feat: migrate embeddings to Candle WASM + remove semantic type inference
Major architectural changes: 1. EMBEDDINGS ENGINE (ONNX → Candle WASM): - Replace ONNX Runtime with Rust Candle compiled to WASM - Embedded model in WASM binary (no external downloads) - Quantized Q8 precision with <50MB memory footprint - Zero-download, offline-first operation - Same embedding quality (all-MiniLM-L6-v2) 2. REMOVE SEMANTIC TYPE INFERENCE: - Delete embeddedKeywordEmbeddings.ts (14MB of pre-computed embeddings) - Remove typeAwareQueryPlanner.ts and semanticTypeInference.ts - Remove VerbExactMatchSignal (uses keyword embeddings) - Update SmartRelationshipExtractor to 3 signals (55%/30%/15% weights) API CHANGES (requires v7.0.0): - Removed: inferTypes(), inferNouns(), inferVerbs(), inferIntent() - Removed: getSemanticTypeInference(), SemanticTypeInference class - Removed: TypeInference, SemanticTypeInferenceOptions types Users can still use natural language queries in find() - they just need to specify type explicitly for type-optimized searches. PACKAGE SIZE IMPACT: - Compressed: 90.1 MB → 86.2 MB (-4.3%) - Uncompressed: 114.4 MB → 100.3 MB (-12%) - ~448K lines of code removed 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@ -1,118 +0,0 @@
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# 🤖 Model Loading Quick Reference
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## 🚀 Common Scenarios
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### ✅ Development (Zero Config)
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```typescript
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const brain = new Brainy()
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await brain.init() // Downloads automatically (FP32 default)
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```
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### ⚡ Development (Optimized - v2.8.0+)
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```typescript
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// 75% smaller models, 99% accuracy
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const brain = new Brainy({
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embeddingOptions: { dtype: 'q8' }
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})
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await brain.init()
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```
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### 🐳 Docker Production
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```dockerfile
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# Both models (recommended)
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RUN npm run download-models
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# Or FP32 only (compatibility)
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RUN npm run download-models:fp32
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# Or Q8 only (space-constrained)
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RUN npm run download-models:q8
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ENV BRAINY_ALLOW_REMOTE_MODELS=false
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```
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### ☁️ Serverless/Lambda
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```bash
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# Build step
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npm run download-models
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# Runtime
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export BRAINY_ALLOW_REMOTE_MODELS=false
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```
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### 🔒 Air-Gapped/Offline
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```bash
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# Connected machine
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npm run download-models
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tar -czf brainy-models.tar.gz ./models
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# Offline machine
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tar -xzf brainy-models.tar.gz
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export BRAINY_ALLOW_REMOTE_MODELS=false
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```
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### 🌐 Browser/CDN
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```html
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<!-- Automatic - no setup needed -->
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<script type="module">
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import { Brainy } from 'brainy'
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const brain = new Brainy()
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await brain.init() // Works in browser
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</script>
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```
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## 🚨 Troubleshooting
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| Error | Solution |
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|-------|----------|
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| "Failed to load embedding model" | `npm run download-models` |
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| "ENOENT: no such file" | Check `BRAINY_MODELS_PATH` |
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| "Network timeout" | Set `BRAINY_ALLOW_REMOTE_MODELS=false` |
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| "Permission denied" | `chmod 755 ./models` |
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| "Out of memory" | Increase container memory limit |
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## 🎯 Environment Variables
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| Variable | Values | Purpose |
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|----------|--------|---------|
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| `BRAINY_ALLOW_REMOTE_MODELS` | `true`/`false` | Allow/block downloads |
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| `BRAINY_MODELS_PATH` | `./models` | Model storage path |
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| `BRAINY_Q8_CONFIRMED` | `true`/`false` | Silence Q8 compatibility warnings |
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| `NODE_ENV` | `production` | Environment detection |
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## 📦 Model Info
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### FP32 (Default)
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- **Model**: All-MiniLM-L6-v2
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- **Dimensions**: 384 (fixed)
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- **Size**: 90MB
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- **Accuracy**: 100% (baseline)
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- **Location**: `./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx`
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### Q8 (Optional - v2.8.0+)
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- **Model**: All-MiniLM-L6-v2 (quantized)
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- **Dimensions**: 384 (same)
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- **Size**: 23MB (75% smaller!)
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- **Accuracy**: ~99% (minimal loss)
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- **Location**: `./models/Xenova/all-MiniLM-L6-v2/onnx/model_quantized.onnx`
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**⚠️ Important**: FP32 and Q8 create different embeddings and are incompatible!
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## ✅ Verification Commands
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```bash
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# Check FP32 model exists
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ls ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
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# Check Q8 model exists
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ls ./models/Xenova/all-MiniLM-L6-v2/onnx/model_quantized.onnx
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# Test offline mode
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BRAINY_ALLOW_REMOTE_MODELS=false npm test
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# Download fresh models (both)
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rm -rf ./models && npm run download-models
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# Download specific model variant
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rm -rf ./models && npm run download-models:q8
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```
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@ -1,6 +1,8 @@
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# Production Service Architecture Guide
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**How to use Brainy optimally in production Express/Node.js services**
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**How to use Brainy optimally in production services (Bun, Node.js, Deno)**
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> **Recommended Runtime:** [Bun](https://bun.sh) provides best performance with Brainy's Candle WASM engine. All examples work with both Bun and Node.js.
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---
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@ -168,7 +170,57 @@ process.on('SIGTERM', async () => {
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---
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### Pattern 3: Express Middleware
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### Pattern 3: Bun Server (Recommended)
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```typescript
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// server.ts - Clean Bun implementation
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import { Brainy } from '@soulcraft/brainy'
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let brain: Brainy | null = null
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async function getBrain(): Promise<Brainy> {
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if (!brain) {
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brain = new Brainy({ storage: { path: './brainy-data' } })
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await brain.init()
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}
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return brain
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}
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// Initialize before server starts
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await getBrain()
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Bun.serve({
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port: 3000,
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async fetch(req) {
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const url = new URL(req.url)
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if (url.pathname === '/api/entities') {
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const b = await getBrain()
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const entities = await b.find({})
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return Response.json(entities)
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}
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if (url.pathname === '/api/entity' && req.method === 'POST') {
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const b = await getBrain()
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const body = await req.json()
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const id = await b.add(body)
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return Response.json({ id })
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}
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return new Response('Not Found', { status: 404 })
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}
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})
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console.log('Server running on http://localhost:3000')
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```
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**Benefits:**
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- ✅ Native Bun performance (~2x faster than Node.js)
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- ✅ No framework dependencies
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- ✅ Works with `bun --compile` for single-binary deployment
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- ✅ Built-in TypeScript support
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### Pattern 4: Express/Node.js Middleware (Legacy)
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```typescript
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// middleware/brainy.ts
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@ -1373,12 +1373,9 @@ const brain = new Brainy({
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typeAware: true // Enable type-aware indexing (v4.0+)
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},
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// Model configuration
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model: {
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type: 'transformers', // transformers | custom
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name: 'Xenova/all-MiniLM-L6-v2',
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device: 'auto' // auto | cpu | gpu
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},
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// Model configuration (embedded in WASM - zero config needed)
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// Model: all-MiniLM-L6-v2 (384 dimensions)
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// Device: CPU via WASM (works everywhere)
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// Cache configuration
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cache: {
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// CUDA detection in Node:
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if (device === 'cuda') {
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// Requires ONNX Runtime GPU packages
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// Future: GPU acceleration support
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}
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```
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@ -80,7 +80,7 @@ const entity = {
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- ✅ **Semantic Understanding**: Types have meaning, not just structure
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- ✅ **Tool Compatibility**: All augmentations understand core types
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- ✅ **Concept Extraction**: NLP can map text to known types
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- ✅ **Type Inference**: Automatic type detection via keywords/synonyms
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- ✅ **Explicit Types**: Clear type specification in API
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- ✅ **Query Optimization**: Type-aware query planning
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- ✅ **Flexible Metadata**: Any fields within typed structure
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- ✅ **Billion-Scale Ready**: Type tracking scales linearly
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@ -121,45 +121,51 @@ class TypeAwareMetadataIndex {
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- **After**: PROJECTED 1.2MB memory for same dataset (385x reduction - calculated from Uint32Array size, not measured)
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- **Scales to billions**: Memory grows with entity count, not key diversity
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### 2. Semantic Type Inference
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### 2. Explicit Type System
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**The Magic**: Map natural language to structured types:
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**The Design**: Specify types clearly in your API calls:
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```typescript
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import { getSemanticTypeInference } from '@soulcraft/brainy'
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import { Brainy, NounType, VerbType } from '@soulcraft/brainy'
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const inference = getSemanticTypeInference()
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// Add entity with explicit type
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await brain.add({
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data: { name: 'Alice', role: 'CEO of Acme Corp' },
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type: NounType.Person // Explicit type specification
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})
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// Automatic type detection
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await inference.inferNounType('CEO of Acme Corp')
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// → 'person'
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await inference.inferNounType('San Francisco office building')
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// → 'place'
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await inference.inferVerbType('Alice manages Bob')
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// → 'manages' (relationship type)
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// Query with type filtering
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await brain.find({
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query: 'Alice',
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type: NounType.Person // Type-optimized search
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})
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```
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**How It Works**:
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1. **Keyword Matching**: "CEO", "manager" → 'person'
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2. **Synonym Detection**: "building", "office" → 'place'
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3. **Semantic Embeddings**: Vector similarity to type prototypes
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4. **Context Analysis**: Surrounding words provide hints
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**Why Explicit Types?**:
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1. **Deterministic**: You control exactly how entities are classified
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2. **Predictable**: No inference surprises or edge cases
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3. **Fast**: No neural processing overhead on every add/query
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4. **Smaller**: No embedded keyword models needed
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**Real-World Use Case**:
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```typescript
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// Import unstructured data
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const text = "Apple announced a new product line in Cupertino"
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// Import data with known types
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await brain.add({
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data: { name: 'Apple Inc.', industry: 'Technology' },
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type: NounType.Organization
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})
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// Brainy automatically infers:
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// - "Apple" → noun type: 'organization'
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// - "product line" → noun type: 'product'
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// - "Cupertino" → noun type: 'place'
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// - "announced" → verb type: 'announces'
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// - "in" → verb type: 'locatedIn'
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await brain.add({
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data: { name: 'Cupertino', country: 'USA' },
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type: NounType.Location
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})
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// Creates typed, queryable knowledge graph automatically!
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// Create relationship
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await brain.relate({
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from: appleId,
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to: cupertinoId,
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type: VerbType.LocatedIn
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})
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```
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### 3. Tool & Augmentation Compatibility
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@ -364,42 +370,47 @@ function processNoun(noun: Noun) {
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---
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## Public API: Semantic Type Inference
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## Public API: Type System
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The type inference system is **fully public** for augmentation developers and external tools:
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The type system is **fully public** for developers and augmentation authors:
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```typescript
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import {
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getSemanticTypeInference,
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SemanticTypeInference
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NounType,
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VerbType,
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getNounTypes,
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getVerbTypes,
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BrainyTypes,
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suggestType
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} from '@soulcraft/brainy'
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// Get singleton instance
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const inference = getSemanticTypeInference()
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// Get all available noun types
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const nounTypes = getNounTypes()
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// → ['Person', 'Organization', 'Location', 'Thing', 'Concept', ...]
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// Infer noun type from text
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const nounType = await inference.inferNounType('Software Engineer')
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// → 'person'
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// Get all available verb types
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const verbTypes = getVerbTypes()
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// → ['RelatedTo', 'Contains', 'CreatedBy', 'LocatedIn', ...]
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// Infer verb type from relationship text
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const verbType = await inference.inferVerbType('works at')
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// → 'worksAt'
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// Use types directly
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await brain.add({
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data: { name: 'Alice' },
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type: NounType.Person
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})
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// Get type keywords for reverse lookup
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const keywords = inference.getNounTypeKeywords('person')
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// → ['person', 'human', 'individual', 'user', 'employee', ...]
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// Get type synonyms
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const synonyms = inference.getNounTypeSynonyms('organization')
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// → ['company', 'corporation', 'business', 'firm', 'enterprise', ...]
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// Query by type
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await brain.find({
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type: NounType.Person,
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where: { name: 'Alice' }
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})
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```
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**Use Cases**:
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- **Import Tools**: Auto-detect entity types during data import
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- **Query Builders**: Suggest types based on user input
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- **Type-Safe Code**: Use TypeScript enums for compile-time checking
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- **Import Tools**: Specify entity types during data import
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- **Query Builders**: Filter by known types
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- **Augmentations**: Type-specific processing pipelines
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- **Visualization**: Type-appropriate rendering
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- **Data Validation**: Ensure correct type assignments
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---
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@ -415,7 +426,7 @@ const synonyms = inference.getNounTypeSynonyms('organization')
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| **Query Planning** | Impossible | Table statistics | Type statistics |
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| **Tool Compatibility** | None | SQL only | Full ecosystem |
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| **Semantic Understanding** | None | None | Built-in |
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| **Concept Extraction** | Manual | Manual | Automatic |
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| **Concept Extraction** | Manual | Manual | Via SmartExtractor |
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| **Flexibility** | Infinite | Zero | Optimal balance |
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---
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@ -483,7 +494,7 @@ Brainy's **Finite Noun/Verb Type System** is revolutionary because it achieves t
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2. ✅ **Semantic understanding** (NLP integration)
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3. ✅ **Tool compatibility** (ecosystem interoperability)
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4. ✅ **Query optimization** (type-aware planning)
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5. ✅ **Concept extraction** (automatic type inference)
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5. ✅ **Concept extraction** (via SmartExtractor for imports)
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6. ✅ **Developer experience** (clean architecture)
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7. ✅ **Flexibility** (metadata freedom within types)
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@ -493,11 +504,10 @@ It's not schemaless chaos. It's not rigid relational constraints. It's **semanti
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## Further Reading
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- [Type Inference System](../api/type-inference.md) - API reference for semantic type detection
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- [Storage Architecture](./storage-architecture.md) - How types enable billion-scale storage
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- [Augmentation System](./augmentations.md) - Building type-aware augmentations
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- [Concept Extraction](../guides/natural-language.md) - NLP integration with typed entities
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- [Query Optimization](../api/query-optimization.md) - Type-aware query planning
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- [Import Flow](../guides/import-flow.md) - How types work in the import pipeline
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---
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@ -25,7 +25,9 @@ Built-in authentication and rate limiting when needed.
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The APIServerAugmentation is included in Brainy core. No additional installation required.
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For Node.js environments, you may want to install optional dependencies:
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**Bun** (recommended): No additional dependencies needed - use `Bun.serve()` directly.
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**Node.js**: Install optional Express dependencies:
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```bash
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npm install express cors ws
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```
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@ -259,6 +261,30 @@ Authorization: Bearer your-token
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## Environment Support
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### Bun ✅ (Recommended)
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Native performance with Bun.serve() - no Express required. Works with `bun --compile` for single-binary deployment.
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```typescript
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// Simple Bun server with Brainy
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import { Brainy } from '@soulcraft/brainy'
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const brain = new Brainy()
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await brain.init()
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Bun.serve({
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port: 3000,
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async fetch(req) {
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const url = new URL(req.url)
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if (url.pathname === '/api/search') {
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const { query } = await req.json()
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const results = await brain.search(query)
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return Response.json(results)
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}
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return new Response('Not Found', { status: 404 })
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}
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})
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```
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### Node.js ✅
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Full support with Express, WebSocket, and all features.
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|
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|
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@ -256,9 +256,9 @@ if (device === 'webgpu') {
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// Transformer models use WebGPU automatically
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||||
}
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// CUDA in Node.js (requires ONNX Runtime GPU)
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||||
// CUDA in Node.js (future GPU support)
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||||
if (device === 'cuda') {
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||||
// Automatically uses GPU for embeddings
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||||
// Future: GPU acceleration for embeddings
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||||
}
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||||
```
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|
|
@ -470,7 +470,7 @@ const verbType = await this.inferRelationship(
|
|||
|
||||
**Location**: `src/neural/SmartRelationshipExtractor.ts:100`
|
||||
|
||||
The SmartRelationshipExtractor runs **4 signals in parallel** (just like entity extraction):
|
||||
The SmartRelationshipExtractor runs **3 signals in parallel**:
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────────────┐
|
||||
|
|
@ -480,40 +480,34 @@ The SmartRelationshipExtractor runs **4 signals in parallel** (just like entity
|
|||
│ Input Context: │
|
||||
│ "Famous painting created by Leonardo da Vinci" │
|
||||
│ │
|
||||
│ 1. VerbExactMatchSignal (40%) │
|
||||
│ → Searches 334 verb keywords │
|
||||
│ → Finds phrase: "created by" │
|
||||
│ → Maps to: VerbType.CreatedBy │
|
||||
│ → Confidence: 0.95 │
|
||||
│ │
|
||||
│ 2. VerbEmbeddingSignal (35%) │
|
||||
│ 1. VerbEmbeddingSignal (55%) │
|
||||
│ → Embeds context: [0.23, -0.45, 0.78, ...] │
|
||||
│ → Compares to 40 verb embeddings │
|
||||
│ → Closest match: CreatedBy (similarity: 0.89) │
|
||||
│ → Confidence: 0.89 │
|
||||
│ │
|
||||
│ 3. VerbPatternSignal (20%) │
|
||||
│ 2. VerbPatternSignal (30%) │
|
||||
│ → Tests 48+ regex patterns │
|
||||
│ → Matches: /\bcreated?\s+by\b/i │
|
||||
│ → Maps to: VerbType.CreatedBy │
|
||||
│ → Confidence: 0.90 │
|
||||
│ │
|
||||
│ 4. VerbContextSignal (5%) │
|
||||
│ 3. VerbContextSignal (15%) │
|
||||
│ → Type pair: (Product, Person) │
|
||||
│ → Hint suggests: CreatedBy │
|
||||
│ → Confidence: 0.80 │
|
||||
│ │
|
||||
│ Ensemble Vote: │
|
||||
│ CreatedBy: 0.95×0.40 + 0.89×0.35 + 0.90×0.20 + 0.80×0.05│
|
||||
│ = 0.38 + 0.31 + 0.18 + 0.04 │
|
||||
│ = 0.91 │
|
||||
│ CreatedBy: 0.89×0.55 + 0.90×0.30 + 0.80×0.15 │
|
||||
│ = 0.49 + 0.27 + 0.12 │
|
||||
│ = 0.88 │
|
||||
│ │
|
||||
│ Agreement Boost: │
|
||||
│ → 4 signals agree on CreatedBy! │
|
||||
│ → Boost: +0.05 × (4-1) = +0.15 │
|
||||
│ → Final: 0.91 + 0.15 = 1.06 → capped at 0.99 │
|
||||
│ → 3 signals agree on CreatedBy! │
|
||||
│ → Boost: +0.05 × (3-1) = +0.10 │
|
||||
│ → Final: 0.88 + 0.10 = 0.98 │
|
||||
│ │
|
||||
│ Winner: CreatedBy (0.99 confidence) 🎯 │
|
||||
│ Winner: CreatedBy (0.98 confidence) 🎯 │
|
||||
└──────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
|
|
@ -897,8 +891,8 @@ const vector = await this.embed('Mona Lisa')
|
|||
```
|
||||
|
||||
**Embedding Service**:
|
||||
- Default: Uses `@xenova/transformers` (local, no API calls!)
|
||||
- Model: `Xenova/all-MiniLM-L6-v2` (384 dimensions)
|
||||
- Uses Candle WASM (local, no API calls, no downloads!)
|
||||
- Model: `all-MiniLM-L6-v2` embedded in WASM (384 dimensions)
|
||||
- Performance: ~5-15ms per embedding
|
||||
|
||||
**Output**:
|
||||
|
|
@ -1868,10 +1862,9 @@ groupBy: 'type'
|
|||
│ └─ ContextSignal (5%) │
|
||||
│ │
|
||||
│ SmartRelationshipExtractor (Verb Types): │
|
||||
│ ├─ VerbExactMatchSignal (40%) │
|
||||
│ ├─ VerbEmbeddingSignal (35%) │
|
||||
│ ├─ VerbPatternSignal (20%) │
|
||||
│ └─ VerbContextSignal (5%) │
|
||||
│ ├─ VerbEmbeddingSignal (55%) │
|
||||
│ ├─ VerbPatternSignal (30%) │
|
||||
│ └─ VerbContextSignal (15%) │
|
||||
│ │
|
||||
│ Result: Intelligent entities + relationships │
|
||||
└───────────────────────────────────────────────┘
|
||||
|
|
|
|||
|
|
@ -1,358 +1,236 @@
|
|||
# 🤖 Model Loading Guide
|
||||
# Model Loading Guide
|
||||
|
||||
Brainy uses AI embedding models to understand and process your data. This guide explains how model loading works and how to handle different scenarios.
|
||||
Brainy uses AI embedding models to understand and process your data. With the Candle WASM engine, the model is **embedded at compile time** - no downloads, no configuration, no external dependencies.
|
||||
|
||||
## 🚀 Zero Configuration (Default)
|
||||
## Zero Configuration (Default)
|
||||
|
||||
**For most developers, no configuration is needed:**
|
||||
**For all developers, no configuration is needed:**
|
||||
|
||||
```typescript
|
||||
const brain = new Brainy()
|
||||
await brain.init() // Models load automatically
|
||||
await brain.init() // Model is already embedded - nothing to download!
|
||||
```
|
||||
|
||||
**What happens automatically:**
|
||||
1. Checks for local models in `./models/`
|
||||
2. Downloads All-MiniLM-L6-v2 if needed (384 dimensions)
|
||||
3. Configures optimal settings for your environment
|
||||
4. Ready to use immediately
|
||||
1. Candle WASM module loads (~90MB, includes model weights)
|
||||
2. Model initializes in ~200ms
|
||||
3. Ready to use immediately
|
||||
|
||||
## 📦 Model Loading Cascade
|
||||
**No downloads. No CDN. No configuration. Just works.**
|
||||
|
||||
Brainy tries multiple sources in this order:
|
||||
## How It Works
|
||||
|
||||
The all-MiniLM-L6-v2 model is embedded in the WASM binary using Rust's `include_bytes!` macro:
|
||||
|
||||
```
|
||||
1. LOCAL CACHE (./models/)
|
||||
↓ (if not found)
|
||||
2. CDN DOWNLOAD (fast mirrors)
|
||||
↓ (if fails)
|
||||
3. GITHUB RELEASES (github.com/xenova/transformers.js)
|
||||
↓ (if fails)
|
||||
4. HUGGINGFACE HUB (huggingface.co)
|
||||
↓ (if fails)
|
||||
5. FALLBACK STRATEGIES (different model variants)
|
||||
candle_embeddings_bg.wasm (~90MB)
|
||||
├── Candle ML Runtime (~3MB)
|
||||
├── Model Weights (safetensors format, ~87MB)
|
||||
└── Tokenizer (HuggingFace tokenizers, ~450KB)
|
||||
```
|
||||
|
||||
## 🌍 Environment-Specific Behavior
|
||||
This single WASM file contains everything needed for sentence embeddings.
|
||||
|
||||
## Environments
|
||||
|
||||
### Bun (Recommended)
|
||||
|
||||
```typescript
|
||||
// Works with Bun runtime
|
||||
bun run server.ts
|
||||
|
||||
// Works with bun --compile (single binary deployment!)
|
||||
bun build --compile --target=bun server.ts
|
||||
./server // Self-contained binary with embedded model
|
||||
```
|
||||
|
||||
### Node.js
|
||||
|
||||
```typescript
|
||||
// Standard Node.js
|
||||
node dist/server.js
|
||||
|
||||
// Runs identically to Bun
|
||||
```
|
||||
|
||||
### Browser
|
||||
```typescript
|
||||
// Automatically configured for browsers
|
||||
const brain = new Brainy() // Works in React, Vue, vanilla JS
|
||||
await brain.init() // Downloads models via CDN
|
||||
```
|
||||
|
||||
### Node.js Development
|
||||
```typescript
|
||||
// Zero config - downloads to ./models/
|
||||
// Model loads via WASM (single file, no additional assets)
|
||||
const brain = new Brainy()
|
||||
await brain.init() // Downloads once, cached forever
|
||||
```
|
||||
|
||||
### Production Server
|
||||
```typescript
|
||||
// Preload models during build/deployment
|
||||
const brain = new Brainy()
|
||||
await brain.init() // Uses cached local models
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
### Docker/Kubernetes
|
||||
|
||||
```dockerfile
|
||||
# Dockerfile - preload models
|
||||
RUN npm run download-models
|
||||
ENV BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
## 🛠️ Manual Model Management
|
||||
|
||||
### Pre-download Models
|
||||
```bash
|
||||
# Download models during build/deployment
|
||||
npm run download-models
|
||||
|
||||
# Custom location
|
||||
BRAINY_MODELS_PATH=./my-models npm run download-models
|
||||
```
|
||||
|
||||
### Verify Models
|
||||
```bash
|
||||
# Check if models exist
|
||||
ls ./models/Xenova/all-MiniLM-L6-v2/
|
||||
|
||||
# Should see:
|
||||
# - config.json
|
||||
# - tokenizer.json
|
||||
# - onnx/model.onnx
|
||||
```
|
||||
|
||||
### Custom Model Path
|
||||
```typescript
|
||||
const brain = new Brainy({
|
||||
embedding: {
|
||||
cacheDir: './custom-models'
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
## 🔒 Offline & Air-Gapped Environments
|
||||
|
||||
### Complete Offline Setup
|
||||
```bash
|
||||
# 1. Download models on connected machine
|
||||
npm run download-models
|
||||
|
||||
# 2. Copy models to offline machine
|
||||
cp -r ./models /path/to/offline/project/
|
||||
|
||||
# 3. Force local-only mode
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
### Container/Server Deployment
|
||||
```dockerfile
|
||||
FROM node:18
|
||||
FROM oven/bun:1.1
|
||||
WORKDIR /app
|
||||
COPY package*.json ./
|
||||
RUN npm ci
|
||||
|
||||
# Download models during build
|
||||
RUN npm run download-models
|
||||
|
||||
# Force local-only in production
|
||||
ENV BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
|
||||
RUN bun install
|
||||
COPY . .
|
||||
EXPOSE 3000
|
||||
CMD ["npm", "start"]
|
||||
CMD ["bun", "run", "server.ts"]
|
||||
|
||||
# That's it! No model download step needed.
|
||||
# Model is embedded in the npm package.
|
||||
```
|
||||
|
||||
## ⚙️ Environment Variables
|
||||
## Model Information
|
||||
|
||||
### BRAINY_ALLOW_REMOTE_MODELS
|
||||
Controls whether remote model downloads are allowed:
|
||||
### all-MiniLM-L6-v2 (Embedded)
|
||||
- **Dimensions**: 384 (fixed)
|
||||
- **Format**: Safetensors (FP32)
|
||||
- **Size**: ~87MB (embedded in WASM)
|
||||
- **Total WASM Size**: ~90MB
|
||||
- **Language**: English-optimized, works with all languages
|
||||
- **Inference**: ~2-10ms per embedding
|
||||
- **Initialization**: ~200ms
|
||||
|
||||
```bash
|
||||
# Allow remote downloads (default in most environments)
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=true
|
||||
### Memory Usage
|
||||
- **Loaded WASM**: ~90MB
|
||||
- **Inference peak**: ~140MB total
|
||||
- **Steady state**: ~100MB
|
||||
|
||||
# Force local-only (recommended for production)
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
## Comparing to Previous Architecture
|
||||
|
||||
### BRAINY_MODELS_PATH
|
||||
Custom model storage location:
|
||||
| Feature | Before (ONNX) | Now (Candle WASM) |
|
||||
|---------|--------------|-------------------|
|
||||
| Model downloads | Required on first use | None - embedded |
|
||||
| External dependencies | onnxruntime-web | None |
|
||||
| Model files | model.onnx, tokenizer.json | Embedded in WASM |
|
||||
| Offline support | Required setup | Works by default |
|
||||
| Bun compile | Broken | Works |
|
||||
| Configuration | Environment variables | None needed |
|
||||
|
||||
```bash
|
||||
# Custom model path
|
||||
export BRAINY_MODELS_PATH=/opt/brainy/models
|
||||
## Troubleshooting
|
||||
|
||||
# Relative path
|
||||
export BRAINY_MODELS_PATH=./my-custom-models
|
||||
```
|
||||
### "Failed to initialize Candle Embedding Engine"
|
||||
|
||||
## 🚨 Troubleshooting
|
||||
|
||||
### "Failed to load embedding model" Error
|
||||
|
||||
**Cause**: Models not found locally and remote download blocked/failed.
|
||||
**Cause**: WASM loading issue.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Option 1: Allow remote downloads
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=true
|
||||
# Rebuild the WASM
|
||||
npm run build:candle
|
||||
|
||||
# Option 2: Download models manually
|
||||
npm run download-models
|
||||
|
||||
# Option 3: Check internet connectivity
|
||||
ping huggingface.co
|
||||
|
||||
# Option 4: Use custom model path
|
||||
export BRAINY_MODELS_PATH=/path/to/existing/models
|
||||
# Verify WASM exists
|
||||
ls dist/embeddings/wasm/pkg/candle_embeddings_bg.wasm
|
||||
# Should be ~90MB
|
||||
```
|
||||
|
||||
### Models Download Very Slowly
|
||||
### Out of Memory
|
||||
|
||||
**Cause**: Network issues or regional restrictions.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Pre-download during build/CI
|
||||
npm run download-models
|
||||
|
||||
# Use faster mirrors (automatic in newer versions)
|
||||
# No action needed - Brainy tries multiple CDNs
|
||||
```
|
||||
|
||||
### Container Out of Memory During Model Load
|
||||
|
||||
**Cause**: Limited container memory during model initialization.
|
||||
**Cause**: Container/environment has less than 256MB RAM.
|
||||
|
||||
**Solutions**:
|
||||
```dockerfile
|
||||
# Increase memory limit
|
||||
docker run -m 2g my-app
|
||||
|
||||
# Use quantized models (default)
|
||||
ENV BRAINY_MODEL_DTYPE=q8
|
||||
|
||||
# Pre-load models at build time (recommended)
|
||||
RUN npm run download-models
|
||||
# Increase memory limit (recommended: 512MB+)
|
||||
docker run -m 512m my-app
|
||||
```
|
||||
|
||||
### Permission Denied Creating Model Cache
|
||||
### Slow Initialization (>500ms)
|
||||
|
||||
**Cause**: Write permissions for model cache directory.
|
||||
**Cause**: Cold start, large WASM parsing.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Make directory writable
|
||||
chmod 755 ./models
|
||||
```typescript
|
||||
// Initialize once at startup, not per-request
|
||||
await brain.init() // Do this once
|
||||
|
||||
# Use custom writable path
|
||||
export BRAINY_MODELS_PATH=/tmp/brainy-models
|
||||
|
||||
# Or use memory-only storage
|
||||
const brain = new Brainy({
|
||||
storage: { forceMemoryStorage: true }
|
||||
// Then reuse for all requests
|
||||
app.get('/api', async (req, res) => {
|
||||
const results = await brain.find(req.query)
|
||||
res.json(results)
|
||||
})
|
||||
```
|
||||
|
||||
## 🎯 Best Practices
|
||||
## Migration from Previous Versions
|
||||
|
||||
### From v6.x (ONNX)
|
||||
|
||||
No changes needed for most users:
|
||||
|
||||
```typescript
|
||||
// Same API - just upgrade
|
||||
const brain = new Brainy()
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
**What's removed:**
|
||||
- `BRAINY_ALLOW_REMOTE_MODELS` - no downloads
|
||||
- `BRAINY_MODELS_PATH` - no external model files
|
||||
- `npm run download-models` - no longer needed
|
||||
|
||||
**What's new:**
|
||||
- Faster initialization
|
||||
- Works with `bun --compile`
|
||||
- No network requirements
|
||||
|
||||
### From Custom Embedding Functions
|
||||
|
||||
If you provided a custom embedding function, it still works:
|
||||
|
||||
```typescript
|
||||
const brain = new Brainy({
|
||||
embeddingFunction: myCustomEmbedder // Still supported
|
||||
})
|
||||
```
|
||||
|
||||
## Advanced: Building Custom WASM
|
||||
|
||||
For contributors who want to modify the embedding engine:
|
||||
|
||||
```bash
|
||||
# Navigate to Candle WASM source
|
||||
cd src/embeddings/candle-wasm
|
||||
|
||||
# Build with wasm-pack
|
||||
wasm-pack build --target web --release
|
||||
|
||||
# Copy to pkg folder
|
||||
cp pkg/* ../wasm/pkg/
|
||||
|
||||
# Build TypeScript
|
||||
npm run build
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Development
|
||||
```typescript
|
||||
// ✅ Zero config - just works
|
||||
// Just works - no setup
|
||||
const brain = new Brainy()
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
### Production
|
||||
```dockerfile
|
||||
# ✅ Pre-download models
|
||||
RUN npm run download-models
|
||||
|
||||
# ✅ Force local-only
|
||||
ENV BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
|
||||
# ✅ Verify models exist
|
||||
RUN test -f ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
|
||||
```
|
||||
|
||||
### CI/CD Pipeline
|
||||
```yaml
|
||||
# .github/workflows/build.yml
|
||||
- name: Download AI Models
|
||||
run: npm run download-models
|
||||
|
||||
- name: Verify Models
|
||||
run: |
|
||||
test -f ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
|
||||
echo "✅ Models verified"
|
||||
|
||||
- name: Test Offline Mode
|
||||
env:
|
||||
BRAINY_ALLOW_REMOTE_MODELS: false
|
||||
run: npm test
|
||||
```
|
||||
|
||||
### Lambda/Serverless
|
||||
```typescript
|
||||
// ✅ Models in deployment package
|
||||
const brain = new Brainy({
|
||||
embedding: {
|
||||
localFilesOnly: true, // No downloads in lambda
|
||||
cacheDir: './models' // Bundled with deployment
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
## 📊 Model Information
|
||||
|
||||
### All-MiniLM-L6-v2 (Default)
|
||||
- **Dimensions**: 384 (fixed)
|
||||
- **Size**: ~80MB compressed, ~330MB uncompressed
|
||||
- **Language**: English (optimized)
|
||||
- **Speed**: Very fast inference
|
||||
- **Quality**: High quality for most use cases
|
||||
|
||||
### Model Files Structure
|
||||
```
|
||||
models/
|
||||
└── Xenova/
|
||||
└── all-MiniLM-L6-v2/
|
||||
├── config.json # Model configuration
|
||||
├── tokenizer.json # Text tokenizer
|
||||
├── tokenizer_config.json
|
||||
└── onnx/
|
||||
├── model.onnx # Main model file
|
||||
└── model_quantized.onnx # Optimized version
|
||||
```
|
||||
|
||||
## 🔄 Migration from Other Embedding Solutions
|
||||
|
||||
### From OpenAI Embeddings
|
||||
```typescript
|
||||
// Before: OpenAI API calls
|
||||
const response = await openai.embeddings.create({
|
||||
model: "text-embedding-ada-002",
|
||||
input: "Your text"
|
||||
})
|
||||
|
||||
// After: Local Brainy embeddings
|
||||
// Initialize once at startup
|
||||
const brain = new Brainy()
|
||||
await brain.init() // One-time setup
|
||||
const id = await brain.add("Your text", { nounType: 'content' }) // Embedded automatically
|
||||
```
|
||||
|
||||
### From Sentence Transformers
|
||||
```python
|
||||
# Before: Python sentence-transformers
|
||||
from sentence_transformers import SentenceTransformer
|
||||
model = SentenceTransformer('all-MiniLM-L6-v2')
|
||||
|
||||
# After: JavaScript Brainy (same model!)
|
||||
const brain = new Brainy() // Uses same all-MiniLM-L6-v2
|
||||
await brain.init()
|
||||
|
||||
// Singleton pattern recommended
|
||||
export { brain }
|
||||
```
|
||||
|
||||
## 🚀 Advanced Configuration
|
||||
### Deployment
|
||||
```bash
|
||||
# Option 1: Bun compile (single binary)
|
||||
bun build --compile server.ts
|
||||
./server # Contains everything
|
||||
|
||||
### Custom Embedding Options
|
||||
```typescript
|
||||
const brain = new Brainy({
|
||||
embedding: {
|
||||
model: 'Xenova/all-MiniLM-L6-v2', // Default
|
||||
dtype: 'q8', // Quantized for speed
|
||||
device: 'cpu', // CPU inference
|
||||
localFilesOnly: false, // Allow downloads
|
||||
verbose: true // Debug logging
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### Multiple Model Support (Advanced)
|
||||
```typescript
|
||||
// Use custom embedding function
|
||||
import { createEmbeddingFunction } from 'brainy'
|
||||
|
||||
const customEmbedder = createEmbeddingFunction({
|
||||
model: 'Xenova/all-MiniLM-L12-v2', // Larger model
|
||||
dtype: 'fp32' // Higher precision
|
||||
})
|
||||
|
||||
const brain = new Brainy({
|
||||
embeddingFunction: customEmbedder
|
||||
})
|
||||
# Option 2: Docker
|
||||
docker build -t my-app .
|
||||
docker run -p 3000:3000 my-app
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Additional Resources
|
||||
## Additional Resources
|
||||
|
||||
- [Zero Configuration Guide](./zero-config.md)
|
||||
- [Enterprise Deployment](./enterprise-deployment.md)
|
||||
- [Production Service Architecture](../PRODUCTION_SERVICE_ARCHITECTURE.md)
|
||||
- [Zero Configuration Guide](../architecture/zero-config.md)
|
||||
- [Troubleshooting Guide](../troubleshooting.md)
|
||||
- [API Reference](../api/README.md)
|
||||
|
||||
**Need help?** Check our [troubleshooting guide](../troubleshooting.md) or [open an issue](https://github.com/your-repo/brainy/issues).
|
||||
**Need help?** [Open an issue](https://github.com/soulcraftlabs/brainy/issues)
|
||||
|
|
|
|||
|
|
@ -49,10 +49,9 @@ else:
|
|||
System Memory: 2048 MB
|
||||
OS Reserved (20%): -410 MB
|
||||
Available: 1638 MB
|
||||
Model Memory (Q8): -150 MB
|
||||
├─ Weights: 22 MB
|
||||
├─ ONNX Runtime: 30 MB
|
||||
└─ Workspace: 98 MB
|
||||
Model Memory: -140 MB
|
||||
├─ WASM + Weights: 90 MB
|
||||
└─ Workspace: 50 MB
|
||||
───────────────────────────
|
||||
Available for Cache: 1488 MB
|
||||
Dev Allocation (25%): 372 MB UnifiedCache
|
||||
|
|
|
|||
|
|
@ -4,47 +4,48 @@ Common issues and solutions for Brainy.
|
|||
|
||||
## 🤖 Model Loading Issues
|
||||
|
||||
### "Failed to load embedding model"
|
||||
### "Failed to initialize Candle Embedding Engine"
|
||||
|
||||
**Symptoms**: Error during `brain.init()` with model loading failure.
|
||||
**Symptoms**: Error during `brain.init()` with WASM loading failure.
|
||||
|
||||
**Causes & Solutions**:
|
||||
|
||||
1. **No local models + remote downloads blocked**
|
||||
1. **WASM file missing**
|
||||
```bash
|
||||
# Solution: Download models manually
|
||||
npm run download-models
|
||||
# Verify WASM exists (~90MB with embedded model)
|
||||
ls -lh dist/embeddings/wasm/pkg/candle_embeddings_bg.wasm
|
||||
|
||||
# Rebuild if missing
|
||||
npm run build
|
||||
```
|
||||
|
||||
2. **Network connectivity issues**
|
||||
2. **Memory too low**
|
||||
```bash
|
||||
# Solution: Allow remote models
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=true
|
||||
|
||||
# Or pre-download in connected environment
|
||||
npm run download-models
|
||||
# Ensure at least 256MB available
|
||||
# For Docker:
|
||||
docker run -m 512m my-app
|
||||
```
|
||||
|
||||
3. **Incorrect model path**
|
||||
3. **Corrupted WASM**
|
||||
```bash
|
||||
# Check if models exist
|
||||
ls ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
|
||||
|
||||
# Set correct path
|
||||
export BRAINY_MODELS_PATH=/correct/path/to/models
|
||||
# Rebuild the Candle WASM
|
||||
npm run build:candle
|
||||
npm run build
|
||||
```
|
||||
|
||||
### Models Download Very Slowly
|
||||
### Slow Initialization (>500ms)
|
||||
|
||||
**Symptoms**: Long wait times during first initialization.
|
||||
**Symptoms**: Long wait times during first `brain.init()`.
|
||||
|
||||
**Cause**: WASM parsing takes ~200ms, which is normal for the 90MB file.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Pre-download during build/CI
|
||||
npm run download-models
|
||||
```typescript
|
||||
// Initialize once at startup, not per-request
|
||||
await brain.init() // Do this once
|
||||
|
||||
# For Docker - download during image build
|
||||
RUN npm run download-models
|
||||
// Reuse for all requests
|
||||
const results = await brain.find(query)
|
||||
```
|
||||
|
||||
### Container Out of Memory During Model Load
|
||||
|
|
@ -388,8 +389,8 @@ node -e "console.log(process.memoryUsage())"
|
|||
# Platform info
|
||||
node -e "console.log(process.platform, process.arch)"
|
||||
|
||||
# Brainy models
|
||||
ls -la ./models/Xenova/all-MiniLM-L6-v2/
|
||||
# Verify WASM file exists (model embedded inside)
|
||||
ls -la dist/embeddings/wasm/pkg/candle_embeddings_bg.wasm
|
||||
```
|
||||
|
||||
### Report Issues
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue