recipes: nutrition calculator with BLS/USDA matching, manual overwrites, and skip

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.

scripts/embed-bls-db.ts

@@ -0,0 +1,61 @@
+/**
+ * Pre-compute sentence embeddings for BLS German food names.
+ * Uses multilingual-e5-small for good German language understanding.
+ *
+ * Run: pnpm exec vite-node scripts/embed-bls-db.ts
+ */
+import { pipeline } from '@huggingface/transformers';
+import { writeFileSync } from 'fs';
+import { resolve } from 'path';
+
+// Dynamic import of blsDb (generated file)
+const { BLS_DB } = await import('../src/lib/data/blsDb');
+
+const MODEL_NAME = 'Xenova/multilingual-e5-small';
+const OUTPUT_FILE = resolve('src/lib/data/blsEmbeddings.json');
+
+async function main() {
+  console.log(`Loading model ${MODEL_NAME}...`);
+  const embedder = await pipeline('feature-extraction', MODEL_NAME, {
+    dtype: 'q8',
+  });
+
+  console.log(`Embedding ${BLS_DB.length} BLS entries...`);
+
+  const entries: { blsCode: string; name: string; vector: number[] }[] = [];
+  const batchSize = 32;
+
+  for (let i = 0; i < BLS_DB.length; i += batchSize) {
+    const batch = BLS_DB.slice(i, i + batchSize);
+    // e5 models require "passage: " prefix for documents
+    const texts = batch.map(e => `passage: ${e.nameDe}`);
+
+    for (let j = 0; j < batch.length; j++) {
+      const result = await embedder(texts[j], { pooling: 'mean', normalize: true });
+      const vector = Array.from(result.data as Float32Array).map(v => Math.round(v * 10000) / 10000);
+
+      entries.push({
+        blsCode: batch[j].blsCode,
+        name: batch[j].nameDe,
+        vector,
+      });
+    }
+
+    if ((i + batchSize) % 500 < batchSize) {
+      console.log(`  ${Math.min(i + batchSize, BLS_DB.length)}/${BLS_DB.length}`);
+    }
+  }
+
+  const output = {
+    model: MODEL_NAME,
+    dimensions: entries[0]?.vector.length || 384,
+    count: entries.length,
+    entries,
+  };
+
+  const json = JSON.stringify(output);
+  writeFileSync(OUTPUT_FILE, json, 'utf-8');
+  console.log(`Written ${OUTPUT_FILE} (${(json.length / 1024 / 1024).toFixed(1)}MB, ${entries.length} entries)`);
+}
+
+main().catch(console.error);