feat: replace transformers.js with direct ONNX WASM for Bun compatibility

- Remove @huggingface/transformers dependency (539MB native binaries)
- Add direct ONNX Runtime Web embedding engine
- Bundle all-MiniLM-L6-v2-q8 model (24MB, no runtime downloads)
- Works with Node.js, Bun, and bun build --compile
- Air-gap compatible: fully self-contained, no internet required

New WASM embedding components:
- WASMEmbeddingEngine: Main integration class
- WordPieceTokenizer: Pure TypeScript tokenizer
- EmbeddingPostProcessor: Mean pooling + L2 normalization
- ONNXInferenceEngine: Direct ONNX Runtime Web wrapper
- AssetLoader: Model file loading

Tests added:
- 11 WASM embedding integration tests
- 8 Bun compatibility tests

New npm scripts:
- test:wasm - Run WASM embedding tests
- test:bun - Run tests with Bun
- test:bun:compile - Build and run compiled binary

scripts/download-model.cjs

@@ -0,0 +1,175 @@
+#!/usr/bin/env node
+/**
+ * Download Model Assets
+ *
+ * Downloads the all-MiniLM-L6-v2 Q8 model from Hugging Face.
+ * Run: node scripts/download-model.cjs
+ */
+
+const fs = require('node:fs')
+const path = require('node:path')
+const https = require('node:https')
+
+const MODEL_DIR = path.join(__dirname, '..', 'assets', 'models', 'all-MiniLM-L6-v2-q8')
+const BASE_URL = 'https://huggingface.co/Xenova/all-MiniLM-L6-v2/resolve/main/onnx'
+
+const FILES = [
+  {
+    name: 'model_quantized.onnx',
+    url: `${BASE_URL}/model_quantized.onnx`,
+    dest: 'model.onnx',
+  },
+  {
+    name: 'tokenizer.json',
+    url: 'https://huggingface.co/Xenova/all-MiniLM-L6-v2/resolve/main/tokenizer.json',
+    dest: 'tokenizer.json',
+  },
+  {
+    name: 'config.json',
+    url: 'https://huggingface.co/Xenova/all-MiniLM-L6-v2/resolve/main/config.json',
+    dest: 'config.json',
+  },
+  {
+    name: 'vocab.txt',
+    url: 'https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main/vocab.txt',
+    dest: 'vocab.txt',
+  },
+]
+
+/**
+ * Follow redirects and download file
+ */
+function downloadFile(url, destPath, maxRedirects = 5) {
+  return new Promise((resolve, reject) => {
+    if (maxRedirects === 0) {
+      reject(new Error('Too many redirects'))
+      return
+    }
+
+    const doRequest = (reqUrl) => {
+      const parsedUrl = new URL(reqUrl)
+      const options = {
+        hostname: parsedUrl.hostname,
+        path: parsedUrl.pathname + parsedUrl.search,
+        headers: {
+          'User-Agent': 'Brainy-Model-Downloader/1.0',
+        },
+      }
+
+      https.get(options, (response) => {
+        // Handle redirects
+        if (response.statusCode >= 300 && response.statusCode < 400 && response.headers.location) {
+          response.resume() // Consume response data to free memory
+          const redirectUrl = response.headers.location.startsWith('http')
+            ? response.headers.location
+            : new URL(response.headers.location, reqUrl).toString()
+          console.log(`  ↳ Redirecting to: ${redirectUrl.slice(0, 80)}...`)
+          downloadFile(redirectUrl, destPath, maxRedirects - 1)
+            .then(resolve)
+            .catch(reject)
+          return
+        }
+
+        if (response.statusCode !== 200) {
+          reject(new Error(`HTTP ${response.statusCode}`))
+          return
+        }
+
+        const fileStream = fs.createWriteStream(destPath)
+        let downloadedBytes = 0
+        const totalBytes = parseInt(response.headers['content-length'] || '0', 10)
+
+        response.on('data', (chunk) => {
+          downloadedBytes += chunk.length
+          if (totalBytes > 0) {
+            const percent = Math.round((downloadedBytes / totalBytes) * 100)
+            process.stdout.write(`\r  Progress: ${percent}% (${Math.round(downloadedBytes / 1024 / 1024)}MB)`)
+          }
+        })
+
+        response.pipe(fileStream)
+
+        fileStream.on('finish', () => {
+          fileStream.close()
+          console.log(`\n  ✅ Downloaded: ${path.basename(destPath)} (${Math.round(downloadedBytes / 1024 / 1024)}MB)`)
+          resolve()
+        })
+
+        fileStream.on('error', (err) => {
+          fs.unlink(destPath, () => {}) // Delete partial file
+          reject(err)
+        })
+      }).on('error', reject)
+    }
+
+    doRequest(url)
+  })
+}
+
+/**
+ * Convert vocab.txt to vocab.json
+ */
+function convertVocabToJson(vocabTxtPath, vocabJsonPath) {
+  console.log('📝 Converting vocab.txt to vocab.json...')
+  const content = fs.readFileSync(vocabTxtPath, 'utf-8')
+  const lines = content.split('\n').filter(line => line.trim())
+
+  const vocab = {}
+  for (let i = 0; i < lines.length; i++) {
+    vocab[lines[i]] = i
+  }
+
+  fs.writeFileSync(vocabJsonPath, JSON.stringify(vocab))
+  console.log(`  ✅ Created vocab.json with ${Object.keys(vocab).length} tokens`)
+
+  // Remove vocab.txt since we have vocab.json
+  fs.unlinkSync(vocabTxtPath)
+}
+
+async function main() {
+  console.log('🔽 Downloading all-MiniLM-L6-v2 Q8 model assets...\n')
+
+  // Create model directory
+  fs.mkdirSync(MODEL_DIR, { recursive: true })
+  console.log(`📁 Model directory: ${MODEL_DIR}\n`)
+
+  // Download each file
+  for (const file of FILES) {
+    const destPath = path.join(MODEL_DIR, file.dest)
+
+    // Check if already exists
+    if (fs.existsSync(destPath)) {
+      const stats = fs.statSync(destPath)
+      if (stats.size > 0) {
+        console.log(`⏭️  Skipping ${file.name} (already exists)`)
+        continue
+      }
+    }
+
+    console.log(`📥 Downloading ${file.name}...`)
+    try {
+      await downloadFile(file.url, destPath)
+    } catch (error) {
+      console.error(`  ❌ Failed to download ${file.name}: ${error.message}`)
+      process.exit(1)
+    }
+  }
+
+  // Convert vocab.txt to vocab.json
+  const vocabTxtPath = path.join(MODEL_DIR, 'vocab.txt')
+  const vocabJsonPath = path.join(MODEL_DIR, 'vocab.json')
+  if (fs.existsSync(vocabTxtPath) && !fs.existsSync(vocabJsonPath)) {
+    convertVocabToJson(vocabTxtPath, vocabJsonPath)
+  }
+
+  console.log('\n✅ All model assets downloaded successfully!')
+  console.log('\nModel files:')
+  const files = fs.readdirSync(MODEL_DIR)
+  for (const file of files) {
+    const stats = fs.statSync(path.join(MODEL_DIR, file))
+    const sizeMB = (stats.size / 1024 / 1024).toFixed(2)
+    console.log(`  - ${file}: ${sizeMB}MB`)
+  }
+}
+
+main().catch(console.error)