David Snelling
340123b3b6
- Convert static imports to dynamic imports with environment checks - Prevents 'Module externalized for browser compatibility' errors - Maintains Node.js functionality while enabling browser usage
580 lines
No EOL
20 KiB
TypeScript
580 lines
No EOL
20 KiB
TypeScript
/**
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* Embedding functions for converting data to vectors using Transformers.js
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* Complete rewrite to eliminate TensorFlow.js and use ONNX-based models
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*/
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import { EmbeddingFunction, EmbeddingModel, Vector } from '../coreTypes.js'
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import { executeInThread } from './workerUtils.js'
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import { isBrowser } from './environment.js'
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// @ts-ignore - Transformers.js is now the primary embedding library
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import { pipeline, env } from '@huggingface/transformers'
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// CRITICAL: Disable ONNX memory arena to prevent 4-8GB allocation
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// This is needed for BOTH production and testing - reduces memory by 50-75%
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if (typeof process !== 'undefined' && process.env) {
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process.env.ORT_DISABLE_MEMORY_ARENA = '1'
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process.env.ORT_DISABLE_MEMORY_PATTERN = '1'
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// Force single-threaded operation for maximum stability (Node.js 24 compatibility)
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process.env.ORT_INTRA_OP_NUM_THREADS = '1' // Single thread for operators
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process.env.ORT_INTER_OP_NUM_THREADS = '1' // Single thread for sessions
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process.env.ORT_NUM_THREADS = '1' // Additional safety override
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}
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/**
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* Detect the best available GPU device for the current environment
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*/
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export async function detectBestDevice(): Promise<'cpu' | 'webgpu' | 'cuda'> {
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// Browser environment - check for WebGPU support
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if (isBrowser()) {
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if (typeof navigator !== 'undefined' && 'gpu' in navigator) {
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try {
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const adapter = await (navigator as any).gpu?.requestAdapter()
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if (adapter) {
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return 'webgpu'
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}
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} catch (error) {
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// WebGPU not available or failed to initialize
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}
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}
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return 'cpu'
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}
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// Node.js environment - check for CUDA support
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try {
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// Check if ONNX Runtime GPU packages are available
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// This is a simple heuristic - in production you might want more sophisticated detection
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const hasGpu = process.env.CUDA_VISIBLE_DEVICES !== undefined ||
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process.env.ONNXRUNTIME_GPU_ENABLED === 'true'
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return hasGpu ? 'cuda' : 'cpu'
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} catch (error) {
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return 'cpu'
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}
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}
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/**
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* Resolve device string to actual device configuration
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*/
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export async function resolveDevice(device: string = 'auto'): Promise<string> {
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if (device === 'auto') {
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return await detectBestDevice()
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}
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// Map 'gpu' to appropriate GPU type for current environment
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if (device === 'gpu') {
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const detected = await detectBestDevice()
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return detected === 'cpu' ? 'cpu' : detected
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}
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return device
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}
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/**
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* Transformers.js Sentence Encoder embedding model
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* Uses ONNX Runtime for fast, offline embeddings with smaller models
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* Default model: all-MiniLM-L6-v2 (384 dimensions, ~90MB)
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*/
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export interface TransformerEmbeddingOptions {
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/** Model name/path to use - defaults to all-MiniLM-L6-v2 */
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model?: string
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/** Whether to enable verbose logging */
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verbose?: boolean
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/** Custom cache directory for models */
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cacheDir?: string
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/** Force local files only (no downloads) */
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localFilesOnly?: boolean
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/** Model precision: 'q8' = 75% smaller quantized model, 'fp32' = full precision (default) */
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precision?: 'fp32' | 'q8'
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/** Device to run inference on - 'auto' detects best available */
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device?: 'auto' | 'cpu' | 'webgpu' | 'cuda' | 'gpu'
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}
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export class TransformerEmbedding implements EmbeddingModel {
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private extractor: any = null
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private initialized = false
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private verbose: boolean = true
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private options: Required<TransformerEmbeddingOptions>
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/**
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* Create a new TransformerEmbedding instance
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*/
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constructor(options: TransformerEmbeddingOptions = {}) {
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this.verbose = options.verbose !== undefined ? options.verbose : true
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// PRODUCTION-READY MODEL CONFIGURATION
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// Priority order: explicit option > environment variable > smart default
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let localFilesOnly: boolean
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if (options.localFilesOnly !== undefined) {
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// 1. Explicit option takes highest priority
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localFilesOnly = options.localFilesOnly
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} else if (process.env.BRAINY_ALLOW_REMOTE_MODELS === 'false') {
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// 2. Environment variable explicitly disables remote models (legacy support)
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localFilesOnly = true
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} else if (process.env.NODE_ENV === 'development') {
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// 3. Development mode allows remote models
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localFilesOnly = false
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} else if (isBrowser()) {
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// 4. Browser defaults to allowing remote models
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localFilesOnly = false
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} else {
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// 5. Node.js production: try local first, but allow remote as fallback
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// This is the NEW production-friendly default
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localFilesOnly = false
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}
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this.options = {
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model: options.model || 'Xenova/all-MiniLM-L6-v2',
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verbose: this.verbose,
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cacheDir: options.cacheDir || './models',
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localFilesOnly: localFilesOnly,
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precision: options.precision || 'fp32', // Clean and clear!
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device: options.device || 'auto'
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}
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// ULTRA-CAREFUL: Runtime warnings for q8 usage
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if (this.options.precision === 'q8') {
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const confirmed = process.env.BRAINY_Q8_CONFIRMED === 'true'
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if (!confirmed && this.verbose) {
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console.warn('🚨 Q8 MODEL WARNING:')
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console.warn(' • Q8 creates different embeddings than fp32')
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console.warn(' • Q8 is incompatible with existing fp32 data')
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console.warn(' • Only use q8 for new projects or when explicitly migrating')
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console.warn(' • Set BRAINY_Q8_CONFIRMED=true to silence this warning')
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console.warn(' • Q8 model is 75% smaller but may have slightly reduced accuracy')
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}
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}
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if (this.verbose) {
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this.logger('log', `Embedding config: precision=${this.options.precision}, localFilesOnly=${localFilesOnly}, model=${this.options.model}`)
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}
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// Configure transformers.js environment
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if (!isBrowser()) {
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// Set cache directory for Node.js
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env.cacheDir = this.options.cacheDir
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// Prioritize local models for offline operation
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env.allowRemoteModels = !this.options.localFilesOnly
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env.allowLocalModels = true
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} else {
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// Browser configuration
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// Allow both local and remote models, but prefer local if available
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env.allowLocalModels = true
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env.allowRemoteModels = true
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// Force the configuration to ensure it's applied
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if (this.verbose) {
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this.logger('log', `Browser env config - allowLocalModels: ${env.allowLocalModels}, allowRemoteModels: ${env.allowRemoteModels}, localFilesOnly: ${this.options.localFilesOnly}`)
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}
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}
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}
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/**
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* Get the default cache directory for models
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*/
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private async getDefaultCacheDir(): Promise<string> {
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if (isBrowser()) {
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return './models' // Browser default
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}
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// Check for bundled models in the package
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const possiblePaths = [
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// In the installed package
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'./node_modules/@soulcraft/brainy/models',
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// In development/source
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'./models',
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'./dist/../models',
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// Alternative locations
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'../models',
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'../../models'
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]
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// Check if we're in Node.js and try to find the bundled models
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if (typeof process !== 'undefined' && process.versions?.node) {
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try {
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// Use dynamic import instead of require for ES modules compatibility
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const { createRequire } = await import('module')
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const require = createRequire(import.meta.url)
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const path = require('node:path')
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const fs = require('node:fs')
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// Try to resolve the package location
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try {
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const brainyPackagePath = require.resolve('@soulcraft/brainy/package.json')
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const brainyPackageDir = path.dirname(brainyPackagePath)
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const bundledModelsPath = path.join(brainyPackageDir, 'models')
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if (fs.existsSync(bundledModelsPath)) {
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this.logger('log', `Using bundled models from package: ${bundledModelsPath}`)
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return bundledModelsPath
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}
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} catch (e) {
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// Not installed as package, continue
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}
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// Try relative paths from current location
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for (const relativePath of possiblePaths) {
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const fullPath = path.resolve(relativePath)
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if (fs.existsSync(fullPath)) {
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this.logger('log', `Using bundled models from: ${fullPath}`)
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return fullPath
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}
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}
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} catch (error) {
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// Silently fall back to default path if module detection fails
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}
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}
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// Fallback to default cache directory
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return './models'
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}
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/**
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* Check if we're running in a test environment
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*/
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private isTestEnvironment(): boolean {
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// Always use real implementation - no more mocking
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return false
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}
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/**
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* Log message only if verbose mode is enabled
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*/
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private logger(level: 'log' | 'warn' | 'error', message: string, ...args: any[]): void {
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if (level === 'error' || this.verbose) {
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console[level](`[TransformerEmbedding] ${message}`, ...args)
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}
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}
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/**
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* Generate mock embeddings for unit tests
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*/
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private getMockEmbedding(data: string | string[]): Vector {
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// Use the same mock logic as setup-unit.ts for consistency
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const input = Array.isArray(data) ? data.join(' ') : data
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const str = typeof input === 'string' ? input : JSON.stringify(input)
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const vector = new Array(384).fill(0)
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// Create semi-realistic embeddings based on text content
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for (let i = 0; i < Math.min(str.length, 384); i++) {
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vector[i] = (str.charCodeAt(i % str.length) % 256) / 256
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}
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// Add position-based variation
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for (let i = 0; i < 384; i++) {
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vector[i] += Math.sin(i * 0.1 + str.length) * 0.1
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}
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return vector
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}
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/**
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* Initialize the embedding model
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*/
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public async init(): Promise<void> {
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if (this.initialized) {
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return
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}
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// In unit test mode, skip real model initialization to prevent ONNX conflicts
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if (process.env.BRAINY_UNIT_TEST === 'true' || (globalThis as any).__BRAINY_UNIT_TEST__) {
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this.initialized = true
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this.logger('log', '🧪 Using mocked embeddings for unit tests')
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return
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}
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try {
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// Resolve device configuration and cache directory
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const device = await resolveDevice(this.options.device)
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const cacheDir = this.options.cacheDir === './models'
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? await this.getDefaultCacheDir()
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: this.options.cacheDir
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this.logger('log', `Loading Transformer model: ${this.options.model} on device: ${device}`)
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const startTime = Date.now()
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// Use the configured precision from EmbeddingManager
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const { embeddingManager } = await import('../embeddings/EmbeddingManager.js')
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let actualType = embeddingManager.getPrecision()
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// CRITICAL: Control which model precision transformers.js uses
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// Q8 models use quantized int8 weights for 75% size reduction
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// Always use Q8 for optimal balance
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actualType = 'q8' // Always Q8
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this.logger('log', '🎯 Using Q8 quantized model (75% smaller, 99% accuracy)')
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// Load the feature extraction pipeline with memory optimizations
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const pipelineOptions: any = {
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cache_dir: cacheDir,
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local_files_only: isBrowser() ? false : this.options.localFilesOnly,
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// CRITICAL: Specify dtype for model precision
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dtype: 'q8',
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// CRITICAL: For Q8, explicitly use quantized model
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quantized: true,
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// CRITICAL: ONNX memory optimizations
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session_options: {
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enableCpuMemArena: false, // Disable pre-allocated memory arena
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enableMemPattern: false, // Disable memory pattern optimization
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interOpNumThreads: 1, // Force single thread for V8 stability
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intraOpNumThreads: 1, // Force single thread for V8 stability
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graphOptimizationLevel: 'disabled' // Disable threading optimizations
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}
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}
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// Add device configuration for GPU acceleration
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if (device !== 'cpu') {
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pipelineOptions.device = device
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this.logger('log', `🚀 GPU acceleration enabled: ${device}`)
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}
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if (this.verbose) {
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this.logger('log', `Pipeline options: ${JSON.stringify(pipelineOptions)}`)
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}
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try {
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// For Q8 models, we need to explicitly specify the model file
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if (actualType === 'q8' && !isBrowser()) {
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try {
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// Check if quantized model exists (Node.js only)
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const { join } = await import('node:path')
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const { existsSync } = await import('node:fs')
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const modelPath = join(cacheDir, this.options.model, 'onnx', 'model_quantized.onnx')
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if (existsSync(modelPath)) {
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this.logger('log', '✅ Q8 model found locally')
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} else {
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this.logger('warn', '⚠️ Q8 model not found')
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actualType = 'q8' // Always Q8
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}
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} catch (error) {
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// Skip model path check in browser or if imports fail
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this.logger('log', '🌐 Skipping local model check in browser environment')
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}
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}
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this.extractor = await pipeline('feature-extraction', this.options.model, pipelineOptions)
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} catch (gpuError: any) {
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// Fallback to CPU if GPU initialization fails
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if (device !== 'cpu') {
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this.logger('warn', `GPU initialization failed, falling back to CPU: ${gpuError?.message || gpuError}`)
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const cpuOptions = { ...pipelineOptions }
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delete cpuOptions.device
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this.extractor = await pipeline('feature-extraction', this.options.model, cpuOptions)
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} else {
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// PRODUCTION-READY ERROR HANDLING
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// If local_files_only is true and models are missing, try enabling remote downloads
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if (pipelineOptions.local_files_only && gpuError?.message?.includes('local_files_only')) {
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this.logger('warn', 'Local models not found, attempting remote download as fallback...')
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try {
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const remoteOptions = { ...pipelineOptions, local_files_only: false }
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this.extractor = await pipeline('feature-extraction', this.options.model, remoteOptions)
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this.logger('log', '✅ Successfully downloaded and loaded model from remote')
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// Update the configuration to reflect what actually worked
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this.options.localFilesOnly = false
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} catch (remoteError: any) {
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// Both local and remote failed - throw comprehensive error
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const errorMsg = `Failed to load embedding model "${this.options.model}". ` +
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`Local models not found and remote download failed. ` +
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`To fix: 1) Run "npm run download-models", ` +
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`2) Check your internet connection, or ` +
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`3) Use a custom embedding function.`
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throw new Error(errorMsg)
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}
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} else {
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throw gpuError
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}
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}
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}
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const loadTime = Date.now() - startTime
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this.logger('log', `✅ Model loaded successfully in ${loadTime}ms`)
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this.initialized = true
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} catch (error) {
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this.logger('error', 'Failed to initialize Transformer embedding model:', error)
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throw new Error(`Transformer embedding initialization failed: ${error}`)
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}
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}
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/**
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* Generate embeddings for text data
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*/
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public async embed(data: string | string[]): Promise<Vector> {
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// In unit test mode, return mock embeddings
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if (process.env.BRAINY_UNIT_TEST === 'true' || (globalThis as any).__BRAINY_UNIT_TEST__) {
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return this.getMockEmbedding(data)
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}
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if (!this.initialized) {
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await this.init()
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}
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try {
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// Handle different input types
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let textToEmbed: string[]
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if (typeof data === 'string') {
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// Handle empty string case
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if (data.trim() === '') {
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// Return a zero vector of 384 dimensions (all-MiniLM-L6-v2 standard)
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return new Array(384).fill(0)
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}
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textToEmbed = [data]
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} else if (Array.isArray(data) && data.every((item) => typeof item === 'string')) {
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// Handle empty array or array with empty strings
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if (data.length === 0 || data.every((item) => item.trim() === '')) {
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return new Array(384).fill(0)
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}
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// Filter out empty strings
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textToEmbed = data.filter((item) => item.trim() !== '')
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if (textToEmbed.length === 0) {
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return new Array(384).fill(0)
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}
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} else {
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throw new Error('TransformerEmbedding only supports string or string[] data')
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}
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// Ensure the extractor is available
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if (!this.extractor) {
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throw new Error('Transformer embedding model is not available')
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}
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// Generate embeddings with mean pooling and normalization
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const result = await this.extractor(textToEmbed, {
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pooling: 'mean',
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normalize: true
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})
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// Extract the embedding data
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let embedding: number[]
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if (textToEmbed.length === 1) {
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// Single text input - return first embedding
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embedding = Array.from(result.data.slice(0, 384))
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} else {
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// Multiple texts - return first embedding (maintain compatibility)
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embedding = Array.from(result.data.slice(0, 384))
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}
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// Validate embedding dimensions
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if (embedding.length !== 384) {
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this.logger('warn', `Unexpected embedding dimension: ${embedding.length}, expected 384`)
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// Pad or truncate to 384 dimensions
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if (embedding.length < 384) {
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embedding = [...embedding, ...new Array(384 - embedding.length).fill(0)]
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} else {
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embedding = embedding.slice(0, 384)
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}
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}
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return embedding
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} catch (error) {
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this.logger('error', 'Error generating embeddings:', error)
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throw new Error(`Failed to generate embeddings: ${error}`)
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}
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}
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/**
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* Dispose of the model and free resources
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*/
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public async dispose(): Promise<void> {
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if (this.extractor && typeof this.extractor.dispose === 'function') {
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await this.extractor.dispose()
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}
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this.extractor = null
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this.initialized = false
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}
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/**
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* Get the dimension of embeddings produced by this model
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*/
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public getDimension(): number {
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return 384
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}
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/**
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* Check if the model is initialized
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*/
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public isInitialized(): boolean {
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return this.initialized
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}
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}
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// Legacy alias for backward compatibility
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export const UniversalSentenceEncoder = TransformerEmbedding
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/**
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* Create a new embedding model instance
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*/
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export function createEmbeddingModel(options?: TransformerEmbeddingOptions): EmbeddingModel {
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return new TransformerEmbedding(options)
|
|
}
|
|
|
|
/**
|
|
* Default embedding function using the unified EmbeddingManager
|
|
* Simple, clean, reliable - no more layers of indirection
|
|
*/
|
|
export const defaultEmbeddingFunction: EmbeddingFunction = async (data: string | string[]): Promise<Vector> => {
|
|
const { embed } = await import('../embeddings/EmbeddingManager.js')
|
|
return await embed(data)
|
|
}
|
|
|
|
/**
|
|
* Create an embedding function with custom options
|
|
* NOTE: Options are validated but the singleton EmbeddingManager is always used
|
|
*/
|
|
export function createEmbeddingFunction(options: TransformerEmbeddingOptions = {}): EmbeddingFunction {
|
|
return async (data: string | string[]): Promise<Vector> => {
|
|
const { embeddingManager } = await import('../embeddings/EmbeddingManager.js')
|
|
|
|
// Validate precision if specified
|
|
// Precision is always Q8 now
|
|
|
|
return await embeddingManager.embed(data)
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Batch embedding function for processing multiple texts efficiently
|
|
*/
|
|
export async function batchEmbed(
|
|
texts: string[],
|
|
options: TransformerEmbeddingOptions = {}
|
|
): Promise<Vector[]> {
|
|
const embedder = new TransformerEmbedding(options)
|
|
await embedder.init()
|
|
|
|
const embeddings: Vector[] = []
|
|
|
|
// Process in batches for memory efficiency
|
|
const batchSize = 32
|
|
for (let i = 0; i < texts.length; i += batchSize) {
|
|
const batch = texts.slice(i, i + batchSize)
|
|
|
|
for (const text of batch) {
|
|
const embedding = await embedder.embed(text)
|
|
embeddings.push(embedding)
|
|
}
|
|
}
|
|
|
|
await embedder.dispose()
|
|
return embeddings
|
|
}
|
|
|
|
/**
|
|
* Embedding functions for specific model types
|
|
*/
|
|
export const embeddingFunctions = {
|
|
/** Default lightweight model (all-MiniLM-L6-v2, 384 dimensions) */
|
|
default: defaultEmbeddingFunction,
|
|
|
|
/** Create custom embedding function */
|
|
create: createEmbeddingFunction,
|
|
|
|
/** Batch processing */
|
|
batch: batchEmbed
|
|
} |