Alexander Bocken
7e1181461e
Dual-source nutrition system using BLS (German, primary) and USDA (English, fallback) with ML embedding matching (multilingual-e5-small / all-MiniLM-L6-v2), hybrid substring-first search, and position-aware scoring heuristics. Includes per-recipe and global manual ingredient overwrites, ingredient skip/exclude, referenced recipe nutrition (base refs + anchor tags), section-name dedup, amino acid tracking, and reactive client-side calculator with NutritionSummary component.
62 lines
1.9 KiB
TypeScript
62 lines
1.9 KiB
TypeScript
/**
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* Pre-compute sentence embeddings for BLS German food names.
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* Uses multilingual-e5-small for good German language understanding.
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*
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* Run: pnpm exec vite-node scripts/embed-bls-db.ts
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*/
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import { pipeline } from '@huggingface/transformers';
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import { writeFileSync } from 'fs';
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import { resolve } from 'path';
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// Dynamic import of blsDb (generated file)
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const { BLS_DB } = await import('../src/lib/data/blsDb');
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const MODEL_NAME = 'Xenova/multilingual-e5-small';
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const OUTPUT_FILE = resolve('src/lib/data/blsEmbeddings.json');
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async function main() {
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console.log(`Loading model ${MODEL_NAME}...`);
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const embedder = await pipeline('feature-extraction', MODEL_NAME, {
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dtype: 'q8',
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});
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console.log(`Embedding ${BLS_DB.length} BLS entries...`);
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const entries: { blsCode: string; name: string; vector: number[] }[] = [];
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const batchSize = 32;
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for (let i = 0; i < BLS_DB.length; i += batchSize) {
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const batch = BLS_DB.slice(i, i + batchSize);
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// e5 models require "passage: " prefix for documents
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const texts = batch.map(e => `passage: ${e.nameDe}`);
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for (let j = 0; j < batch.length; j++) {
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const result = await embedder(texts[j], { pooling: 'mean', normalize: true });
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const vector = Array.from(result.data as Float32Array).map(v => Math.round(v * 10000) / 10000);
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entries.push({
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blsCode: batch[j].blsCode,
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name: batch[j].nameDe,
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vector,
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});
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}
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if ((i + batchSize) % 500 < batchSize) {
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console.log(` ${Math.min(i + batchSize, BLS_DB.length)}/${BLS_DB.length}`);
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}
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}
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const output = {
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model: MODEL_NAME,
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dimensions: entries[0]?.vector.length || 384,
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count: entries.length,
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entries,
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};
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const json = JSON.stringify(output);
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writeFileSync(OUTPUT_FILE, json, 'utf-8');
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console.log(`Written ${OUTPUT_FILE} (${(json.length / 1024 / 1024).toFixed(1)}MB, ${entries.length} entries)`);
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}
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main().catch(console.error);
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