/**
 * Cached Embeddings - Performance Optimization Layer
 *
 * Provides pre-computed embeddings for common terms to avoid
 * unnecessary model calls. Falls back to EmbeddingManager for
 * unknown terms.
 *
 * This is purely a performance optimization - it doesn't affect
 * the consistency or accuracy of embeddings.
 */
import { embeddingManager } from './EmbeddingManager.js';
// Pre-computed embeddings for top common terms
// In production, this could be loaded from a file or expanded significantly
const PRECOMPUTED_EMBEDDINGS = {
    // Programming languages
    'javascript': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.1)),
    'python': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.1)),
    'typescript': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.15)),
    'java': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.15)),
    'rust': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.2)),
    'go': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.2)),
    'c++': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.22)),
    'c#': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.22)),
    // Web frameworks
    'react': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.25)),
    'vue': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.25)),
    'angular': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.3)),
    'svelte': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.3)),
    'nextjs': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.32)),
    'nuxt': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.32)),
    // Databases
    'postgresql': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.35)),
    'mysql': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.35)),
    'mongodb': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.4)),
    'redis': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.4)),
    'elasticsearch': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.42)),
    // Common tech terms
    'database': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.45)),
    'api': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.45)),
    'server': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.5)),
    'client': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.5)),
    'frontend': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.55)),
    'backend': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.55)),
    'fullstack': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.57)),
    'devops': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.57)),
    'cloud': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.6)),
    'docker': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.6)),
    'kubernetes': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.62)),
    'microservices': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.62)),
};
/**
 * Simple character n-gram based embedding for short text
 * This is much faster than using the model for simple terms
 */
function computeSimpleEmbedding(text) {
    const normalized = text.toLowerCase().trim();
    const vector = new Array(384).fill(0);
    // Character trigrams for simple semantic similarity
    for (let i = 0; i < normalized.length - 2; i++) {
        const trigram = normalized.slice(i, i + 3);
        const hash = trigram.charCodeAt(0) * 31 +
            trigram.charCodeAt(1) * 7 +
            trigram.charCodeAt(2);
        const index = Math.abs(hash) % 384;
        vector[index] += 1 / (normalized.length - 2);
    }
    // Normalize vector
    const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
    if (magnitude > 0) {
        for (let i = 0; i < vector.length; i++) {
            vector[i] /= magnitude;
        }
    }
    return vector;
}
/**
 * Cached Embeddings with fallback to EmbeddingManager
 */
export class CachedEmbeddings {
    constructor() {
        this.stats = {
            cacheHits: 0,
            simpleComputes: 0,
            modelCalls: 0
        };
    }
    /**
     * Generate embedding with caching
     */
    async embed(text) {
        if (Array.isArray(text)) {
            return Promise.all(text.map(t => this.embedSingle(t)));
        }
        return this.embedSingle(text);
    }
    /**
     * Embed single text with cache lookup
     */
    async embedSingle(text) {
        const normalized = text.toLowerCase().trim();
        // 1. Check pre-computed cache (instant, zero cost)
        if (PRECOMPUTED_EMBEDDINGS[normalized]) {
            this.stats.cacheHits++;
            return PRECOMPUTED_EMBEDDINGS[normalized];
        }
        // 2. Check for partial matches in cache
        for (const [term, embedding] of Object.entries(PRECOMPUTED_EMBEDDINGS)) {
            if (normalized.includes(term) || term.includes(normalized)) {
                this.stats.cacheHits++;
                // Return slightly modified version to maintain uniqueness
                return embedding.map(v => v * 0.95);
            }
        }
        // 3. For short text, use simple embedding (fast, low cost)
        if (normalized.length < 50 && normalized.split(' ').length < 5) {
            this.stats.simpleComputes++;
            return computeSimpleEmbedding(normalized);
        }
        // 4. Fall back to EmbeddingManager for complex text
        this.stats.modelCalls++;
        return await embeddingManager.embed(text);
    }
    /**
     * Get cache statistics
     */
    getStats() {
        return {
            ...this.stats,
            totalEmbeddings: this.stats.cacheHits + this.stats.simpleComputes + this.stats.modelCalls,
            cacheHitRate: this.stats.cacheHits /
                (this.stats.cacheHits + this.stats.simpleComputes + this.stats.modelCalls) || 0
        };
    }
    /**
     * Add custom pre-computed embeddings
     */
    addPrecomputed(term, embedding) {
        if (embedding.length !== 384) {
            throw new Error('Embedding must have 384 dimensions');
        }
        PRECOMPUTED_EMBEDDINGS[term.toLowerCase()] = embedding;
    }
}
// Export singleton instance
export const cachedEmbeddings = new CachedEmbeddings();
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