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
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src/embeddings/CachedEmbeddings.ts
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src/embeddings/CachedEmbeddings.ts
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/**
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* Cached Embeddings - Performance Optimization Layer
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*
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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 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 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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'python': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.1)),
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'typescript': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.15)),
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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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// 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 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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'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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/**
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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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// Character trigrams for simple semantic similarity
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for (let i = 0; i < normalized.length - 2; i++) {
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const trigram = normalized.slice(i, i + 3)
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const hash = trigram.charCodeAt(0) * 31 +
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trigram.charCodeAt(1) * 7 +
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trigram.charCodeAt(2)
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const index = Math.abs(hash) % 384
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vector[index] += 1 / (normalized.length - 2)
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}
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// Normalize vector
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const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0))
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if (magnitude > 0) {
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for (let i = 0; i < vector.length; i++) {
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vector[i] /= magnitude
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}
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}
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return vector
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}
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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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cacheHits: 0,
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simpleComputes: 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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}
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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 cache (instant, zero cost)
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if (PRECOMPUTED_EMBEDDINGS[normalized]) {
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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 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.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 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. 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.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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/**
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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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// Export singleton instance
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export const cachedEmbeddings = new CachedEmbeddings()
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