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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@ -470,7 +470,7 @@ const verbType = await this.inferRelationship(
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**Location**: `src/neural/SmartRelationshipExtractor.ts:100`
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The SmartRelationshipExtractor runs **4 signals in parallel** (just like entity extraction):
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The SmartRelationshipExtractor runs **3 signals in parallel**:
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```
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┌──────────────────────────────────────────────────────────┐
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@ -480,40 +480,34 @@ The SmartRelationshipExtractor runs **4 signals in parallel** (just like entity
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│ Input Context: │
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│ "Famous painting created by Leonardo da Vinci" │
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│ │
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│ 1. VerbExactMatchSignal (40%) │
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│ → Searches 334 verb keywords │
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│ → Finds phrase: "created by" │
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│ → Maps to: VerbType.CreatedBy │
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│ → Confidence: 0.95 │
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│ │
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│ 2. VerbEmbeddingSignal (35%) │
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│ 1. VerbEmbeddingSignal (55%) │
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│ → Embeds context: [0.23, -0.45, 0.78, ...] │
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│ → Compares to 40 verb embeddings │
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│ → Closest match: CreatedBy (similarity: 0.89) │
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│ → Confidence: 0.89 │
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│ │
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│ 3. VerbPatternSignal (20%) │
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│ 2. VerbPatternSignal (30%) │
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│ → Tests 48+ regex patterns │
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│ → Matches: /\bcreated?\s+by\b/i │
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│ → Maps to: VerbType.CreatedBy │
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│ → Confidence: 0.90 │
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│ │
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│ 4. VerbContextSignal (5%) │
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│ 3. VerbContextSignal (15%) │
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│ → Type pair: (Product, Person) │
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│ → Hint suggests: CreatedBy │
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│ → Confidence: 0.80 │
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│ │
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│ Ensemble Vote: │
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│ CreatedBy: 0.95×0.40 + 0.89×0.35 + 0.90×0.20 + 0.80×0.05│
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│ = 0.38 + 0.31 + 0.18 + 0.04 │
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│ = 0.91 │
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│ CreatedBy: 0.89×0.55 + 0.90×0.30 + 0.80×0.15 │
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│ = 0.49 + 0.27 + 0.12 │
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│ = 0.88 │
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│ │
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│ Agreement Boost: │
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│ → 4 signals agree on CreatedBy! │
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│ → Boost: +0.05 × (4-1) = +0.15 │
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│ → Final: 0.91 + 0.15 = 1.06 → capped at 0.99 │
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│ → 3 signals agree on CreatedBy! │
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│ → Boost: +0.05 × (3-1) = +0.10 │
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│ → Final: 0.88 + 0.10 = 0.98 │
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│ │
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│ Winner: CreatedBy (0.99 confidence) 🎯 │
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│ Winner: CreatedBy (0.98 confidence) 🎯 │
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└──────────────────────────────────────────────────────────┘
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```
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@ -897,8 +891,8 @@ const vector = await this.embed('Mona Lisa')
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```
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**Embedding Service**:
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- Default: Uses `@xenova/transformers` (local, no API calls!)
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- Model: `Xenova/all-MiniLM-L6-v2` (384 dimensions)
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- Uses Candle WASM (local, no API calls, no downloads!)
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- Model: `all-MiniLM-L6-v2` embedded in WASM (384 dimensions)
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- Performance: ~5-15ms per embedding
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**Output**:
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@ -1868,10 +1862,9 @@ groupBy: 'type'
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│ └─ ContextSignal (5%) │
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│ │
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│ SmartRelationshipExtractor (Verb Types): │
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│ ├─ VerbExactMatchSignal (40%) │
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│ ├─ VerbEmbeddingSignal (35%) │
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│ ├─ VerbPatternSignal (20%) │
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│ └─ VerbContextSignal (5%) │
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│ ├─ VerbEmbeddingSignal (55%) │
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│ ├─ VerbPatternSignal (30%) │
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│ └─ VerbContextSignal (15%) │
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│ │
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│ Result: Intelligent entities + relationships │
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└───────────────────────────────────────────────┘
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@ -1,358 +1,236 @@
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# 🤖 Model Loading Guide
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# Model Loading Guide
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Brainy uses AI embedding models to understand and process your data. This guide explains how model loading works and how to handle different scenarios.
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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.
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## 🚀 Zero Configuration (Default)
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## Zero Configuration (Default)
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**For most developers, no configuration is needed:**
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**For all developers, no configuration is needed:**
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```typescript
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const brain = new Brainy()
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await brain.init() // Models load automatically
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await brain.init() // Model is already embedded - nothing to download!
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```
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**What happens automatically:**
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1. Checks for local models in `./models/`
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2. Downloads All-MiniLM-L6-v2 if needed (384 dimensions)
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3. Configures optimal settings for your environment
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4. Ready to use immediately
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1. Candle WASM module loads (~90MB, includes model weights)
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2. Model initializes in ~200ms
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3. Ready to use immediately
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## 📦 Model Loading Cascade
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**No downloads. No CDN. No configuration. Just works.**
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Brainy tries multiple sources in this order:
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## How It Works
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The all-MiniLM-L6-v2 model is embedded in the WASM binary using Rust's `include_bytes!` macro:
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```
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1. LOCAL CACHE (./models/)
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↓ (if not found)
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2. CDN DOWNLOAD (fast mirrors)
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↓ (if fails)
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3. GITHUB RELEASES (github.com/xenova/transformers.js)
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↓ (if fails)
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4. HUGGINGFACE HUB (huggingface.co)
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↓ (if fails)
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5. FALLBACK STRATEGIES (different model variants)
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candle_embeddings_bg.wasm (~90MB)
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├── Candle ML Runtime (~3MB)
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├── Model Weights (safetensors format, ~87MB)
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└── Tokenizer (HuggingFace tokenizers, ~450KB)
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```
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## 🌍 Environment-Specific Behavior
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This single WASM file contains everything needed for sentence embeddings.
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## Environments
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### Bun (Recommended)
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```typescript
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// Works with Bun runtime
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bun run server.ts
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// Works with bun --compile (single binary deployment!)
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bun build --compile --target=bun server.ts
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./server // Self-contained binary with embedded model
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```
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### Node.js
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```typescript
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// Standard Node.js
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node dist/server.js
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// Runs identically to Bun
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```
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### Browser
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```typescript
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// Automatically configured for browsers
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const brain = new Brainy() // Works in React, Vue, vanilla JS
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await brain.init() // Downloads models via CDN
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```
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### Node.js Development
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```typescript
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// Zero config - downloads to ./models/
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// Model loads via WASM (single file, no additional assets)
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const brain = new Brainy()
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await brain.init() // Downloads once, cached forever
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```
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### Production Server
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```typescript
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// Preload models during build/deployment
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const brain = new Brainy()
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await brain.init() // Uses cached local models
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await brain.init()
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```
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### Docker/Kubernetes
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```dockerfile
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# Dockerfile - preload models
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RUN npm run download-models
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ENV BRAINY_ALLOW_REMOTE_MODELS=false
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```
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## 🛠️ Manual Model Management
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### Pre-download Models
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```bash
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# Download models during build/deployment
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npm run download-models
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# Custom location
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BRAINY_MODELS_PATH=./my-models npm run download-models
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```
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### Verify Models
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```bash
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# Check if models exist
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ls ./models/Xenova/all-MiniLM-L6-v2/
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# Should see:
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# - config.json
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# - tokenizer.json
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# - onnx/model.onnx
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```
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### Custom Model Path
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```typescript
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const brain = new Brainy({
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embedding: {
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cacheDir: './custom-models'
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}
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})
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```
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## 🔒 Offline & Air-Gapped Environments
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### Complete Offline Setup
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```bash
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# 1. Download models on connected machine
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npm run download-models
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# 2. Copy models to offline machine
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cp -r ./models /path/to/offline/project/
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# 3. Force local-only mode
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export BRAINY_ALLOW_REMOTE_MODELS=false
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```
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### Container/Server Deployment
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```dockerfile
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FROM node:18
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FROM oven/bun:1.1
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WORKDIR /app
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COPY package*.json ./
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RUN npm ci
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# Download models during build
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RUN npm run download-models
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# Force local-only in production
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ENV BRAINY_ALLOW_REMOTE_MODELS=false
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RUN bun install
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COPY . .
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EXPOSE 3000
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CMD ["npm", "start"]
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CMD ["bun", "run", "server.ts"]
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# That's it! No model download step needed.
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# Model is embedded in the npm package.
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```
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## ⚙️ Environment Variables
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## Model Information
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### BRAINY_ALLOW_REMOTE_MODELS
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Controls whether remote model downloads are allowed:
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### all-MiniLM-L6-v2 (Embedded)
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- **Dimensions**: 384 (fixed)
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- **Format**: Safetensors (FP32)
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- **Size**: ~87MB (embedded in WASM)
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- **Total WASM Size**: ~90MB
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- **Language**: English-optimized, works with all languages
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- **Inference**: ~2-10ms per embedding
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- **Initialization**: ~200ms
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```bash
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# Allow remote downloads (default in most environments)
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export BRAINY_ALLOW_REMOTE_MODELS=true
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### Memory Usage
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- **Loaded WASM**: ~90MB
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- **Inference peak**: ~140MB total
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- **Steady state**: ~100MB
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# Force local-only (recommended for production)
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export BRAINY_ALLOW_REMOTE_MODELS=false
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```
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## Comparing to Previous Architecture
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### BRAINY_MODELS_PATH
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Custom model storage location:
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| Feature | Before (ONNX) | Now (Candle WASM) |
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|---------|--------------|-------------------|
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| Model downloads | Required on first use | None - embedded |
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| External dependencies | onnxruntime-web | None |
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| Model files | model.onnx, tokenizer.json | Embedded in WASM |
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| Offline support | Required setup | Works by default |
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| Bun compile | Broken | Works |
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| Configuration | Environment variables | None needed |
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```bash
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# Custom model path
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export BRAINY_MODELS_PATH=/opt/brainy/models
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## Troubleshooting
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# Relative path
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export BRAINY_MODELS_PATH=./my-custom-models
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```
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### "Failed to initialize Candle Embedding Engine"
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## 🚨 Troubleshooting
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### "Failed to load embedding model" Error
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**Cause**: Models not found locally and remote download blocked/failed.
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**Cause**: WASM loading issue.
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**Solutions**:
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```bash
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# Option 1: Allow remote downloads
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export BRAINY_ALLOW_REMOTE_MODELS=true
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# Rebuild the WASM
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npm run build:candle
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# Option 2: Download models manually
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npm run download-models
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# Option 3: Check internet connectivity
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ping huggingface.co
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# Option 4: Use custom model path
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export BRAINY_MODELS_PATH=/path/to/existing/models
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# Verify WASM exists
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ls dist/embeddings/wasm/pkg/candle_embeddings_bg.wasm
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# Should be ~90MB
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```
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### Models Download Very Slowly
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### Out of Memory
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**Cause**: Network issues or regional restrictions.
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**Solutions**:
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```bash
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# Pre-download during build/CI
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npm run download-models
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# Use faster mirrors (automatic in newer versions)
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# No action needed - Brainy tries multiple CDNs
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```
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### Container Out of Memory During Model Load
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**Cause**: Limited container memory during model initialization.
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**Cause**: Container/environment has less than 256MB RAM.
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**Solutions**:
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```dockerfile
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# Increase memory limit
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docker run -m 2g my-app
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# Use quantized models (default)
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ENV BRAINY_MODEL_DTYPE=q8
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# Pre-load models at build time (recommended)
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RUN npm run download-models
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# Increase memory limit (recommended: 512MB+)
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docker run -m 512m my-app
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```
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### Permission Denied Creating Model Cache
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### Slow Initialization (>500ms)
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**Cause**: Write permissions for model cache directory.
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**Cause**: Cold start, large WASM parsing.
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**Solutions**:
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```bash
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# Make directory writable
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chmod 755 ./models
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```typescript
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// Initialize once at startup, not per-request
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await brain.init() // Do this once
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# Use custom writable path
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export BRAINY_MODELS_PATH=/tmp/brainy-models
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# Or use memory-only storage
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const brain = new Brainy({
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storage: { forceMemoryStorage: true }
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// Then reuse for all requests
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app.get('/api', async (req, res) => {
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const results = await brain.find(req.query)
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res.json(results)
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})
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```
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## 🎯 Best Practices
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## Migration from Previous Versions
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### From v6.x (ONNX)
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No changes needed for most users:
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```typescript
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// Same API - just upgrade
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const brain = new Brainy()
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await brain.init()
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```
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**What's removed:**
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- `BRAINY_ALLOW_REMOTE_MODELS` - no downloads
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- `BRAINY_MODELS_PATH` - no external model files
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- `npm run download-models` - no longer needed
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**What's new:**
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- Faster initialization
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- Works with `bun --compile`
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- No network requirements
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### From Custom Embedding Functions
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If you provided a custom embedding function, it still works:
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```typescript
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const brain = new Brainy({
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embeddingFunction: myCustomEmbedder // Still supported
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})
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```
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## Advanced: Building Custom WASM
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For contributors who want to modify the embedding engine:
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```bash
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# Navigate to Candle WASM source
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cd src/embeddings/candle-wasm
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# Build with wasm-pack
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wasm-pack build --target web --release
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# Copy to pkg folder
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cp pkg/* ../wasm/pkg/
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# Build TypeScript
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npm run build
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```
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## Best Practices
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### Development
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```typescript
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// ✅ Zero config - just works
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// Just works - no setup
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const brain = new Brainy()
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await brain.init()
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```
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### Production
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```dockerfile
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# ✅ Pre-download models
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RUN npm run download-models
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# ✅ Force local-only
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ENV BRAINY_ALLOW_REMOTE_MODELS=false
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# ✅ Verify models exist
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RUN test -f ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
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```
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### CI/CD Pipeline
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```yaml
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# .github/workflows/build.yml
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- name: Download AI Models
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run: npm run download-models
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- name: Verify Models
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run: |
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test -f ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
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echo "✅ Models verified"
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- name: Test Offline Mode
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env:
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BRAINY_ALLOW_REMOTE_MODELS: false
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run: npm test
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```
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### Lambda/Serverless
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```typescript
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// ✅ Models in deployment package
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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)
|
||||
|
|
|
|||
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