56 lines
1.6 KiB
JavaScript
56 lines
1.6 KiB
JavaScript
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const logger = require('../utils/logger');
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let pipeline;
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try {
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// Lazy import to avoid startup cost when unused
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({ pipeline } = require('@xenova/transformers'));
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} catch (e) {
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logger.warn('Embedding pipeline not available. Did you install @xenova/transformers?');
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}
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class EmbeddingService {
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constructor() {
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this.initialized = false;
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this.extractor = null;
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this.modelName = process.env.EMBEDDING_MODEL || 'Xenova/all-MiniLM-L6-v2';
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}
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async initIfNeeded() {
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if (this.initialized) return;
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if (!pipeline) {
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throw new Error('Transformers pipeline not available');
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}
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this.extractor = await pipeline('feature-extraction', this.modelName);
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this.initialized = true;
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logger.info(`Embedding model loaded: ${this.modelName}`);
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}
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async embedText(text) {
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if (!text || !text.trim()) return [];
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await this.initIfNeeded();
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const output = await this.extractor(text, { pooling: 'mean', normalize: true });
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// output is a Tensor; convert to plain JS array
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// Depending on version, .data or .tolist()
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const vector = Array.isArray(output) ? output : (output?.data ? Array.from(output.data) : output.tolist());
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return vector;
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}
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cosineSimilarity(a, b) {
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if (!a || !b || a.length !== b.length || a.length === 0) return 0;
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let dot = 0;
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let normA = 0;
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let normB = 0;
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for (let i = 0; i < a.length; i++) {
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const va = a[i] || 0;
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const vb = b[i] || 0;
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dot += va * vb;
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normA += va * va;
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normB += vb * vb;
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}
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const denom = Math.sqrt(normA) * Math.sqrt(normB);
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return denom ? dot / denom : 0;
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}
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}
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module.exports = new EmbeddingService();
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