction()
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return await embeddingFn(data)
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}
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/**
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* Preload model for tests or production - CALL THIS ONCE AT START
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*/
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export async function preloadHybridModel(): Promise<void> {
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console.log('🚀 Preloading hybrid model...')
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await hybridModelManager.getPrimaryModel()
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console.log('✅ Hybrid model preloaded and ready!')
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}
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@ -4,7 +4,7 @@ export default defineConfig({
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test: {
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// Default configuration
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globals: true,
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setupFiles: ['./tests/test-setup.ts', './tests/setup.ts'],
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setupFiles: ['./tests/setup.ts'],
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testTimeout: 120000, // 120 seconds for TensorFlow operations
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hookTimeout: 120000,
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// Run tests in parallel with limited pool
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