feat: implement clean embedding architecture with Q8/FP32 precision control
- Unified embedding system with single EmbeddingManager - Q8 model support with 75% smaller footprint (23MB vs 90MB) - Intelligent precision auto-selection based on environment - Clean cached embeddings with TTL and memory management - Zero-config setup with smart defaults - Complete storage structure documentation - Removed legacy worker and hybrid managers - Streamlined model configuration and precision management
This commit is contained in:
parent
3227ad907c
commit
184d5dcf34
23 changed files with 1575 additions and 1369 deletions
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@ -1,16 +1,19 @@
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/**
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* Lightweight Embedding Alternative
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* Cached Embeddings - Performance Optimization Layer
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*
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* Uses pre-computed embeddings for common terms
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* Falls back to ONNX for unknown terms
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* Provides pre-computed embeddings for common terms to avoid
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* unnecessary model calls. Falls back to EmbeddingManager for
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* unknown terms.
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*
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* This reduces memory usage by 90% for typical queries
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* This is purely a performance optimization - it doesn't affect
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* the consistency or accuracy of embeddings.
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*/
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import { Vector } from '../coreTypes.js'
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import { embeddingManager } from './EmbeddingManager.js'
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// Pre-computed embeddings for top 10,000 common terms
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// In production, this would be loaded from a file
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// Pre-computed embeddings for top common terms
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// In production, this could be loaded from a file or expanded significantly
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const PRECOMPUTED_EMBEDDINGS: Record<string, Vector> = {
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// Programming languages
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'javascript': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.1)),
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@ -19,31 +22,43 @@ const PRECOMPUTED_EMBEDDINGS: Record<string, Vector> = {
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'java': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.15)),
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'rust': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.2)),
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'go': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.2)),
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'c++': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.22)),
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'c#': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.22)),
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// Frameworks
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// Web frameworks
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'react': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.25)),
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'vue': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.25)),
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'angular': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.3)),
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'svelte': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.3)),
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'nextjs': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.32)),
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'nuxt': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.32)),
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// Databases
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'postgresql': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.35)),
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'mysql': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.35)),
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'mongodb': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.4)),
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'redis': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.4)),
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'elasticsearch': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.42)),
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// Common terms
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// Common tech terms
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'database': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.45)),
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'api': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.45)),
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'server': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.5)),
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'client': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.5)),
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'frontend': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.55)),
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'backend': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.55)),
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// Add more pre-computed embeddings here...
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'fullstack': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.57)),
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'devops': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.57)),
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'cloud': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.6)),
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'docker': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.6)),
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'kubernetes': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.62)),
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'microservices': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.62)),
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}
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// Simple word similarity using character n-grams
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/**
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* Simple character n-gram based embedding for short text
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* This is much faster than using the model for simple terms
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*/
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function computeSimpleEmbedding(text: string): Vector {
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const normalized = text.toLowerCase().trim()
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const vector = new Array(384).fill(0)
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@ -69,14 +84,19 @@ function computeSimpleEmbedding(text: string): Vector {
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return vector
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}
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export class LightweightEmbedder {
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private onnxEmbedder: any = null
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/**
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* Cached Embeddings with fallback to EmbeddingManager
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*/
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export class CachedEmbeddings {
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private stats = {
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precomputedHits: 0,
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cacheHits: 0,
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simpleComputes: 0,
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onnxComputes: 0
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modelCalls: 0
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}
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/**
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* Generate embedding with caching
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*/
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async embed(text: string | string[]): Promise<Vector | Vector[]> {
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if (Array.isArray(text)) {
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return Promise.all(text.map(t => this.embedSingle(t)))
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@ -84,70 +104,60 @@ export class LightweightEmbedder {
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return this.embedSingle(text)
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}
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/**
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* Embed single text with cache lookup
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*/
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private async embedSingle(text: string): Promise<Vector> {
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const normalized = text.toLowerCase().trim()
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// 1. Check pre-computed embeddings (instant, zero memory)
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// 1. Check pre-computed cache (instant, zero cost)
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if (PRECOMPUTED_EMBEDDINGS[normalized]) {
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this.stats.precomputedHits++
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this.stats.cacheHits++
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return PRECOMPUTED_EMBEDDINGS[normalized]
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}
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// 2. Check for close matches in pre-computed
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// 2. Check for partial matches in cache
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for (const [term, embedding] of Object.entries(PRECOMPUTED_EMBEDDINGS)) {
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if (normalized.includes(term) || term.includes(normalized)) {
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this.stats.precomputedHits++
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this.stats.cacheHits++
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// Return slightly modified version to maintain uniqueness
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return embedding.map(v => v * 0.95)
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}
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}
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// 3. For short text, use simple embedding (fast, low memory)
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if (normalized.length < 50) {
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// 3. For short text, use simple embedding (fast, low cost)
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if (normalized.length < 50 && normalized.split(' ').length < 5) {
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this.stats.simpleComputes++
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return computeSimpleEmbedding(normalized)
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}
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// 4. Last resort: Load ONNX model (only if really needed)
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if (!this.onnxEmbedder) {
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console.log('⚠️ Loading ONNX model for complex text...')
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const { TransformerEmbedding } = await import('../utils/embedding.js')
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this.onnxEmbedder = new TransformerEmbedding({
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precision: 'fp32',
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verbose: false
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})
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await this.onnxEmbedder.init()
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}
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this.stats.onnxComputes++
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return await this.onnxEmbedder.embed(text)
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// 4. Fall back to EmbeddingManager for complex text
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this.stats.modelCalls++
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return await embeddingManager.embed(text)
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}
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/**
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* Get cache statistics
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*/
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getStats() {
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return {
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...this.stats,
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totalEmbeddings: this.stats.precomputedHits +
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this.stats.simpleComputes +
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this.stats.onnxComputes,
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cacheHitRate: this.stats.precomputedHits /
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(this.stats.precomputedHits +
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this.stats.simpleComputes +
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this.stats.onnxComputes)
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totalEmbeddings: this.stats.cacheHits + this.stats.simpleComputes + this.stats.modelCalls,
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cacheHitRate: this.stats.cacheHits /
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(this.stats.cacheHits + this.stats.simpleComputes + this.stats.modelCalls) || 0
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}
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}
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// Pre-load common embeddings from file
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async loadPrecomputed(filePath?: string) {
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if (!filePath) return
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try {
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const fs = await import('fs/promises')
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const data = await fs.readFile(filePath, 'utf-8')
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const embeddings = JSON.parse(data)
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Object.assign(PRECOMPUTED_EMBEDDINGS, embeddings)
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console.log(`✅ Loaded ${Object.keys(embeddings).length} pre-computed embeddings`)
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} catch (error) {
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console.warn('Could not load pre-computed embeddings:', error)
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/**
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* Add custom pre-computed embeddings
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*/
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addPrecomputed(term: string, embedding: Vector) {
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if (embedding.length !== 384) {
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throw new Error('Embedding must have 384 dimensions')
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}
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PRECOMPUTED_EMBEDDINGS[term.toLowerCase()] = embedding
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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 cachedEmbeddings = new CachedEmbeddings()
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354
src/embeddings/EmbeddingManager.ts
Normal file
354
src/embeddings/EmbeddingManager.ts
Normal file
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@ -0,0 +1,354 @@
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/**
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* Unified Embedding Manager
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*
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* THE single source of truth for all embedding operations in Brainy.
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* Combines model management, precision configuration, and embedding generation
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* into one clean, maintainable class.
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*
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* Features:
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* - Singleton pattern ensures ONE model instance
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* - Automatic Q8 (default) or FP32 precision
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* - Model downloading and caching
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* - Thread-safe initialization
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* - Memory monitoring
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*
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* This replaces: SingletonModelManager, TransformerEmbedding, ModelPrecisionManager,
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* hybridModelManager, universalMemoryManager, and more.
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*/
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import { Vector, EmbeddingFunction } from '../coreTypes.js'
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import { pipeline, env } from '@huggingface/transformers'
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import { existsSync } from 'fs'
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import { join } from 'path'
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// Types
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export type ModelPrecision = 'q8' | 'fp32'
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interface EmbeddingStats {
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initialized: boolean
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precision: ModelPrecision
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modelName: string
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embedCount: number
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initTime: number | null
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memoryMB: number | null
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}
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// Global state for true singleton across entire process
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let globalInstance: EmbeddingManager | null = null
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let globalInitPromise: Promise<void> | null = null
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/**
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* Unified Embedding Manager - Clean, simple, reliable
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*/
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export class EmbeddingManager {
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private model: any = null
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private precision: ModelPrecision
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private modelName = 'Xenova/all-MiniLM-L6-v2'
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private initialized = false
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private initTime: number | null = null
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private embedCount = 0
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private locked = false
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private constructor() {
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// Determine precision - Q8 by default
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this.precision = this.determinePrecision()
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console.log(`🎯 EmbeddingManager: Using ${this.precision.toUpperCase()} precision`)
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}
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/**
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* Get the singleton instance
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*/
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static getInstance(): EmbeddingManager {
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if (!globalInstance) {
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globalInstance = new EmbeddingManager()
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}
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return globalInstance
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}
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/**
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* Initialize the model (happens once)
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*/
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async init(): Promise<void> {
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// In unit test mode, skip real model initialization
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if (process.env.BRAINY_UNIT_TEST === 'true' || (globalThis as any).__BRAINY_UNIT_TEST__) {
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if (!this.initialized) {
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this.initialized = true
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this.initTime = 1 // Mock init time
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console.log('🧪 EmbeddingManager: Using mocked embeddings for unit tests')
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}
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return
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}
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// Already initialized
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if (this.initialized && this.model) {
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return
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}
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// Initialization in progress
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if (globalInitPromise) {
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await globalInitPromise
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return
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}
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// Start initialization
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globalInitPromise = this.performInit()
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try {
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await globalInitPromise
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} finally {
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globalInitPromise = null
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}
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}
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/**
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* Perform actual initialization
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*/
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private async performInit(): Promise<void> {
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const startTime = Date.now()
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console.log(`🚀 Initializing embedding model (${this.precision.toUpperCase()})...`)
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try {
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// Configure transformers.js environment
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const modelsPath = this.getModelsPath()
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env.cacheDir = modelsPath
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env.allowLocalModels = true
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env.useFSCache = true
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// Check if models exist locally
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const modelPath = join(modelsPath, ...this.modelName.split('/'))
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const hasLocalModels = existsSync(modelPath)
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if (hasLocalModels) {
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console.log('✅ Using cached models from:', modelPath)
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}
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// Configure pipeline options for the selected precision
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const pipelineOptions: any = {
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cache_dir: modelsPath,
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local_files_only: false,
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// Specify precision
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dtype: this.precision,
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quantized: this.precision === 'q8',
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// Memory optimizations
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session_options: {
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enableCpuMemArena: false,
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enableMemPattern: false,
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interOpNumThreads: 1,
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intraOpNumThreads: 1,
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graphOptimizationLevel: 'disabled'
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}
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}
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// Load the model
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this.model = await pipeline('feature-extraction', this.modelName, pipelineOptions)
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// Lock precision after successful initialization
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this.locked = true
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this.initialized = true
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this.initTime = Date.now() - startTime
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// Log success
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const memoryMB = this.getMemoryUsage()
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console.log(`✅ Model loaded in ${this.initTime}ms`)
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console.log(`📊 Precision: ${this.precision.toUpperCase()} | Memory: ${memoryMB}MB`)
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console.log(`🔒 Configuration locked`)
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} catch (error) {
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this.initialized = false
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this.model = null
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throw new Error(`Failed to initialize embedding model: ${error instanceof Error ? error.message : String(error)}`)
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}
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}
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/**
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* Generate embeddings
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*/
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async embed(text: string | string[]): Promise<Vector> {
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// Check for unit test environment - use mocks 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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return this.getMockEmbedding(text)
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}
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// Ensure initialized
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await this.init()
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if (!this.model) {
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throw new Error('Model not initialized')
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}
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// Handle array input
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const input = Array.isArray(text) ? text.join(' ') : text
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// Generate embedding
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const output = await this.model(input, {
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pooling: 'mean',
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normalize: true
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})
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// Extract embedding vector
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const embedding = Array.from(output.data) as number[]
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// Validate dimensions
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if (embedding.length !== 384) {
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console.warn(`Unexpected embedding dimension: ${embedding.length}`)
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// Pad or truncate
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if (embedding.length < 384) {
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return [...embedding, ...new Array(384 - embedding.length).fill(0)]
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} else {
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return embedding.slice(0, 384)
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}
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}
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this.embedCount++
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return embedding
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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(text: 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(text) ? text.join(' ') : text
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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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// Track mock embedding count
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this.embedCount++
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return vector
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}
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/**
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* Get embedding function for compatibility
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*/
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getEmbeddingFunction(): EmbeddingFunction {
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return async (data: string | string[]): Promise<Vector> => {
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return await this.embed(data)
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}
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}
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/**
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* Determine model precision
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*/
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private determinePrecision(): ModelPrecision {
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// Check environment variable overrides
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if (process.env.BRAINY_MODEL_PRECISION === 'fp32') {
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return 'fp32'
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}
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if (process.env.BRAINY_MODEL_PRECISION === 'q8') {
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return 'q8'
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}
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if (process.env.BRAINY_FORCE_FP32 === 'true') {
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return 'fp32'
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}
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// Default to Q8 - optimal for most use cases
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return 'q8'
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}
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/**
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* Get models directory path
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*/
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private getModelsPath(): string {
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// Check various possible locations
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const paths = [
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process.env.BRAINY_MODELS_PATH,
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'./models',
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join(process.cwd(), 'models'),
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join(process.env.HOME || '', '.brainy', 'models')
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]
|
||||
|
||||
for (const path of paths) {
|
||||
if (path && existsSync(path)) {
|
||||
return path
|
||||
}
|
||||
}
|
||||
|
||||
// Default
|
||||
return join(process.cwd(), 'models')
|
||||
}
|
||||
|
||||
/**
|
||||
* Get memory usage in MB
|
||||
*/
|
||||
private getMemoryUsage(): number | null {
|
||||
if (typeof process !== 'undefined' && process.memoryUsage) {
|
||||
const usage = process.memoryUsage()
|
||||
return Math.round(usage.heapUsed / 1024 / 1024)
|
||||
}
|
||||
return null
|
||||
}
|
||||
|
||||
/**
|
||||
* Get current statistics
|
||||
*/
|
||||
getStats(): EmbeddingStats {
|
||||
return {
|
||||
initialized: this.initialized,
|
||||
precision: this.precision,
|
||||
modelName: this.modelName,
|
||||
embedCount: this.embedCount,
|
||||
initTime: this.initTime,
|
||||
memoryMB: this.getMemoryUsage()
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if initialized
|
||||
*/
|
||||
isInitialized(): boolean {
|
||||
return this.initialized
|
||||
}
|
||||
|
||||
/**
|
||||
* Get current precision
|
||||
*/
|
||||
getPrecision(): ModelPrecision {
|
||||
return this.precision
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate precision matches expected
|
||||
*/
|
||||
validatePrecision(expected: ModelPrecision): void {
|
||||
if (this.locked && expected !== this.precision) {
|
||||
throw new Error(
|
||||
`Precision mismatch! System using ${this.precision.toUpperCase()} ` +
|
||||
`but ${expected.toUpperCase()} was requested. Cannot mix precisions.`
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Export singleton instance and convenience functions
|
||||
export const embeddingManager = EmbeddingManager.getInstance()
|
||||
|
||||
/**
|
||||
* Direct embed function
|
||||
*/
|
||||
export async function embed(text: string | string[]): Promise<Vector> {
|
||||
return await embeddingManager.embed(text)
|
||||
}
|
||||
|
||||
/**
|
||||
* Get embedding function for compatibility
|
||||
*/
|
||||
export function getEmbeddingFunction(): EmbeddingFunction {
|
||||
return embeddingManager.getEmbeddingFunction()
|
||||
}
|
||||
|
||||
/**
|
||||
* Get statistics
|
||||
*/
|
||||
export function getEmbeddingStats(): EmbeddingStats {
|
||||
return embeddingManager.getStats()
|
||||
}
|
||||
28
src/embeddings/index.ts
Normal file
28
src/embeddings/index.ts
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
/**
|
||||
* Embeddings Module - Clean, Unified Architecture
|
||||
*
|
||||
* This module provides all embedding functionality for Brainy.
|
||||
*
|
||||
* Main Components:
|
||||
* - EmbeddingManager: Core embedding generation with Q8/FP32 support
|
||||
* - CachedEmbeddings: Performance optimization layer with pre-computed embeddings
|
||||
*/
|
||||
|
||||
// Core embedding functionality
|
||||
export {
|
||||
EmbeddingManager,
|
||||
embeddingManager,
|
||||
embed,
|
||||
getEmbeddingFunction,
|
||||
getEmbeddingStats,
|
||||
type ModelPrecision
|
||||
} from './EmbeddingManager.js'
|
||||
|
||||
// Cached embeddings for performance
|
||||
export {
|
||||
CachedEmbeddings,
|
||||
cachedEmbeddings
|
||||
} from './CachedEmbeddings.js'
|
||||
|
||||
// Default export is the singleton manager
|
||||
export { embeddingManager as default } from './EmbeddingManager.js'
|
||||
|
|
@ -1,290 +0,0 @@
|
|||
/**
|
||||
* Model Manager - Ensures transformer models are available at runtime
|
||||
*
|
||||
* Strategy (in order):
|
||||
* 1. Check local cache first (instant)
|
||||
* 2. Try Soulcraft CDN (fastest when available)
|
||||
* 3. Try GitHub release tar.gz with extraction (reliable backup)
|
||||
* 4. Fall back to Hugging Face (always works)
|
||||
*
|
||||
* NO USER CONFIGURATION REQUIRED - Everything is automatic!
|
||||
*/
|
||||
|
||||
import { existsSync } from 'fs'
|
||||
import { mkdir, writeFile } from 'fs/promises'
|
||||
import { join, dirname } from 'path'
|
||||
import { env } from '@huggingface/transformers'
|
||||
|
||||
// Model sources in order of preference
|
||||
const MODEL_SOURCES = {
|
||||
// CDN - Fastest when available (currently active)
|
||||
cdn: {
|
||||
host: 'https://models.soulcraft.com/models',
|
||||
pathTemplate: '{model}/', // e.g., Xenova/all-MiniLM-L6-v2/
|
||||
testFile: 'config.json' // File to test availability
|
||||
},
|
||||
|
||||
// GitHub Release - tar.gz fallback (already exists and works)
|
||||
githubRelease: {
|
||||
tarUrl: 'https://github.com/soulcraftlabs/brainy/releases/download/models-v1/all-MiniLM-L6-v2.tar.gz'
|
||||
},
|
||||
|
||||
// Original Hugging Face - final fallback (always works)
|
||||
huggingface: {
|
||||
host: 'https://huggingface.co',
|
||||
pathTemplate: '{model}/resolve/{revision}/' // Default transformers.js pattern
|
||||
}
|
||||
}
|
||||
|
||||
// Model verification files - BOTH fp32 and q8 variants
|
||||
const REQUIRED_FILES = [
|
||||
'config.json',
|
||||
'tokenizer.json',
|
||||
'tokenizer_config.json'
|
||||
]
|
||||
|
||||
const MODEL_VARIANTS = {
|
||||
fp32: 'onnx/model.onnx',
|
||||
q8: 'onnx/model_quantized.onnx'
|
||||
}
|
||||
|
||||
export class ModelManager {
|
||||
private static instance: ModelManager
|
||||
private modelsPath: string
|
||||
private isInitialized = false
|
||||
|
||||
private constructor() {
|
||||
// Determine models path
|
||||
this.modelsPath = this.getModelsPath()
|
||||
}
|
||||
|
||||
static getInstance(): ModelManager {
|
||||
if (!ModelManager.instance) {
|
||||
ModelManager.instance = new ModelManager()
|
||||
}
|
||||
return ModelManager.instance
|
||||
}
|
||||
|
||||
private getModelsPath(): string {
|
||||
// Check various possible locations
|
||||
const paths = [
|
||||
process.env.BRAINY_MODELS_PATH,
|
||||
'./models',
|
||||
join(process.cwd(), 'models'),
|
||||
join(process.env.HOME || '', '.brainy', 'models'),
|
||||
env.cacheDir
|
||||
]
|
||||
|
||||
// Find first existing path or use default
|
||||
for (const path of paths) {
|
||||
if (path && existsSync(path)) {
|
||||
return path
|
||||
}
|
||||
}
|
||||
|
||||
// Default to local models directory
|
||||
return join(process.cwd(), 'models')
|
||||
}
|
||||
|
||||
async ensureModels(modelName = 'Xenova/all-MiniLM-L6-v2'): Promise<boolean> {
|
||||
if (this.isInitialized) {
|
||||
return true
|
||||
}
|
||||
|
||||
// Configure transformers.js environment
|
||||
env.cacheDir = this.modelsPath
|
||||
env.allowLocalModels = true
|
||||
env.useFSCache = true
|
||||
|
||||
// Check if model already exists locally
|
||||
const modelPath = join(this.modelsPath, ...modelName.split('/'))
|
||||
if (await this.verifyModelFiles(modelPath)) {
|
||||
console.log('✅ Models found in cache:', modelPath)
|
||||
env.allowRemoteModels = false // Use local only
|
||||
this.isInitialized = true
|
||||
return true
|
||||
}
|
||||
|
||||
// Try to download from our sources
|
||||
console.log('📥 Downloading transformer models...')
|
||||
|
||||
// Try CDN first (fastest when available)
|
||||
if (await this.tryModelSource('Soulcraft CDN', MODEL_SOURCES.cdn, modelName)) {
|
||||
this.isInitialized = true
|
||||
return true
|
||||
}
|
||||
|
||||
// Try GitHub release with tar.gz extraction (reliable backup)
|
||||
if (await this.downloadAndExtractFromGitHub(modelName)) {
|
||||
this.isInitialized = true
|
||||
return true
|
||||
}
|
||||
|
||||
// Fall back to Hugging Face (always works)
|
||||
console.log('⚠️ Using Hugging Face fallback for models')
|
||||
env.remoteHost = MODEL_SOURCES.huggingface.host
|
||||
env.remotePathTemplate = MODEL_SOURCES.huggingface.pathTemplate
|
||||
env.allowRemoteModels = true
|
||||
this.isInitialized = true
|
||||
return true
|
||||
}
|
||||
|
||||
private async verifyModelFiles(modelPath: string): Promise<boolean> {
|
||||
// Check if essential files exist
|
||||
for (const file of REQUIRED_FILES) {
|
||||
const fullPath = join(modelPath, file)
|
||||
if (!existsSync(fullPath)) {
|
||||
return false
|
||||
}
|
||||
}
|
||||
// At least one model variant must exist (fp32 or q8)
|
||||
const fp32Exists = existsSync(join(modelPath, MODEL_VARIANTS.fp32))
|
||||
const q8Exists = existsSync(join(modelPath, MODEL_VARIANTS.q8))
|
||||
return fp32Exists || q8Exists
|
||||
}
|
||||
|
||||
/**
|
||||
* Check which model variants are available locally
|
||||
*/
|
||||
public getAvailableModels(modelName: string = 'Xenova/all-MiniLM-L6-v2'): { fp32: boolean, q8: boolean } {
|
||||
const modelPath = join(this.modelsPath, modelName)
|
||||
return {
|
||||
fp32: existsSync(join(modelPath, MODEL_VARIANTS.fp32)),
|
||||
q8: existsSync(join(modelPath, MODEL_VARIANTS.q8))
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the best available model variant based on preference and availability
|
||||
*/
|
||||
public getBestAvailableModel(preferredType: 'fp32' | 'q8' = 'fp32', modelName: string = 'Xenova/all-MiniLM-L6-v2'): 'fp32' | 'q8' | null {
|
||||
const available = this.getAvailableModels(modelName)
|
||||
|
||||
// If preferred type is available, use it
|
||||
if (available[preferredType]) {
|
||||
return preferredType
|
||||
}
|
||||
|
||||
// Otherwise fall back to what's available
|
||||
if (preferredType === 'q8' && available.fp32) {
|
||||
console.warn('⚠️ Q8 model requested but not available, falling back to FP32')
|
||||
return 'fp32'
|
||||
}
|
||||
|
||||
if (preferredType === 'fp32' && available.q8) {
|
||||
console.warn('⚠️ FP32 model requested but not available, falling back to Q8')
|
||||
return 'q8'
|
||||
}
|
||||
|
||||
return null
|
||||
}
|
||||
|
||||
private async tryModelSource(name: string, source: { host: string, pathTemplate: string, testFile?: string }, modelName: string): Promise<boolean> {
|
||||
try {
|
||||
console.log(`📥 Trying ${name}...`)
|
||||
|
||||
// Test if the source is accessible by trying to fetch a test file
|
||||
const testFile = source.testFile || 'config.json'
|
||||
const modelPath = source.pathTemplate.replace('{model}', modelName).replace('{revision}', 'main')
|
||||
const testUrl = `${source.host}/${modelPath}${testFile}`
|
||||
|
||||
const response = await fetch(testUrl).catch(() => null)
|
||||
|
||||
if (response && response.ok) {
|
||||
console.log(`✅ ${name} is available`)
|
||||
|
||||
// Configure transformers.js to use this source
|
||||
env.remoteHost = source.host
|
||||
env.remotePathTemplate = source.pathTemplate
|
||||
env.allowRemoteModels = true
|
||||
|
||||
// The model will be downloaded automatically by transformers.js when needed
|
||||
return true
|
||||
} else {
|
||||
console.log(`⚠️ ${name} not available (${response?.status || 'unreachable'})`)
|
||||
return false
|
||||
}
|
||||
} catch (error) {
|
||||
console.log(`⚠️ ${name} check failed:`, (error as Error).message)
|
||||
return false
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
private async downloadAndExtractFromGitHub(modelName: string): Promise<boolean> {
|
||||
try {
|
||||
console.log('📥 Trying GitHub Release (tar.gz)...')
|
||||
|
||||
// Download tar.gz file
|
||||
const response = await fetch(MODEL_SOURCES.githubRelease.tarUrl)
|
||||
if (!response.ok) {
|
||||
console.log(`⚠️ GitHub Release not available (${response.status})`)
|
||||
return false
|
||||
}
|
||||
|
||||
// Since we can't use tar-stream, we'll use Node's built-in child_process
|
||||
// to extract using system tar command (available on all Unix systems)
|
||||
const buffer = await response.arrayBuffer()
|
||||
const modelPath = join(this.modelsPath, ...modelName.split('/'))
|
||||
|
||||
// Create model directory
|
||||
await mkdir(modelPath, { recursive: true })
|
||||
|
||||
// Write tar.gz to temp file and extract
|
||||
const tempFile = join(this.modelsPath, 'temp-model.tar.gz')
|
||||
await writeFile(tempFile, Buffer.from(buffer))
|
||||
|
||||
// Extract using system tar command
|
||||
const { exec } = await import('child_process')
|
||||
const { promisify } = await import('util')
|
||||
const execAsync = promisify(exec)
|
||||
|
||||
try {
|
||||
// Extract and strip the first directory component
|
||||
await execAsync(`tar -xzf ${tempFile} -C ${modelPath} --strip-components=1`, {
|
||||
cwd: this.modelsPath
|
||||
})
|
||||
|
||||
// Clean up temp file
|
||||
const { unlink } = await import('fs/promises')
|
||||
await unlink(tempFile)
|
||||
|
||||
console.log('✅ GitHub Release models extracted and cached locally')
|
||||
|
||||
// Configure to use local models now
|
||||
env.allowRemoteModels = false
|
||||
return true
|
||||
} catch (extractError) {
|
||||
console.log('⚠️ Tar extraction failed, trying alternative method')
|
||||
return false
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
console.log('⚠️ GitHub Release download failed:', (error as Error).message)
|
||||
return false
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Pre-download models for deployment
|
||||
* This is what npm run download-models calls
|
||||
*/
|
||||
static async predownload(): Promise<void> {
|
||||
const manager = ModelManager.getInstance()
|
||||
const success = await manager.ensureModels()
|
||||
|
||||
if (!success) {
|
||||
throw new Error('Failed to download models')
|
||||
}
|
||||
|
||||
console.log('✅ Models downloaded successfully')
|
||||
}
|
||||
}
|
||||
|
||||
// Auto-initialize on import in production
|
||||
if (process.env.NODE_ENV === 'production' && process.env.SKIP_MODEL_CHECK !== 'true') {
|
||||
ModelManager.getInstance().ensureModels().catch(error => {
|
||||
console.error('⚠️ Model initialization failed:', error)
|
||||
// Don't throw - allow app to start and try downloading on first use
|
||||
})
|
||||
}
|
||||
|
|
@ -1,247 +0,0 @@
|
|||
/**
|
||||
* Universal Memory Manager for Embeddings
|
||||
*
|
||||
* Works in ALL environments: Node.js, browsers, serverless, workers
|
||||
* Solves transformers.js memory leak with environment-specific strategies
|
||||
*/
|
||||
|
||||
import { Vector, EmbeddingFunction } from '../coreTypes.js'
|
||||
|
||||
// Environment detection
|
||||
const isNode = typeof process !== 'undefined' && process.versions?.node
|
||||
const isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined'
|
||||
const isServerless = typeof process !== 'undefined' && (
|
||||
process.env.VERCEL ||
|
||||
process.env.NETLIFY ||
|
||||
process.env.AWS_LAMBDA_FUNCTION_NAME ||
|
||||
process.env.FUNCTIONS_WORKER_RUNTIME
|
||||
)
|
||||
|
||||
interface MemoryStats {
|
||||
embeddings: number
|
||||
memoryUsage: string
|
||||
restarts: number
|
||||
strategy: string
|
||||
}
|
||||
|
||||
export class UniversalMemoryManager {
|
||||
private embeddingFunction: any = null
|
||||
private embedCount = 0
|
||||
private restartCount = 0
|
||||
private lastRestart = 0
|
||||
private strategy: string
|
||||
private maxEmbeddings: number
|
||||
|
||||
constructor() {
|
||||
// CRITICAL FIX: Never use worker threads with ONNX Runtime
|
||||
// Worker threads cause HandleScope V8 API errors due to isolate issues
|
||||
// Always use direct embedding on main thread for ONNX compatibility
|
||||
|
||||
if (isServerless) {
|
||||
this.strategy = 'serverless-restart'
|
||||
this.maxEmbeddings = 50 // Restart frequently in serverless
|
||||
} else if (isNode && !isBrowser) {
|
||||
// CHANGED: Use direct strategy instead of node-worker to avoid V8 isolate issues
|
||||
this.strategy = 'node-direct'
|
||||
this.maxEmbeddings = 200 // Main thread can handle more with single model instance
|
||||
} else if (isBrowser) {
|
||||
this.strategy = 'browser-dispose'
|
||||
this.maxEmbeddings = 25 // Browser memory is limited
|
||||
} else {
|
||||
this.strategy = 'fallback-dispose'
|
||||
this.maxEmbeddings = 75
|
||||
}
|
||||
|
||||
console.log(`🧠 Universal Memory Manager: Using ${this.strategy} strategy`)
|
||||
console.log('✅ UNIVERSAL: Memory-safe embedding system initialized')
|
||||
}
|
||||
|
||||
async getEmbeddingFunction(): Promise<EmbeddingFunction> {
|
||||
return async (data: string | string[]): Promise<Vector> => {
|
||||
return this.embed(data)
|
||||
}
|
||||
}
|
||||
|
||||
async embed(data: string | string[]): Promise<Vector> {
|
||||
// Check if we need to restart/cleanup
|
||||
await this.checkMemoryLimits()
|
||||
|
||||
// Ensure embedding function is available
|
||||
await this.ensureEmbeddingFunction()
|
||||
|
||||
// Perform embedding
|
||||
const result = await this.embeddingFunction.embed(data)
|
||||
this.embedCount++
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
private async checkMemoryLimits(): Promise<void> {
|
||||
if (this.embedCount >= this.maxEmbeddings) {
|
||||
console.log(`🔄 Memory cleanup: ${this.embedCount} embeddings processed`)
|
||||
await this.cleanup()
|
||||
}
|
||||
}
|
||||
|
||||
private async ensureEmbeddingFunction(): Promise<void> {
|
||||
if (this.embeddingFunction) {
|
||||
return
|
||||
}
|
||||
|
||||
switch (this.strategy) {
|
||||
case 'node-direct':
|
||||
await this.initNodeDirect()
|
||||
break
|
||||
|
||||
case 'serverless-restart':
|
||||
await this.initServerless()
|
||||
break
|
||||
|
||||
case 'browser-dispose':
|
||||
await this.initBrowser()
|
||||
break
|
||||
|
||||
default:
|
||||
await this.initFallback()
|
||||
}
|
||||
}
|
||||
|
||||
private async initNodeDirect(): Promise<void> {
|
||||
if (isNode) {
|
||||
// CRITICAL: Use direct embedding to avoid worker thread V8 isolate issues
|
||||
// This prevents HandleScope errors and ensures single model instance
|
||||
console.log('✅ Using Node.js direct embedding (main thread - ONNX compatible)')
|
||||
await this.initDirect()
|
||||
}
|
||||
}
|
||||
|
||||
private async initServerless(): Promise<void> {
|
||||
// In serverless, use direct embedding but restart more aggressively
|
||||
await this.initDirect()
|
||||
console.log('✅ Using serverless strategy with aggressive cleanup')
|
||||
}
|
||||
|
||||
private async initBrowser(): Promise<void> {
|
||||
// In browser, use direct embedding with disposal
|
||||
await this.initDirect()
|
||||
console.log('✅ Using browser strategy with disposal')
|
||||
}
|
||||
|
||||
private async initFallback(): Promise<void> {
|
||||
await this.initDirect()
|
||||
console.log('✅ Using fallback direct embedding strategy')
|
||||
}
|
||||
|
||||
private async initDirect(): Promise<void> {
|
||||
try {
|
||||
// Dynamic import to handle different environments
|
||||
const { TransformerEmbedding } = await import('../utils/embedding.js')
|
||||
|
||||
this.embeddingFunction = new TransformerEmbedding({
|
||||
verbose: false,
|
||||
precision: 'fp32',
|
||||
localFilesOnly: process.env.BRAINY_ALLOW_REMOTE_MODELS !== 'true'
|
||||
})
|
||||
|
||||
await this.embeddingFunction.init()
|
||||
console.log('✅ Direct embedding function initialized')
|
||||
} catch (error) {
|
||||
throw new Error(`Failed to initialize embedding function: ${error instanceof Error ? error.message : String(error)}`)
|
||||
}
|
||||
}
|
||||
|
||||
private async cleanup(): Promise<void> {
|
||||
const startTime = Date.now()
|
||||
|
||||
try {
|
||||
// Strategy-specific cleanup
|
||||
switch (this.strategy) {
|
||||
case 'node-worker':
|
||||
if (this.embeddingFunction?.forceRestart) {
|
||||
await this.embeddingFunction.forceRestart()
|
||||
}
|
||||
break
|
||||
|
||||
case 'serverless-restart':
|
||||
// In serverless, create new instance
|
||||
if (this.embeddingFunction?.dispose) {
|
||||
this.embeddingFunction.dispose()
|
||||
}
|
||||
this.embeddingFunction = null
|
||||
break
|
||||
|
||||
case 'browser-dispose':
|
||||
// In browser, try disposal
|
||||
if (this.embeddingFunction?.dispose) {
|
||||
this.embeddingFunction.dispose()
|
||||
}
|
||||
// Force garbage collection if available
|
||||
if (typeof window !== 'undefined' && (window as any).gc) {
|
||||
(window as any).gc()
|
||||
}
|
||||
break
|
||||
|
||||
default:
|
||||
// Fallback: dispose and recreate
|
||||
if (this.embeddingFunction?.dispose) {
|
||||
this.embeddingFunction.dispose()
|
||||
}
|
||||
this.embeddingFunction = null
|
||||
}
|
||||
|
||||
this.embedCount = 0
|
||||
this.restartCount++
|
||||
this.lastRestart = Date.now()
|
||||
|
||||
const cleanupTime = Date.now() - startTime
|
||||
console.log(`🧹 Memory cleanup completed in ${cleanupTime}ms (strategy: ${this.strategy})`)
|
||||
|
||||
} catch (error) {
|
||||
console.warn('⚠️ Cleanup failed:', error instanceof Error ? error.message : String(error))
|
||||
// Force null assignment as last resort
|
||||
this.embeddingFunction = null
|
||||
}
|
||||
}
|
||||
|
||||
getMemoryStats(): MemoryStats {
|
||||
let memoryUsage = 'unknown'
|
||||
|
||||
// Get memory stats based on environment
|
||||
if (isNode && typeof process !== 'undefined') {
|
||||
const mem = process.memoryUsage()
|
||||
memoryUsage = `${(mem.heapUsed / 1024 / 1024).toFixed(2)} MB`
|
||||
} else if (isBrowser && (performance as any).memory) {
|
||||
const mem = (performance as any).memory
|
||||
memoryUsage = `${(mem.usedJSHeapSize / 1024 / 1024).toFixed(2)} MB`
|
||||
}
|
||||
|
||||
return {
|
||||
embeddings: this.embedCount,
|
||||
memoryUsage,
|
||||
restarts: this.restartCount,
|
||||
strategy: this.strategy
|
||||
}
|
||||
}
|
||||
|
||||
async dispose(): Promise<void> {
|
||||
if (this.embeddingFunction) {
|
||||
if (this.embeddingFunction.dispose) {
|
||||
await this.embeddingFunction.dispose()
|
||||
}
|
||||
this.embeddingFunction = null
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Export singleton instance
|
||||
export const universalMemoryManager = new UniversalMemoryManager()
|
||||
|
||||
// Export convenience function
|
||||
export async function getUniversalEmbeddingFunction(): Promise<EmbeddingFunction> {
|
||||
return universalMemoryManager.getEmbeddingFunction()
|
||||
}
|
||||
|
||||
// Export memory stats function
|
||||
export function getEmbeddingMemoryStats(): MemoryStats {
|
||||
return universalMemoryManager.getMemoryStats()
|
||||
}
|
||||
|
|
@ -1,85 +0,0 @@
|
|||
/**
|
||||
* Worker process for embeddings - Workaround for transformers.js memory leak
|
||||
*
|
||||
* This worker can be killed and restarted to release memory completely.
|
||||
* Based on 2024 research: dispose() doesn't fully free memory in transformers.js
|
||||
*/
|
||||
|
||||
import { TransformerEmbedding } from '../utils/embedding.js'
|
||||
import { parentPort } from 'worker_threads'
|
||||
|
||||
let model: TransformerEmbedding | null = null
|
||||
let requestCount = 0
|
||||
const MAX_REQUESTS = 100 // Restart worker after 100 requests to prevent memory leak
|
||||
|
||||
async function initModel(): Promise<void> {
|
||||
if (!model) {
|
||||
model = new TransformerEmbedding({
|
||||
verbose: false,
|
||||
precision: 'fp32',
|
||||
localFilesOnly: process.env.BRAINY_ALLOW_REMOTE_MODELS !== 'true'
|
||||
})
|
||||
await model.init()
|
||||
console.log('🔧 Worker: Model initialized')
|
||||
}
|
||||
}
|
||||
|
||||
if (parentPort) {
|
||||
parentPort.on('message', async (message) => {
|
||||
try {
|
||||
const { id, type, data } = message
|
||||
|
||||
switch (type) {
|
||||
case 'embed':
|
||||
await initModel()
|
||||
const embeddings = await model!.embed(data)
|
||||
parentPort!.postMessage({ id, success: true, result: embeddings })
|
||||
|
||||
requestCount++
|
||||
|
||||
// Proactively restart worker to prevent memory leak
|
||||
if (requestCount >= MAX_REQUESTS) {
|
||||
console.log(`🔄 Worker: Restarting after ${requestCount} requests (memory leak prevention)`)
|
||||
process.exit(0) // Parent will restart us
|
||||
}
|
||||
break
|
||||
|
||||
case 'dispose':
|
||||
if (model) {
|
||||
// This doesn't fully free memory (known issue), but try anyway
|
||||
if ('dispose' in model && typeof model.dispose === 'function') {
|
||||
model.dispose()
|
||||
}
|
||||
model = null
|
||||
}
|
||||
parentPort!.postMessage({ id, success: true })
|
||||
break
|
||||
|
||||
case 'restart':
|
||||
// Force restart to clear memory
|
||||
console.log('🔄 Worker: Force restart requested')
|
||||
process.exit(0)
|
||||
break
|
||||
|
||||
default:
|
||||
parentPort!.postMessage({
|
||||
id,
|
||||
success: false,
|
||||
error: `Unknown message type: ${type}`
|
||||
})
|
||||
}
|
||||
} catch (error) {
|
||||
parentPort!.postMessage({
|
||||
id: message.id,
|
||||
success: false,
|
||||
error: error instanceof Error ? error.message : String(error)
|
||||
})
|
||||
}
|
||||
})
|
||||
|
||||
console.log('🚀 Embedding worker started')
|
||||
parentPort.postMessage({ type: 'ready' })
|
||||
} else {
|
||||
console.error('❌ Worker: parentPort is null, cannot communicate with main thread')
|
||||
process.exit(1)
|
||||
}
|
||||
|
|
@ -1,193 +0,0 @@
|
|||
/**
|
||||
* Worker Manager for Memory-Safe Embeddings
|
||||
*
|
||||
* Manages worker lifecycle to prevent transformers.js memory leaks
|
||||
* Workers are automatically restarted when memory usage grows too high
|
||||
*/
|
||||
|
||||
import { Worker } from 'worker_threads'
|
||||
import { join, dirname } from 'path'
|
||||
import { fileURLToPath } from 'url'
|
||||
import { Vector, EmbeddingFunction } from '../coreTypes.js'
|
||||
|
||||
// Get current directory for worker path
|
||||
const __filename = fileURLToPath(import.meta.url)
|
||||
const __dirname = dirname(__filename)
|
||||
|
||||
interface PendingRequest {
|
||||
resolve: (result: any) => void
|
||||
reject: (error: Error) => void
|
||||
timeout?: NodeJS.Timeout
|
||||
}
|
||||
|
||||
export class WorkerEmbeddingManager {
|
||||
private worker: Worker | null = null
|
||||
private requestId = 0
|
||||
private pendingRequests = new Map<number, PendingRequest>()
|
||||
private isRestarting = false
|
||||
private totalRequests = 0
|
||||
|
||||
async getEmbeddingFunction(): Promise<EmbeddingFunction> {
|
||||
return async (data: string | string[]): Promise<Vector> => {
|
||||
return this.embed(data)
|
||||
}
|
||||
}
|
||||
|
||||
async embed(data: string | string[]): Promise<Vector> {
|
||||
await this.ensureWorker()
|
||||
|
||||
const id = ++this.requestId
|
||||
this.totalRequests++
|
||||
|
||||
return new Promise((resolve, reject) => {
|
||||
const timeout = setTimeout(() => {
|
||||
this.pendingRequests.delete(id)
|
||||
reject(new Error('Embedding request timed out (120s)'))
|
||||
}, 120000)
|
||||
|
||||
this.pendingRequests.set(id, { resolve, reject, timeout })
|
||||
|
||||
this.worker!.postMessage({
|
||||
id,
|
||||
type: 'embed',
|
||||
data
|
||||
})
|
||||
})
|
||||
}
|
||||
|
||||
private async ensureWorker(): Promise<void> {
|
||||
if (this.worker && !this.isRestarting) {
|
||||
return
|
||||
}
|
||||
|
||||
if (this.isRestarting) {
|
||||
// Wait for restart to complete
|
||||
return new Promise((resolve) => {
|
||||
const checkRestart = () => {
|
||||
if (!this.isRestarting) {
|
||||
resolve()
|
||||
} else {
|
||||
setTimeout(checkRestart, 100)
|
||||
}
|
||||
}
|
||||
checkRestart()
|
||||
})
|
||||
}
|
||||
|
||||
await this.createWorker()
|
||||
}
|
||||
|
||||
private async createWorker(): Promise<void> {
|
||||
this.isRestarting = true
|
||||
|
||||
// Kill existing worker if any
|
||||
if (this.worker) {
|
||||
this.worker.terminate()
|
||||
this.worker = null
|
||||
}
|
||||
|
||||
// Clear pending requests
|
||||
for (const [id, request] of this.pendingRequests) {
|
||||
if (request.timeout) {
|
||||
clearTimeout(request.timeout)
|
||||
}
|
||||
request.reject(new Error('Worker restarted'))
|
||||
}
|
||||
this.pendingRequests.clear()
|
||||
|
||||
console.log('🔄 Starting embedding worker...')
|
||||
|
||||
// Create new worker
|
||||
const workerPath = join(__dirname, 'worker-embedding.js')
|
||||
this.worker = new Worker(workerPath)
|
||||
|
||||
// Handle worker messages
|
||||
this.worker.on('message', (message) => {
|
||||
if (message.type === 'ready') {
|
||||
console.log('✅ Embedding worker ready')
|
||||
this.isRestarting = false
|
||||
return
|
||||
}
|
||||
|
||||
const { id, success, result, error } = message
|
||||
const request = this.pendingRequests.get(id)
|
||||
|
||||
if (request) {
|
||||
if (request.timeout) {
|
||||
clearTimeout(request.timeout)
|
||||
}
|
||||
this.pendingRequests.delete(id)
|
||||
|
||||
if (success) {
|
||||
request.resolve(result)
|
||||
} else {
|
||||
request.reject(new Error(error))
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
// Handle worker exit
|
||||
this.worker.on('exit', (code) => {
|
||||
console.log(`🔄 Embedding worker exited with code ${code}`)
|
||||
if (code !== 0 && !this.isRestarting) {
|
||||
console.log('🔄 Worker crashed, will restart on next request')
|
||||
}
|
||||
this.worker = null
|
||||
})
|
||||
|
||||
// Wait for worker to be ready
|
||||
return new Promise((resolve, reject) => {
|
||||
const timeout = setTimeout(() => {
|
||||
reject(new Error('Worker startup timeout'))
|
||||
}, 30000)
|
||||
|
||||
const checkReady = () => {
|
||||
if (!this.isRestarting) {
|
||||
clearTimeout(timeout)
|
||||
resolve()
|
||||
} else {
|
||||
setTimeout(checkReady, 100)
|
||||
}
|
||||
}
|
||||
checkReady()
|
||||
})
|
||||
}
|
||||
|
||||
async dispose(): Promise<void> {
|
||||
if (this.worker) {
|
||||
this.worker.terminate()
|
||||
this.worker = null
|
||||
}
|
||||
|
||||
// Clear pending requests
|
||||
for (const [id, request] of this.pendingRequests) {
|
||||
if (request.timeout) {
|
||||
clearTimeout(request.timeout)
|
||||
}
|
||||
request.reject(new Error('Manager disposed'))
|
||||
}
|
||||
this.pendingRequests.clear()
|
||||
}
|
||||
|
||||
async forceRestart(): Promise<void> {
|
||||
console.log('🔄 Force restarting embedding worker (memory cleanup)')
|
||||
await this.createWorker()
|
||||
}
|
||||
|
||||
getStats() {
|
||||
return {
|
||||
totalRequests: this.totalRequests,
|
||||
pendingRequests: this.pendingRequests.size,
|
||||
workerActive: this.worker !== null,
|
||||
isRestarting: this.isRestarting
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Export singleton instance
|
||||
export const workerEmbeddingManager = new WorkerEmbeddingManager()
|
||||
|
||||
// Export convenience function
|
||||
export async function getWorkerEmbeddingFunction(): Promise<EmbeddingFunction> {
|
||||
return workerEmbeddingManager.getEmbeddingFunction()
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue