diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 00000000..813d3021
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,206 @@
+name: CI
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ branches: [main]
+
+env:
+ NODE_VERSION: '20'
+ RUST_VERSION: 'stable'
+
+jobs:
+ # Build and test TypeScript/JavaScript
+ test-node:
+ name: Test (Node.js)
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Setup Node.js
+ uses: actions/setup-node@v4
+ with:
+ node-version: ${{ env.NODE_VERSION }}
+ cache: 'npm'
+
+ - name: Install dependencies
+ run: npm ci
+
+ - name: Build TypeScript
+ run: npm run build
+
+ - name: Run unit tests
+ run: npm run test:unit
+
+ - name: Run integration tests
+ run: npm run test:integration
+
+ # Build and test with Bun
+ test-bun:
+ name: Test (Bun)
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Setup Bun
+ uses: oven-sh/setup-bun@v2
+ with:
+ bun-version: latest
+
+ - name: Setup Node.js (for npm compatibility)
+ uses: actions/setup-node@v4
+ with:
+ node-version: ${{ env.NODE_VERSION }}
+ cache: 'npm'
+
+ - name: Install dependencies
+ run: npm ci
+
+ - name: Build TypeScript
+ run: npm run build
+
+ - name: Run Bun tests
+ run: npm run test:bun
+
+ # Build Candle WASM (if Rust source changed)
+ build-candle-wasm:
+ name: Build Candle WASM
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Setup Rust
+ uses: dtolnay/rust-action@stable
+ with:
+ toolchain: ${{ env.RUST_VERSION }}
+ targets: wasm32-unknown-unknown
+
+ - name: Install wasm-pack
+ run: cargo install wasm-pack
+
+ - name: Cache Cargo registry
+ uses: actions/cache@v4
+ with:
+ path: |
+ ~/.cargo/registry
+ ~/.cargo/git
+ src/embeddings/candle-wasm/target
+ key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+
+ - name: Build WASM
+ run: |
+ cd src/embeddings/candle-wasm
+ wasm-pack build --target web --release
+
+ - name: Upload WASM artifacts
+ uses: actions/upload-artifact@v4
+ with:
+ name: candle-wasm
+ path: src/embeddings/wasm/pkg/
+ retention-days: 7
+
+ # Test Bun compile (standalone binary)
+ test-bun-compile:
+ name: Test Bun Compile
+ runs-on: ubuntu-latest
+ needs: [build-candle-wasm]
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Setup Bun
+ uses: oven-sh/setup-bun@v2
+ with:
+ bun-version: latest
+
+ - name: Setup Node.js
+ uses: actions/setup-node@v4
+ with:
+ node-version: ${{ env.NODE_VERSION }}
+ cache: 'npm'
+
+ - name: Download WASM artifacts
+ uses: actions/download-artifact@v4
+ with:
+ name: candle-wasm
+ path: src/embeddings/wasm/pkg/
+
+ - name: Install dependencies
+ run: npm ci
+
+ - name: Build TypeScript
+ run: npm run build
+
+ - name: Test Bun compile
+ run: |
+ # Create a test script
+ cat > /tmp/test-compile.ts << 'EOF'
+ import { Brainy } from './dist/index.js'
+
+ const brainy = new Brainy()
+ await brainy.init()
+ console.log('Brainy initialized!')
+
+ const embedding = await brainy.embed('Hello world')
+ console.log(`Embedding dimension: ${embedding.length}`)
+
+ if (embedding.length !== 384) {
+ throw new Error('Expected 384-dimensional embedding')
+ }
+
+ console.log('Test passed!')
+ EOF
+
+ # Compile to standalone binary
+ bun build --compile /tmp/test-compile.ts --outfile /tmp/brainy-test
+
+ # Run the compiled binary
+ /tmp/brainy-test
+
+ # Lint and type check
+ lint:
+ name: Lint & Type Check
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Setup Node.js
+ uses: actions/setup-node@v4
+ with:
+ node-version: ${{ env.NODE_VERSION }}
+ cache: 'npm'
+
+ - name: Install dependencies
+ run: npm ci
+
+ - name: Type check
+ run: npm run typecheck
+
+ - name: Lint
+ run: npm run lint
+
+ # License check
+ license-check:
+ name: License Check
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Setup Node.js
+ uses: actions/setup-node@v4
+ with:
+ node-version: ${{ env.NODE_VERSION }}
+ cache: 'npm'
+
+ - name: Install dependencies
+ run: npm ci
+
+ - name: Check licenses
+ run: |
+ # Install license checker
+ npm install -g license-checker
+
+ # Check for problematic licenses
+ license-checker --production --excludePrivatePackages \
+ --failOn 'GPL;LGPL;AGPL;SSPL' \
+ --summary
diff --git a/.gitignore b/.gitignore
index 2b354e6e..d0a27628 100644
--- a/.gitignore
+++ b/.gitignore
@@ -90,6 +90,16 @@ docs/internal/
# Cache files
*.cache
+# Rust/Cargo build artifacts
+src/embeddings/candle-wasm/target/
+src/embeddings/candle-wasm/Cargo.lock
+src/embeddings/wasm/pkg/
+
+# But keep the pre-built WASM (committed for users without Rust)
+!src/embeddings/wasm/pkg/*.wasm
+!src/embeddings/wasm/pkg/*.js
+!src/embeddings/wasm/pkg/*.d.ts
+
# Log files (redundant but explicit)
*.log
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 7b7e70ad..ab8a0246 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -43,15 +43,38 @@ Feature requests are welcome! Please provide:
#### Development Setup
+**Quick Setup (Recommended):**
```bash
# Clone your fork
git clone https://github.com/your-username/brainy.git
cd brainy
-# Install dependencies
+# Run setup script (installs all dependencies including Rust)
+./scripts/setup-dev.sh
+```
+
+**Manual Setup:**
+```bash
+# Clone your fork
+git clone https://github.com/your-username/brainy.git
+cd brainy
+
+# Install system dependencies (Ubuntu/Debian)
+sudo apt-get install -y build-essential pkg-config libssl-dev
+
+# Install Rust (for WASM embedding engine)
+curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
+source ~/.cargo/env
+rustup target add wasm32-unknown-unknown
+cargo install wasm-pack
+
+# Install Node.js dependencies
npm install
-# Build the project
+# Build Candle WASM embedding engine
+npm run build:candle
+
+# Build TypeScript
npm run build
# Run tests
diff --git a/README.md b/README.md
index 6f8bad70..e0f9e855 100644
--- a/README.md
+++ b/README.md
@@ -197,7 +197,7 @@ const brain = new Brainy({
})
```
**Scale:** Thousands to hundreds of thousands • **Performance:** <5ms queries
-**→ [Production Service Architecture](docs/PRODUCTION_SERVICE_ARCHITECTURE.md)** — Singleton patterns, caching, and scaling for Express/Node.js services
+**→ [Production Service Architecture](docs/PRODUCTION_SERVICE_ARCHITECTURE.md)** — Singleton patterns, caching, and scaling for Bun/Node.js services
### 🏢 Growing Company → Multi-Million Scale
```javascript
@@ -685,12 +685,18 @@ This comprehensive guide includes:
## Requirements
-**Node.js 22 LTS** (recommended) or **Node.js 20 LTS**
+**Bun 1.0+** (recommended) or **Node.js 22 LTS**
```bash
-nvm use # We provide .nvmrc
+# Bun (recommended - best performance, single-binary deployment)
+bun install @soulcraft/brainy
+
+# Node.js (fully supported)
+npm install @soulcraft/brainy
```
+> **Why Bun?** Brainy's Candle WASM engine works seamlessly with `bun --compile` for standalone binary deployment. No external model files, no runtime downloads.
+
---
## Why Brainy Exists
diff --git a/assets/models/all-MiniLM-L6-v2-q8/model.onnx b/assets/models/all-MiniLM-L6-v2-q8/model.onnx
deleted file mode 100644
index 712e070a..00000000
Binary files a/assets/models/all-MiniLM-L6-v2-q8/model.onnx and /dev/null differ
diff --git a/assets/models/all-MiniLM-L6-v2-q8/tokenizer.json b/assets/models/all-MiniLM-L6-v2-q8/tokenizer.json
deleted file mode 100644
index c17ed520..00000000
--- a/assets/models/all-MiniLM-L6-v2-q8/tokenizer.json
+++ /dev/null
@@ -1,30686 +0,0 @@
-{
- "version": "1.0",
- "truncation": {
- "direction": "Right",
- "max_length": 128,
- "strategy": "LongestFirst",
- "stride": 0
- },
- "padding": {
- "strategy": {
- "Fixed": 128
- },
- "direction": "Right",
- "pad_to_multiple_of": null,
- "pad_id": 0,
- "pad_type_id": 0,
- "pad_token": "[PAD]"
- },
- "added_tokens": [
- {
- "id": 0,
- "content": "[PAD]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 100,
- "content": "[UNK]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 101,
- "content": "[CLS]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 102,
- "content": "[SEP]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 103,
- "content": "[MASK]",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- }
- ],
- "normalizer": {
- "type": "BertNormalizer",
- "clean_text": true,
- "handle_chinese_chars": true,
- "strip_accents": null,
- "lowercase": true
- },
- "pre_tokenizer": {
- "type": "BertPreTokenizer"
- },
- "post_processor": {
- "type": "TemplateProcessing",
- "single": [
- {
- "SpecialToken": {
- "id": "[CLS]",
- "type_id": 0
- }
- },
- {
- "Sequence": {
- "id": "A",
- "type_id": 0
- }
- },
- {
- "SpecialToken": {
- "id": "[SEP]",
- "type_id": 0
- }
- }
- ],
- "pair": [
- {
- "SpecialToken": {
- "id": "[CLS]",
- "type_id": 0
- }
- },
- {
- "Sequence": {
- "id": "A",
- "type_id": 0
- }
- },
- {
- "SpecialToken": {
- "id": "[SEP]",
- "type_id": 0
- }
- },
- {
- "Sequence": {
- "id": "B",
- "type_id": 1
- }
- },
- {
- "SpecialToken": {
- "id": "[SEP]",
- "type_id": 1
- }
- }
- ],
- "special_tokens": {
- "[CLS]": {
- "id": "[CLS]",
- "ids": [
- 101
- ],
- "tokens": [
- "[CLS]"
- ]
- },
- "[SEP]": {
- "id": "[SEP]",
- "ids": [
- 102
- ],
- "tokens": [
- "[SEP]"
- ]
- }
- }
- },
- "decoder": {
- "type": "WordPiece",
- "prefix": "##",
- "cleanup": true
- },
- "model": {
- "type": "WordPiece",
- "unk_token": "[UNK]",
- "continuing_subword_prefix": "##",
- "max_input_chars_per_word": 100,
- "vocab": {
- "[PAD]": 0,
- "[unused0]": 1,
- "[unused1]": 2,
- "[unused2]": 3,
- "[unused3]": 4,
- "[unused4]": 5,
- "[unused5]": 6,
- "[unused6]": 7,
- "[unused7]": 8,
- "[unused8]": 9,
- "[unused9]": 10,
- "[unused10]": 11,
- "[unused11]": 12,
- "[unused12]": 13,
- "[unused13]": 14,
- "[unused14]": 15,
- "[unused15]": 16,
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- "[unused17]": 18,
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+ "@grpc/proto-loader/yargs/string-width/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
+
+ "@inquirer/core/wrap-ansi/ansi-styles/color-convert/color-name": ["color-name@1.1.4", "", {}, "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA=="],
+
+ "cliui/wrap-ansi/ansi-styles/color-convert/color-name": ["color-name@1.1.4", "", {}, "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA=="],
+
+ "dotgitignore/find-up/locate-path/p-locate/p-limit": ["p-limit@2.3.0", "", { "dependencies": { "p-try": "^2.0.0" } }, "sha512-//88mFWSJx8lxCzwdAABTJL2MyWB12+eIY7MDL2SqLmAkeKU9qxRvWuSyTjm3FUmpBEMuFfckAIqEaVGUDxb6w=="],
+
+ "eslint/chalk/ansi-styles/color-convert/color-name": ["color-name@1.1.4", "", {}, "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA=="],
+
+ "meow/read-pkg-up/find-up/locate-path/p-locate": ["p-locate@4.1.0", "", { "dependencies": { "p-limit": "^2.2.0" } }, "sha512-R79ZZ/0wAxKGu3oYMlz8jy/kbhsNrS7SKZ7PxEHBgJ5+F2mtFW2fK2cOtBh1cHYkQsbzFV7I+EoRKe6Yt0oK7A=="],
+
+ "meow/read-pkg-up/read-pkg/normalize-package-data/hosted-git-info": ["hosted-git-info@2.8.9", "", {}, "sha512-mxIDAb9Lsm6DoOJ7xH+5+X4y1LU/4Hi50L9C5sIswK3JzULS4bwk1FvjdBgvYR4bzT4tuUQiC15FE2f5HbLvYw=="],
+
+ "meow/read-pkg-up/read-pkg/normalize-package-data/semver": ["semver@5.7.2", "", { "bin": "bin/semver" }, "sha512-cBznnQ9KjJqU67B52RMC65CMarK2600WFnbkcaiwWq3xy/5haFJlshgnpjovMVJ+Hff49d8GEn0b87C5pDQ10g=="],
+
+ "read-pkg-up/find-up/locate-path/p-locate/p-limit": ["p-limit@1.3.0", "", { "dependencies": { "p-try": "^1.0.0" } }, "sha512-vvcXsLAJ9Dr5rQOPk7toZQZJApBl2K4J6dANSsEuh6QI41JYcsS/qhTGa9ErIUUgK3WNQoJYvylxvjqmiqEA9Q=="],
+
+ "@grpc/grpc-js/@grpc/proto-loader/yargs/cliui/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
+
+ "@grpc/grpc-js/@grpc/proto-loader/yargs/cliui/wrap-ansi/ansi-styles": ["ansi-styles@4.3.0", "", { "dependencies": { "color-convert": "^2.0.1" } }, "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg=="],
+
+ "@grpc/grpc-js/@grpc/proto-loader/yargs/string-width/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
+
+ "@grpc/proto-loader/yargs/cliui/wrap-ansi/ansi-styles/color-convert": ["color-convert@2.0.1", "", { "dependencies": { "color-name": "~1.1.4" } }, "sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ=="],
+
+ "meow/read-pkg-up/find-up/locate-path/p-locate/p-limit": ["p-limit@2.3.0", "", { "dependencies": { "p-try": "^2.0.0" } }, "sha512-//88mFWSJx8lxCzwdAABTJL2MyWB12+eIY7MDL2SqLmAkeKU9qxRvWuSyTjm3FUmpBEMuFfckAIqEaVGUDxb6w=="],
+
+ "read-pkg-up/find-up/locate-path/p-locate/p-limit/p-try": ["p-try@1.0.0", "", {}, "sha512-U1etNYuMJoIz3ZXSrrySFjsXQTWOx2/jdi86L+2pRvph/qMKL6sbcCYdH23fqsbm8TH2Gn0OybpT4eSFlCVHww=="],
+
+ "@grpc/grpc-js/@grpc/proto-loader/yargs/cliui/wrap-ansi/ansi-styles/color-convert": ["color-convert@2.0.1", "", { "dependencies": { "color-name": "~1.1.4" } }, "sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ=="],
+
+ "@grpc/proto-loader/yargs/cliui/wrap-ansi/ansi-styles/color-convert/color-name": ["color-name@1.1.4", "", {}, "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA=="],
+
+ "@grpc/grpc-js/@grpc/proto-loader/yargs/cliui/wrap-ansi/ansi-styles/color-convert/color-name": ["color-name@1.1.4", "", {}, "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA=="],
+ }
+}
diff --git a/docs/MODEL_LOADING_QUICK_REFERENCE.md b/docs/MODEL_LOADING_QUICK_REFERENCE.md
deleted file mode 100644
index f94e2723..00000000
--- a/docs/MODEL_LOADING_QUICK_REFERENCE.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# 🤖 Model Loading Quick Reference
-
-## 🚀 Common Scenarios
-
-### ✅ Development (Zero Config)
-```typescript
-const brain = new Brainy()
-await brain.init() // Downloads automatically (FP32 default)
-```
-
-### ⚡ Development (Optimized - v2.8.0+)
-```typescript
-// 75% smaller models, 99% accuracy
-const brain = new Brainy({
- embeddingOptions: { dtype: 'q8' }
-})
-await brain.init()
-```
-
-### 🐳 Docker Production
-```dockerfile
-# Both models (recommended)
-RUN npm run download-models
-
-# Or FP32 only (compatibility)
-RUN npm run download-models:fp32
-
-# Or Q8 only (space-constrained)
-RUN npm run download-models:q8
-
-ENV BRAINY_ALLOW_REMOTE_MODELS=false
-```
-
-### ☁️ Serverless/Lambda
-```bash
-# Build step
-npm run download-models
-
-# Runtime
-export BRAINY_ALLOW_REMOTE_MODELS=false
-```
-
-### 🔒 Air-Gapped/Offline
-```bash
-# Connected machine
-npm run download-models
-tar -czf brainy-models.tar.gz ./models
-
-# Offline machine
-tar -xzf brainy-models.tar.gz
-export BRAINY_ALLOW_REMOTE_MODELS=false
-```
-
-### 🌐 Browser/CDN
-```html
-
-
-```
-
-## 🚨 Troubleshooting
-
-| Error | Solution |
-|-------|----------|
-| "Failed to load embedding model" | `npm run download-models` |
-| "ENOENT: no such file" | Check `BRAINY_MODELS_PATH` |
-| "Network timeout" | Set `BRAINY_ALLOW_REMOTE_MODELS=false` |
-| "Permission denied" | `chmod 755 ./models` |
-| "Out of memory" | Increase container memory limit |
-
-## 🎯 Environment Variables
-
-| Variable | Values | Purpose |
-|----------|--------|---------|
-| `BRAINY_ALLOW_REMOTE_MODELS` | `true`/`false` | Allow/block downloads |
-| `BRAINY_MODELS_PATH` | `./models` | Model storage path |
-| `BRAINY_Q8_CONFIRMED` | `true`/`false` | Silence Q8 compatibility warnings |
-| `NODE_ENV` | `production` | Environment detection |
-
-## 📦 Model Info
-
-### FP32 (Default)
-- **Model**: All-MiniLM-L6-v2
-- **Dimensions**: 384 (fixed)
-- **Size**: 90MB
-- **Accuracy**: 100% (baseline)
-- **Location**: `./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx`
-
-### Q8 (Optional - v2.8.0+)
-- **Model**: All-MiniLM-L6-v2 (quantized)
-- **Dimensions**: 384 (same)
-- **Size**: 23MB (75% smaller!)
-- **Accuracy**: ~99% (minimal loss)
-- **Location**: `./models/Xenova/all-MiniLM-L6-v2/onnx/model_quantized.onnx`
-
-**⚠️ Important**: FP32 and Q8 create different embeddings and are incompatible!
-
-## ✅ Verification Commands
-
-```bash
-# Check FP32 model exists
-ls ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
-
-# Check Q8 model exists
-ls ./models/Xenova/all-MiniLM-L6-v2/onnx/model_quantized.onnx
-
-# Test offline mode
-BRAINY_ALLOW_REMOTE_MODELS=false npm test
-
-# Download fresh models (both)
-rm -rf ./models && npm run download-models
-
-# Download specific model variant
-rm -rf ./models && npm run download-models:q8
-```
\ No newline at end of file
diff --git a/docs/PRODUCTION_SERVICE_ARCHITECTURE.md b/docs/PRODUCTION_SERVICE_ARCHITECTURE.md
index 1f3f9e9a..a7e088f4 100644
--- a/docs/PRODUCTION_SERVICE_ARCHITECTURE.md
+++ b/docs/PRODUCTION_SERVICE_ARCHITECTURE.md
@@ -1,6 +1,8 @@
# Production Service Architecture Guide
-**How to use Brainy optimally in production Express/Node.js services**
+**How to use Brainy optimally in production services (Bun, Node.js, Deno)**
+
+> **Recommended Runtime:** [Bun](https://bun.sh) provides best performance with Brainy's Candle WASM engine. All examples work with both Bun and Node.js.
---
@@ -168,7 +170,57 @@ process.on('SIGTERM', async () => {
---
-### Pattern 3: Express Middleware
+### Pattern 3: Bun Server (Recommended)
+
+```typescript
+// server.ts - Clean Bun implementation
+import { Brainy } from '@soulcraft/brainy'
+
+let brain: Brainy | null = null
+
+async function getBrain(): Promise {
+ if (!brain) {
+ brain = new Brainy({ storage: { path: './brainy-data' } })
+ await brain.init()
+ }
+ return brain
+}
+
+// Initialize before server starts
+await getBrain()
+
+Bun.serve({
+ port: 3000,
+ async fetch(req) {
+ const url = new URL(req.url)
+
+ if (url.pathname === '/api/entities') {
+ const b = await getBrain()
+ const entities = await b.find({})
+ return Response.json(entities)
+ }
+
+ if (url.pathname === '/api/entity' && req.method === 'POST') {
+ const b = await getBrain()
+ const body = await req.json()
+ const id = await b.add(body)
+ return Response.json({ id })
+ }
+
+ return new Response('Not Found', { status: 404 })
+ }
+})
+
+console.log('Server running on http://localhost:3000')
+```
+
+**Benefits:**
+- ✅ Native Bun performance (~2x faster than Node.js)
+- ✅ No framework dependencies
+- ✅ Works with `bun --compile` for single-binary deployment
+- ✅ Built-in TypeScript support
+
+### Pattern 4: Express/Node.js Middleware (Legacy)
```typescript
// middleware/brainy.ts
diff --git a/docs/api/README.md b/docs/api/README.md
index 625ea5e6..cb378f41 100644
--- a/docs/api/README.md
+++ b/docs/api/README.md
@@ -1373,12 +1373,9 @@ const brain = new Brainy({
typeAware: true // Enable type-aware indexing (v4.0+)
},
- // Model configuration
- model: {
- type: 'transformers', // transformers | custom
- name: 'Xenova/all-MiniLM-L6-v2',
- device: 'auto' // auto | cpu | gpu
- },
+ // Model configuration (embedded in WASM - zero config needed)
+ // Model: all-MiniLM-L6-v2 (384 dimensions)
+ // Device: CPU via WASM (works everywhere)
// Cache configuration
cache: {
diff --git a/docs/architecture/augmentations-actual.md b/docs/architecture/augmentations-actual.md
index d8e42dbe..808c37aa 100644
--- a/docs/architecture/augmentations-actual.md
+++ b/docs/architecture/augmentations-actual.md
@@ -206,7 +206,7 @@ if (device === 'webgpu') {
// CUDA detection in Node:
if (device === 'cuda') {
- // Requires ONNX Runtime GPU packages
+ // Future: GPU acceleration support
}
```
diff --git a/docs/architecture/finite-type-system.md b/docs/architecture/finite-type-system.md
index abf96b0c..51ba515c 100644
--- a/docs/architecture/finite-type-system.md
+++ b/docs/architecture/finite-type-system.md
@@ -80,7 +80,7 @@ const entity = {
- ✅ **Semantic Understanding**: Types have meaning, not just structure
- ✅ **Tool Compatibility**: All augmentations understand core types
- ✅ **Concept Extraction**: NLP can map text to known types
-- ✅ **Type Inference**: Automatic type detection via keywords/synonyms
+- ✅ **Explicit Types**: Clear type specification in API
- ✅ **Query Optimization**: Type-aware query planning
- ✅ **Flexible Metadata**: Any fields within typed structure
- ✅ **Billion-Scale Ready**: Type tracking scales linearly
@@ -121,45 +121,51 @@ class TypeAwareMetadataIndex {
- **After**: PROJECTED 1.2MB memory for same dataset (385x reduction - calculated from Uint32Array size, not measured)
- **Scales to billions**: Memory grows with entity count, not key diversity
-### 2. Semantic Type Inference
+### 2. Explicit Type System
-**The Magic**: Map natural language to structured types:
+**The Design**: Specify types clearly in your API calls:
```typescript
-import { getSemanticTypeInference } from '@soulcraft/brainy'
+import { Brainy, NounType, VerbType } from '@soulcraft/brainy'
-const inference = getSemanticTypeInference()
+// Add entity with explicit type
+await brain.add({
+ data: { name: 'Alice', role: 'CEO of Acme Corp' },
+ type: NounType.Person // Explicit type specification
+})
-// Automatic type detection
-await inference.inferNounType('CEO of Acme Corp')
-// → 'person'
-
-await inference.inferNounType('San Francisco office building')
-// → 'place'
-
-await inference.inferVerbType('Alice manages Bob')
-// → 'manages' (relationship type)
+// Query with type filtering
+await brain.find({
+ query: 'Alice',
+ type: NounType.Person // Type-optimized search
+})
```
-**How It Works**:
-1. **Keyword Matching**: "CEO", "manager" → 'person'
-2. **Synonym Detection**: "building", "office" → 'place'
-3. **Semantic Embeddings**: Vector similarity to type prototypes
-4. **Context Analysis**: Surrounding words provide hints
+**Why Explicit Types?**:
+1. **Deterministic**: You control exactly how entities are classified
+2. **Predictable**: No inference surprises or edge cases
+3. **Fast**: No neural processing overhead on every add/query
+4. **Smaller**: No embedded keyword models needed
**Real-World Use Case**:
```typescript
-// Import unstructured data
-const text = "Apple announced a new product line in Cupertino"
+// Import data with known types
+await brain.add({
+ data: { name: 'Apple Inc.', industry: 'Technology' },
+ type: NounType.Organization
+})
-// Brainy automatically infers:
-// - "Apple" → noun type: 'organization'
-// - "product line" → noun type: 'product'
-// - "Cupertino" → noun type: 'place'
-// - "announced" → verb type: 'announces'
-// - "in" → verb type: 'locatedIn'
+await brain.add({
+ data: { name: 'Cupertino', country: 'USA' },
+ type: NounType.Location
+})
-// Creates typed, queryable knowledge graph automatically!
+// Create relationship
+await brain.relate({
+ from: appleId,
+ to: cupertinoId,
+ type: VerbType.LocatedIn
+})
```
### 3. Tool & Augmentation Compatibility
@@ -364,42 +370,47 @@ function processNoun(noun: Noun) {
---
-## Public API: Semantic Type Inference
+## Public API: Type System
-The type inference system is **fully public** for augmentation developers and external tools:
+The type system is **fully public** for developers and augmentation authors:
```typescript
import {
- getSemanticTypeInference,
- SemanticTypeInference
+ NounType,
+ VerbType,
+ getNounTypes,
+ getVerbTypes,
+ BrainyTypes,
+ suggestType
} from '@soulcraft/brainy'
-// Get singleton instance
-const inference = getSemanticTypeInference()
+// Get all available noun types
+const nounTypes = getNounTypes()
+// → ['Person', 'Organization', 'Location', 'Thing', 'Concept', ...]
-// Infer noun type from text
-const nounType = await inference.inferNounType('Software Engineer')
-// → 'person'
+// Get all available verb types
+const verbTypes = getVerbTypes()
+// → ['RelatedTo', 'Contains', 'CreatedBy', 'LocatedIn', ...]
-// Infer verb type from relationship text
-const verbType = await inference.inferVerbType('works at')
-// → 'worksAt'
+// Use types directly
+await brain.add({
+ data: { name: 'Alice' },
+ type: NounType.Person
+})
-// Get type keywords for reverse lookup
-const keywords = inference.getNounTypeKeywords('person')
-// → ['person', 'human', 'individual', 'user', 'employee', ...]
-
-// Get type synonyms
-const synonyms = inference.getNounTypeSynonyms('organization')
-// → ['company', 'corporation', 'business', 'firm', 'enterprise', ...]
+// Query by type
+await brain.find({
+ type: NounType.Person,
+ where: { name: 'Alice' }
+})
```
**Use Cases**:
-- **Import Tools**: Auto-detect entity types during data import
-- **Query Builders**: Suggest types based on user input
+- **Type-Safe Code**: Use TypeScript enums for compile-time checking
+- **Import Tools**: Specify entity types during data import
+- **Query Builders**: Filter by known types
- **Augmentations**: Type-specific processing pipelines
- **Visualization**: Type-appropriate rendering
-- **Data Validation**: Ensure correct type assignments
---
@@ -415,7 +426,7 @@ const synonyms = inference.getNounTypeSynonyms('organization')
| **Query Planning** | Impossible | Table statistics | Type statistics |
| **Tool Compatibility** | None | SQL only | Full ecosystem |
| **Semantic Understanding** | None | None | Built-in |
-| **Concept Extraction** | Manual | Manual | Automatic |
+| **Concept Extraction** | Manual | Manual | Via SmartExtractor |
| **Flexibility** | Infinite | Zero | Optimal balance |
---
@@ -483,7 +494,7 @@ Brainy's **Finite Noun/Verb Type System** is revolutionary because it achieves t
2. ✅ **Semantic understanding** (NLP integration)
3. ✅ **Tool compatibility** (ecosystem interoperability)
4. ✅ **Query optimization** (type-aware planning)
-5. ✅ **Concept extraction** (automatic type inference)
+5. ✅ **Concept extraction** (via SmartExtractor for imports)
6. ✅ **Developer experience** (clean architecture)
7. ✅ **Flexibility** (metadata freedom within types)
@@ -493,11 +504,10 @@ It's not schemaless chaos. It's not rigid relational constraints. It's **semanti
## Further Reading
-- [Type Inference System](../api/type-inference.md) - API reference for semantic type detection
- [Storage Architecture](./storage-architecture.md) - How types enable billion-scale storage
- [Augmentation System](./augmentations.md) - Building type-aware augmentations
-- [Concept Extraction](../guides/natural-language.md) - NLP integration with typed entities
- [Query Optimization](../api/query-optimization.md) - Type-aware query planning
+- [Import Flow](../guides/import-flow.md) - How types work in the import pipeline
---
diff --git a/docs/augmentations/api-server.md b/docs/augmentations/api-server.md
index 9546a41e..aa7973ca 100644
--- a/docs/augmentations/api-server.md
+++ b/docs/augmentations/api-server.md
@@ -25,7 +25,9 @@ Built-in authentication and rate limiting when needed.
The APIServerAugmentation is included in Brainy core. No additional installation required.
-For Node.js environments, you may want to install optional dependencies:
+**Bun** (recommended): No additional dependencies needed - use `Bun.serve()` directly.
+
+**Node.js**: Install optional Express dependencies:
```bash
npm install express cors ws
```
@@ -259,6 +261,30 @@ Authorization: Bearer your-token
## Environment Support
+### Bun ✅ (Recommended)
+Native performance with Bun.serve() - no Express required. Works with `bun --compile` for single-binary deployment.
+
+```typescript
+// Simple Bun server with Brainy
+import { Brainy } from '@soulcraft/brainy'
+
+const brain = new Brainy()
+await brain.init()
+
+Bun.serve({
+ port: 3000,
+ async fetch(req) {
+ const url = new URL(req.url)
+ if (url.pathname === '/api/search') {
+ const { query } = await req.json()
+ const results = await brain.search(query)
+ return Response.json(results)
+ }
+ return new Response('Not Found', { status: 404 })
+ }
+})
+```
+
### Node.js ✅
Full support with Express, WebSocket, and all features.
diff --git a/docs/features/complete-feature-list.md b/docs/features/complete-feature-list.md
index df02a3f7..b936ad71 100644
--- a/docs/features/complete-feature-list.md
+++ b/docs/features/complete-feature-list.md
@@ -256,9 +256,9 @@ if (device === 'webgpu') {
// Transformer models use WebGPU automatically
}
-// CUDA in Node.js (requires ONNX Runtime GPU)
+// CUDA in Node.js (future GPU support)
if (device === 'cuda') {
- // Automatically uses GPU for embeddings
+ // Future: GPU acceleration for embeddings
}
```
diff --git a/docs/guides/import-flow.md b/docs/guides/import-flow.md
index 77d11b36..de6c3e30 100644
--- a/docs/guides/import-flow.md
+++ b/docs/guides/import-flow.md
@@ -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 │
└───────────────────────────────────────────────┘
diff --git a/docs/guides/model-loading.md b/docs/guides/model-loading.md
index 020c420e..415fe2e3 100644
--- a/docs/guides/model-loading.md
+++ b/docs/guides/model-loading.md
@@ -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).
\ No newline at end of file
+**Need help?** [Open an issue](https://github.com/soulcraftlabs/brainy/issues)
diff --git a/docs/operations/capacity-planning.md b/docs/operations/capacity-planning.md
index 2f69eb53..3105c87b 100644
--- a/docs/operations/capacity-planning.md
+++ b/docs/operations/capacity-planning.md
@@ -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
diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md
index 1e2e6e12..5fb5ce68 100644
--- a/docs/troubleshooting.md
+++ b/docs/troubleshooting.md
@@ -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
diff --git a/examples/debug-vector-similarity.js b/examples/debug-vector-similarity.js
deleted file mode 100644
index ff9514cd..00000000
--- a/examples/debug-vector-similarity.js
+++ /dev/null
@@ -1,61 +0,0 @@
-/**
- * Debug script to analyze vector similarity scores for threshold tuning
- */
-
-import { TypeInferenceSystem } from '../dist/query/typeInference.js';
-
-async function debugVectorSimilarity() {
- console.log('🔍 Vector Similarity Threshold Analysis\n');
- console.log('='.repeat(60));
-
- // Create hybrid system with debug enabled
- const system = new TypeInferenceSystem({
- enableVectorFallback: true,
- fallbackConfidenceThreshold: 0.7,
- vectorThreshold: 0.3, // Lower threshold to see more matches
- debug: true
- });
-
- // Test cases: unknown words, typos, medical terms
- const testQueries = [
- 'Find documnets', // Typo: document
- 'Find cardiologists', // Medical: person
- 'Find oncologists', // Medical: person
- 'Find pysicians', // Typo: physician -> person
- 'Find organiztions', // Typo: organization
- 'Find kompanies', // Severe typo: companies -> organization
- 'Find xyzabc', // Completely unknown
- 'neurologist', // Medical single word
- 'cardiologist' // Medical single word
- ];
-
- console.log('\n📊 Testing vector similarity with threshold = 0.3\n');
-
- for (const query of testQueries) {
- console.log(`\nQuery: "${query}"`);
- const start = performance.now();
-
- const results = await system.inferTypesAsync(query);
-
- const elapsed = performance.now() - start;
-
- if (results.length > 0) {
- console.log(` ✅ Matched ${results.length} types in ${elapsed.toFixed(2)}ms:`);
- for (const result of results.slice(0, 3)) {
- console.log(` - ${result.type}: ${(result.confidence * 100).toFixed(1)}% (${result.matchedKeywords.join(', ')})`);
- }
- } else {
- console.log(` ❌ No matches in ${elapsed.toFixed(2)}ms`);
- }
- }
-
- console.log('\n' + '='.repeat(60));
- console.log('✅ Analysis complete! Use these insights to tune thresholds.');
- console.log('='.repeat(60));
-}
-
-debugVectorSimilarity().catch(err => {
- console.error('❌ Error:', err.message);
- console.error(err.stack);
- process.exit(1);
-});
diff --git a/examples/hybrid-type-inference-demo.js b/examples/hybrid-type-inference-demo.js
deleted file mode 100644
index 9bbae00a..00000000
--- a/examples/hybrid-type-inference-demo.js
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * Hybrid Type Inference Demo - REAL WORKING EXAMPLE
- *
- * Demonstrates the hybrid TypeInference system with vector similarity fallback
- * actually working end-to-end through the TypeAwareQueryPlanner.
- */
-
-import { TypeAwareQueryPlanner } from '../dist/query/typeAwareQueryPlanner.js';
-import { NounType } from '../dist/types/graphTypes.js';
-
-async function main() {
- console.log('🎯 Hybrid Type Inference Demo\n');
- console.log('='.repeat(60));
-
- // ========== Test 1: Synchronous Mode (Keyword Only) ==========
- console.log('\n📌 Test 1: Synchronous Mode (Keyword Matching Only)\n');
-
- const syncPlanner = new TypeAwareQueryPlanner();
-
- console.log('Query: "Find engineers"');
- const plan1 = syncPlanner.planQuery('Find engineers');
- console.log(`✅ Result: ${plan1.routing} routing`);
- console.log(` Types: ${plan1.targetTypes.join(', ')}`);
- console.log(` Confidence: ${(plan1.confidence * 100).toFixed(0)}%`);
- console.log(` Speedup: ${plan1.estimatedSpeedup.toFixed(1)}x`);
-
- console.log('\nQuery: "Find physicians" (unknown word - will fail in sync mode)');
- const plan2 = syncPlanner.planQuery('Find physicians');
- console.log(`⚠️ Result: ${plan2.routing} routing`);
- console.log(` Types: ${plan2.targetTypes.length} types (searches ALL)`);
- console.log(` Reason: ${plan2.reasoning}`);
-
- // ========== Test 2: Hybrid Mode (Keyword + Vector Fallback) ==========
- console.log('\n\n📌 Test 2: Hybrid Mode (Keyword + Vector Fallback)\n');
-
- const hybridPlanner = new TypeAwareQueryPlanner(undefined, {
- enableVectorFallback: true,
- debug: true,
- typeInferenceConfig: {
- fallbackConfidenceThreshold: 0.7,
- vectorThreshold: 0.5,
- debug: true
- }
- });
-
- console.log('Query: "Find engineers" (known keyword - fast path)');
- const plan3 = await hybridPlanner.planQueryAsync('Find engineers');
- console.log(`✅ Result: ${plan3.routing} routing`);
- console.log(` Types: ${plan3.targetTypes.join(', ')}`);
- console.log(` Confidence: ${(plan3.confidence * 100).toFixed(0)}%`);
-
- console.log('\n\nQuery: "Find physicians" (unknown word - vector fallback!)');
- const plan4 = await hybridPlanner.planQueryAsync('Find physicians');
- console.log(`✅ Result: ${plan4.routing} routing`);
- console.log(` Types: ${plan4.targetTypes.join(', ')}`);
- console.log(` Confidence: ${(plan4.confidence * 100).toFixed(0)}%`);
- console.log(` Speedup: ${plan4.estimatedSpeedup.toFixed(1)}x`);
-
- console.log('\n\nQuery: "Find documnets" (typo - vector fallback handles it!)');
- const plan5 = await hybridPlanner.planQueryAsync('Find documnets');
- console.log(`✅ Result: ${plan5.routing} routing`);
- console.log(` Types: ${plan5.targetTypes.join(', ')}`);
- console.log(` Confidence: ${(plan5.confidence * 100).toFixed(0)}%`);
-
- // ========== Performance Comparison ==========
- console.log('\n\n📌 Performance Comparison\n');
-
- console.log('Synchronous (keyword-only):');
- const start1 = performance.now();
- syncPlanner.planQuery('Find engineers');
- const elapsed1 = performance.now() - start1;
- console.log(` Latency: ${elapsed1.toFixed(2)}ms (fast path)`);
-
- console.log('\nHybrid with known keyword (should use fast path):');
- const start2 = performance.now();
- await hybridPlanner.planQueryAsync('Find engineers');
- const elapsed2 = performance.now() - start2;
- console.log(` Latency: ${elapsed2.toFixed(2)}ms (fast path)`);
-
- console.log('\nHybrid with unknown word (triggers vector fallback):');
- const start3 = performance.now();
- await hybridPlanner.planQueryAsync('Find cardiologists');
- const elapsed3 = performance.now() - start3;
- console.log(` Latency: ${elapsed3.toFixed(2)}ms (includes vector similarity)`);
-
- console.log('\n' + '='.repeat(60));
- console.log('✅ Demo Complete! The hybrid system is ACTUALLY WORKING!');
- console.log('='.repeat(60));
-}
-
-// Run the demo
-main().catch(error => {
- console.error('\n❌ Demo failed:', error.message);
- console.error(error.stack);
- process.exit(1);
-});
diff --git a/examples/production-ready-demo.js b/examples/production-ready-demo.js
deleted file mode 100644
index 33d3b76c..00000000
--- a/examples/production-ready-demo.js
+++ /dev/null
@@ -1,107 +0,0 @@
-/**
- * Production-Ready Hybrid Type Inference - Comprehensive Demo
- *
- * Demonstrates all three inference modes:
- * 1. Fast path: Keyword matching (0.01-0.1ms)
- * 2. Fuzzy path: Edit distance matching for typos (0.1-0.5ms)
- * 3. Vector path: Semantic similarity fallback (50-150ms first call, 2-5ms cached)
- */
-
-import { TypeAwareQueryPlanner } from '../dist/query/typeAwareQueryPlanner.js';
-
-async function comprehensiveDemo() {
- console.log('🎯 Production-Ready Hybrid Type Inference Demo\n');
- console.log('='.repeat(60));
-
- // Initialize hybrid planner
- const planner = new TypeAwareQueryPlanner(undefined, {
- enableVectorFallback: true,
- debug: false,
- typeInferenceConfig: {
- fallbackConfidenceThreshold: 0.7,
- vectorThreshold: 0.3
- }
- });
-
- console.log('\n📌 Test Suite: All Three Inference Modes\n');
-
- // ========== Fast Path: Exact Keywords ==========
- console.log('1️⃣ FAST PATH - Exact keyword matches (0.01-0.1ms)');
- console.log('-'.repeat(60));
-
- const fastTests = [
- 'Find engineers in San Francisco',
- 'Show cardiologists',
- 'List oncologists and neurologists',
- 'Search documents about AI'
- ];
-
- for (const query of fastTests) {
- const start = performance.now();
- const plan = await planner.planQueryAsync(query);
- const elapsed = performance.now() - start;
-
- console.log(` "${query}"`);
- console.log(` → ${plan.routing}: [${plan.targetTypes.join(', ')}]`);
- console.log(` → Confidence: ${(plan.confidence * 100).toFixed(0)}%, Speedup: ${plan.estimatedSpeedup.toFixed(1)}x, Latency: ${elapsed.toFixed(2)}ms\n`);
- }
-
- // ========== Fuzzy Path: Typo Correction ==========
- console.log('\n2️⃣ FUZZY PATH - Typo correction via edit distance (0.1-0.5ms)');
- console.log('-'.repeat(60));
-
- const fuzzyTests = [
- 'Find pysicians', // physician (1 char substitution)
- 'Show organiztions', // organization (2 chars: missing 'a', extra 't')
- 'List enginners', // engineer (1 char: extra 'n')
- 'Search documnets' // documents (1 char: swapped 'n'/'m')
- ];
-
- for (const query of fuzzyTests) {
- const start = performance.now();
- const plan = await planner.planQueryAsync(query);
- const elapsed = performance.now() - start;
-
- console.log(` "${query}"`);
- console.log(` → ${plan.routing}: [${plan.targetTypes.join(', ')}]`);
- console.log(` → Confidence: ${(plan.confidence * 100).toFixed(0)}%, Speedup: ${plan.estimatedSpeedup.toFixed(1)}x, Latency: ${elapsed.toFixed(2)}ms\n`);
- }
-
- // ========== Vector Path: Semantic Fallback ==========
- console.log('\n3️⃣ VECTOR PATH - Semantic similarity fallback (2-150ms)');
- console.log('-'.repeat(60));
-
- const vectorTests = [
- 'Find cardiovascular specialists', // Should match via vector similarity
- 'Search publications', // Should match document
- 'List facilities' // Should match location/organization
- ];
-
- for (const query of vectorTests) {
- const start = performance.now();
- const plan = await planner.planQueryAsync(query);
- const elapsed = performance.now() - start;
-
- console.log(` "${query}"`);
- console.log(` → ${plan.routing}: [${plan.targetTypes.slice(0, 3).join(', ')}${plan.targetTypes.length > 3 ? '...' : ''}]`);
- console.log(` → Confidence: ${(plan.confidence * 100).toFixed(0)}%, Speedup: ${plan.estimatedSpeedup.toFixed(1)}x, Latency: ${elapsed.toFixed(2)}ms\n`);
- }
-
- // ========== Performance Summary ==========
- console.log('\n📊 Performance Summary');
- console.log('-'.repeat(60));
- console.log(' Fast Path (exact match): < 0.1ms ✅ 95% of queries');
- console.log(' Fuzzy Path (typo correction): 0.1-0.5ms ✅ 3-4% of queries');
- console.log(' Vector Path (semantic): 2-150ms ✅ 1-2% of queries');
- console.log(' Weighted Average Latency: ~0.5ms ✅ Production-ready!');
-
- console.log('\n' + '='.repeat(60));
- console.log('✅ All three inference modes working in production!');
- console.log('='.repeat(60));
-}
-
-comprehensiveDemo().catch(error => {
- console.error('\n❌ Demo failed:', error.message);
- console.error(error.stack);
- process.exit(1);
-});
diff --git a/examples/test-threshold-tuning.js b/examples/test-threshold-tuning.js
deleted file mode 100644
index 96716c34..00000000
--- a/examples/test-threshold-tuning.js
+++ /dev/null
@@ -1,77 +0,0 @@
-/**
- * Test different vector thresholds to find optimal balance
- */
-
-import { TypeInferenceSystem } from '../dist/query/typeInference.js';
-
-async function testThresholds() {
- console.log('🎯 Vector Threshold Tuning Test\n');
- console.log('='.repeat(60));
-
- // Test queries: typos and edge cases
- const testCases = [
- { query: 'Find pysicians', expected: 'person', description: 'Typo: physician' },
- { query: 'Find documnets', expected: 'document', description: 'Typo: documents' },
- { query: 'Find organiztions', expected: 'organization', description: 'Typo: organizations' },
- { query: 'Find kompanies', expected: 'organization', description: 'Severe typo: companies' },
- { query: 'Find enginners', expected: 'person', description: 'Typo: engineers' },
- { query: 'Find xyzabc', expected: null, description: 'Nonsense word (should fail)' }
- ];
-
- // Test with different thresholds
- const thresholds = [0.35, 0.30, 0.25, 0.20];
-
- for (const threshold of thresholds) {
- console.log(`\n📊 Testing with vectorThreshold = ${threshold}`);
- console.log('-'.repeat(60));
-
- const system = new TypeInferenceSystem({
- enableVectorFallback: true,
- fallbackConfidenceThreshold: 0.7,
- vectorThreshold: threshold,
- debug: false
- });
-
- let successes = 0;
- let falsePositives = 0;
-
- for (const testCase of testCases) {
- const results = await system.inferTypesAsync(testCase.query);
- const matched = results.length > 0;
- const correctType = results.length > 0 && results[0].type === testCase.expected;
-
- if (testCase.expected === null) {
- // Should NOT match
- if (!matched) {
- successes++;
- console.log(` ✅ "${testCase.query}" correctly returned no matches`);
- } else {
- falsePositives++;
- console.log(` ❌ "${testCase.query}" false positive: ${results[0].type} (${(results[0].confidence * 100).toFixed(1)}%)`);
- }
- } else {
- // Should match expected type
- if (correctType) {
- successes++;
- console.log(` ✅ "${testCase.query}" → ${results[0].type} (${(results[0].confidence * 100).toFixed(1)}%)`);
- } else if (matched) {
- console.log(` ⚠️ "${testCase.query}" → ${results[0].type} (expected ${testCase.expected})`);
- } else {
- console.log(` ❌ "${testCase.query}" no match (expected ${testCase.expected})`);
- }
- }
- }
-
- const accuracy = (successes / testCases.length) * 100;
- console.log(`\n Accuracy: ${successes}/${testCases.length} (${accuracy.toFixed(0)}%), False positives: ${falsePositives}`);
- }
-
- console.log('\n' + '='.repeat(60));
- console.log('✅ Threshold tuning complete!');
- console.log('='.repeat(60));
-}
-
-testThresholds().catch(err => {
- console.error('❌ Error:', err.message);
- process.exit(1);
-});
diff --git a/package.json b/package.json
index 97b2320d..e662c8b0 100644
--- a/package.json
+++ b/package.json
@@ -68,16 +68,16 @@
"bun": ">=1.0.0"
},
"scripts": {
- "build": "npm run build:types:if-needed && npm run build:patterns:if-needed && npm run build:keywords:if-needed && tsc && tsc -p tsconfig.cli.json",
+ "build": "npm run build:types:if-needed && npm run build:patterns:if-needed && tsc && tsc -p tsconfig.cli.json && npm run build:copy-wasm",
+ "build:copy-wasm": "node -e \"const fs=require('fs');const src='src/embeddings/wasm/pkg';const dst='dist/embeddings/wasm/pkg';if(fs.existsSync(src)){fs.mkdirSync(dst,{recursive:true});fs.readdirSync(src).forEach(f=>fs.copyFileSync(src+'/'+f,dst+'/'+f));console.log('Copied WASM pkg to dist')}\"",
"build:types": "tsx scripts/buildTypeEmbeddings.ts",
"build:types:if-needed": "node scripts/check-type-embeddings.cjs || npm run build:types",
"build:types:force": "npm run build:types",
"build:patterns": "tsx scripts/buildEmbeddedPatterns.ts",
"build:patterns:if-needed": "node scripts/check-patterns.cjs || npm run build:patterns",
"build:patterns:force": "npm run build:patterns",
- "build:keywords": "tsx scripts/buildKeywordEmbeddings.ts",
- "build:keywords:if-needed": "node scripts/check-keyword-embeddings.cjs || npm run build:keywords",
- "build:keywords:force": "npm run build:keywords",
+ "build:candle": "./scripts/build-candle-wasm.sh",
+ "build:candle:dev": "./scripts/build-candle-wasm.sh --dev",
"prepare": "npm run build",
"test": "npm run test:unit",
"test:watch": "NODE_OPTIONS='--max-old-space-size=8192' vitest --config tests/configs/vitest.unit.config.ts",
@@ -94,10 +94,7 @@
"test:bun": "bun tests/integration/bun-compile-test.ts",
"test:bun:compile": "bun build tests/integration/bun-compile-test.ts --compile --outfile /tmp/brainy-bun-test && /tmp/brainy-bun-test",
"test:wasm": "npx vitest run tests/integration/wasm-embeddings.test.ts",
- "download-model": "node scripts/download-model.cjs",
- "download-models": "node scripts/download-models.cjs",
- "download-models:q8": "node scripts/download-models.cjs",
- "models:verify": "node scripts/ensure-models.js",
+ "typecheck": "tsc --noEmit",
"lint": "eslint --ext .ts,.js src/",
"lint:fix": "eslint --ext .ts,.js src/ --fix",
"format": "prettier --write \"src/**/*.{ts,js}\"",
@@ -146,12 +143,8 @@
"files": [
"dist/**/*.js",
"dist/**/*.d.ts",
+ "dist/**/*.wasm",
"bin/",
- "assets/models/**/*",
- "scripts/download-models.cjs",
- "scripts/download-model.cjs",
- "scripts/ensure-models.js",
- "scripts/prepare-models.js",
"brainy.png",
"LICENSE",
"README.md",
@@ -187,7 +180,6 @@
"@azure/identity": "^4.0.0",
"@azure/storage-blob": "^12.17.0",
"@google-cloud/storage": "^7.14.0",
- "onnxruntime-web": "^1.22.0",
"@msgpack/msgpack": "^3.1.2",
"@types/js-yaml": "^4.0.9",
"boxen": "^8.0.1",
diff --git a/scripts/build-candle-wasm.sh b/scripts/build-candle-wasm.sh
new file mode 100755
index 00000000..0fc3cc6c
--- /dev/null
+++ b/scripts/build-candle-wasm.sh
@@ -0,0 +1,132 @@
+#!/bin/bash
+# Build script for Candle WASM embedding engine
+#
+# Requirements:
+# - Rust toolchain (rustup)
+# - wasm-pack (cargo install wasm-pack)
+# - Build tools (build-essential on Ubuntu/Debian)
+#
+# Usage:
+# ./scripts/build-candle-wasm.sh
+# ./scripts/build-candle-wasm.sh --release
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
+CANDLE_DIR="$PROJECT_ROOT/src/embeddings/candle-wasm"
+OUTPUT_DIR="$PROJECT_ROOT/src/embeddings/wasm/pkg"
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+NC='\033[0m' # No Color
+
+echo -e "${GREEN}Building Candle WASM embedding engine...${NC}"
+
+# Check prerequisites
+check_prerequisites() {
+ local missing=()
+
+ if ! command -v rustc &> /dev/null; then
+ missing+=("rust (install via: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh)")
+ fi
+
+ if ! command -v wasm-pack &> /dev/null; then
+ missing+=("wasm-pack (install via: cargo install wasm-pack)")
+ fi
+
+ if ! command -v cc &> /dev/null && ! command -v gcc &> /dev/null; then
+ missing+=("C compiler (install via: sudo apt-get install build-essential)")
+ fi
+
+ if [ ${#missing[@]} -gt 0 ]; then
+ echo -e "${RED}Missing prerequisites:${NC}"
+ for prereq in "${missing[@]}"; do
+ echo " - $prereq"
+ done
+ exit 1
+ fi
+
+ echo -e "${GREEN}All prerequisites found.${NC}"
+}
+
+# Download model files if not present
+download_model() {
+ local MODEL_DIR="$PROJECT_ROOT/assets/models/all-MiniLM-L6-v2"
+ local SAFETENSORS="$MODEL_DIR/model.safetensors"
+ local TOKENIZER="$MODEL_DIR/tokenizer.json"
+ local CONFIG="$MODEL_DIR/config.json"
+
+ if [ -f "$SAFETENSORS" ] && [ -f "$TOKENIZER" ] && [ -f "$CONFIG" ]; then
+ echo -e "${GREEN}Model files already present.${NC}"
+ return
+ fi
+
+ echo -e "${YELLOW}Downloading model files...${NC}"
+ mkdir -p "$MODEL_DIR"
+
+ # Download from HuggingFace Hub
+ local HF_URL="https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main"
+
+ if [ ! -f "$SAFETENSORS" ]; then
+ curl -L "$HF_URL/model.safetensors" -o "$SAFETENSORS"
+ fi
+
+ if [ ! -f "$TOKENIZER" ]; then
+ curl -L "$HF_URL/tokenizer.json" -o "$TOKENIZER"
+ fi
+
+ if [ ! -f "$CONFIG" ]; then
+ curl -L "$HF_URL/config.json" -o "$CONFIG"
+ fi
+
+ echo -e "${GREEN}Model files downloaded.${NC}"
+}
+
+# Build WASM
+build_wasm() {
+ local BUILD_MODE="${1:-release}"
+
+ echo -e "${GREEN}Building WASM (${BUILD_MODE})...${NC}"
+ cd "$CANDLE_DIR"
+
+ if [ "$BUILD_MODE" = "release" ]; then
+ wasm-pack build --target web --release --out-dir "$OUTPUT_DIR"
+ else
+ wasm-pack build --target web --dev --out-dir "$OUTPUT_DIR"
+ fi
+
+ echo -e "${GREEN}WASM build complete. Output: $OUTPUT_DIR${NC}"
+}
+
+# Main
+main() {
+ local mode="release"
+
+ while [[ $# -gt 0 ]]; do
+ case $1 in
+ --dev|--debug)
+ mode="dev"
+ shift
+ ;;
+ --release)
+ mode="release"
+ shift
+ ;;
+ *)
+ echo "Unknown option: $1"
+ exit 1
+ ;;
+ esac
+ done
+
+ check_prerequisites
+ download_model
+ build_wasm "$mode"
+
+ echo -e "${GREEN}Done! WASM package ready at: $OUTPUT_DIR${NC}"
+}
+
+main "$@"
diff --git a/scripts/buildKeywordEmbeddings.ts b/scripts/buildKeywordEmbeddings.ts
deleted file mode 100644
index fbf115fe..00000000
--- a/scripts/buildKeywordEmbeddings.ts
+++ /dev/null
@@ -1,571 +0,0 @@
-/**
- * Build Keyword Embeddings - Generate pre-computed embeddings for all keywords
- *
- * Extracts keywords from TypeInferenceSystem, adds strategic synonyms,
- * and generates semantic embeddings for fast type inference.
- *
- * Output: src/neural/embeddedKeywordEmbeddings.ts (~2-3MB)
- * Runtime: ~60-90 seconds (one-time build cost)
- */
-
-import { TransformerEmbedding } from '../src/utils/embedding.js'
-import { NounType, VerbType } from '../src/types/graphTypes.js'
-import { writeFileSync } from 'fs'
-import { prodLog } from '../src/utils/logger.js'
-
-interface KeywordDefinition {
- keyword: string
- type: NounType | VerbType
- typeCategory: 'noun' | 'verb'
- confidence: number
- isCanonical: boolean
-}
-
-/**
- * Extract and expand keywords with synonyms (NOUNS + VERBS)
- */
-function buildExpandedKeywordList(): KeywordDefinition[] {
- const keywords: KeywordDefinition[] = []
-
- // Helper to add noun keywords
- const addNoun = (words: string[], type: NounType, confidence: number, isCanonical = true) => {
- for (const word of words) {
- keywords.push({ keyword: word, type, typeCategory: 'noun', confidence, isCanonical })
- }
- }
-
- // Helper to add verb keywords
- const addVerb = (words: string[], type: VerbType, confidence: number, isCanonical = true) => {
- for (const word of words) {
- keywords.push({ keyword: word, type, typeCategory: 'verb', confidence, isCanonical })
- }
- }
-
- // Legacy alias for noun keywords
- const add = addNoun
-
- // ========== Person Type ==========
- // Core professional roles (canonical)
- add(['person', 'people', 'individual', 'human'], NounType.Person, 0.95, true)
- add(['employee', 'worker', 'staff', 'personnel'], NounType.Person, 0.90, true)
-
- // Engineering & Tech
- add(['engineer', 'developer', 'programmer', 'architect', 'designer', 'technician'], NounType.Person, 0.95, true)
- add(['coder', 'techie'], NounType.Person, 0.85, false) // Synonyms
-
- // Medical
- add(['doctor', 'physician', 'surgeon', 'nurse', 'therapist'], NounType.Person, 0.95, true)
- add(['cardiologist', 'oncologist', 'neurologist', 'psychiatrist', 'psychologist'], NounType.Person, 0.90, true)
- add(['radiologist', 'pathologist', 'anesthesiologist', 'dermatologist'], NounType.Person, 0.90, true)
- add(['pediatrician', 'obstetrician', 'gynecologist', 'ophthalmologist'], NounType.Person, 0.90, true)
- add(['dentist', 'orthodontist', 'pharmacist', 'paramedic', 'emt'], NounType.Person, 0.90, true)
- add(['medic', 'practitioner', 'clinician'], NounType.Person, 0.85, false) // Medical synonyms
-
- // Management & Leadership
- add(['manager', 'director', 'executive', 'leader', 'supervisor', 'coordinator'], NounType.Person, 0.95, true)
- add(['ceo', 'cto', 'cfo', 'coo', 'vp', 'president', 'founder', 'owner'], NounType.Person, 0.95, true)
-
- // Professional services
- add(['analyst', 'consultant', 'specialist', 'expert', 'professional'], NounType.Person, 0.90, true)
- add(['lawyer', 'attorney', 'judge', 'paralegal'], NounType.Person, 0.95, true)
- add(['accountant', 'auditor', 'banker', 'trader', 'broker'], NounType.Person, 0.90, true)
- add(['advisor', 'counselor'], NounType.Person, 0.85, false)
-
- // Education & Research
- add(['teacher', 'professor', 'instructor', 'educator', 'tutor'], NounType.Person, 0.95, true)
- add(['student', 'pupil', 'learner', 'trainee', 'intern'], NounType.Person, 0.90, true)
- add(['researcher', 'scientist', 'scholar', 'academic'], NounType.Person, 0.95, true)
-
- // Creative professions
- add(['artist', 'musician', 'painter', 'sculptor', 'performer'], NounType.Person, 0.90, true)
- add(['author', 'writer', 'journalist', 'editor', 'reporter'], NounType.Person, 0.90, true)
-
- // Sales & Marketing
- add(['salesperson', 'marketer', 'recruiter', 'agent'], NounType.Person, 0.85, true)
-
- // Social relationships
- add(['friend', 'colleague', 'coworker', 'teammate', 'partner'], NounType.Person, 0.85, true)
- add(['customer', 'client', 'vendor', 'supplier', 'contractor'], NounType.Person, 0.85, true)
- add(['mentor', 'mentee', 'coach', 'volunteer', 'activist', 'advocate', 'supporter'], NounType.Person, 0.80, true)
-
- // Demographics
- add(['male', 'female', 'adult', 'child', 'teen', 'senior', 'junior'], NounType.Person, 0.75, true)
-
- // Multi-word professions (important for semantic matching)
- add(['software engineer', 'software developer', 'web developer'], NounType.Person, 0.95, true)
- add(['data scientist', 'data engineer', 'machine learning engineer', 'ml engineer'], NounType.Person, 0.95, true)
- add(['product manager', 'project manager', 'engineering manager'], NounType.Person, 0.95, true)
- add(['ux designer', 'ui designer', 'graphic designer'], NounType.Person, 0.90, true)
- add(['medical doctor', 'registered nurse', 'healthcare worker'], NounType.Person, 0.90, false)
-
- // ========== Organization Type ==========
- add(['organization', 'company', 'business', 'corporation', 'enterprise'], NounType.Organization, 0.95, true)
- add(['firm', 'agency', 'bureau', 'office', 'department'], NounType.Organization, 0.90, true)
- add(['startup', 'venture', 'subsidiary', 'branch', 'division'], NounType.Organization, 0.90, true)
- add(['institution', 'foundation', 'association', 'society', 'club'], NounType.Organization, 0.90, true)
- add(['nonprofit', 'ngo', 'charity', 'trust', 'federation'], NounType.Organization, 0.90, true)
- add(['government', 'ministry', 'administration', 'authority'], NounType.Organization, 0.90, true)
- add(['university', 'college', 'school', 'academy', 'institute'], NounType.Organization, 0.90, true)
- add(['hospital', 'clinic', 'medical center'], NounType.Organization, 0.90, true)
- add(['bank', 'credit union'], NounType.Organization, 0.90, true)
- add(['manufacturer', 'factory', 'plant', 'facility'], NounType.Organization, 0.85, true)
- add(['retailer', 'store', 'shop', 'outlet', 'chain'], NounType.Organization, 0.85, true)
- add(['restaurant', 'hotel', 'resort', 'casino'], NounType.Organization, 0.85, true)
- add(['publisher', 'studio', 'gallery', 'museum', 'library'], NounType.Organization, 0.85, true)
- add(['lab', 'laboratory', 'research center'], NounType.Organization, 0.85, true)
- add(['team', 'squad', 'crew', 'group', 'committee'], NounType.Organization, 0.80, true)
-
- // Organization synonyms
- add(['corp', 'inc', 'llc', 'ltd'], NounType.Organization, 0.85, false)
-
- // ========== Location Type ==========
- add(['location', 'place', 'area', 'region', 'zone', 'district'], NounType.Location, 0.90, true)
- add(['city', 'town', 'village', 'municipality', 'metro'], NounType.Location, 0.95, true)
- add(['country', 'nation', 'state', 'province', 'territory'], NounType.Location, 0.95, true)
- add(['county', 'parish', 'prefecture', 'canton'], NounType.Location, 0.85, true)
- add(['continent', 'island', 'peninsula', 'archipelago'], NounType.Location, 0.90, true)
- add(['street', 'road', 'avenue', 'boulevard', 'lane', 'drive'], NounType.Location, 0.85, true)
- add(['address', 'building', 'structure', 'tower', 'complex'], NounType.Location, 0.85, true)
- add(['headquarters', 'hq', 'campus', 'site'], NounType.Location, 0.85, true)
- add(['center', 'venue', 'space', 'room'], NounType.Location, 0.80, true)
- add(['warehouse', 'depot', 'terminal', 'station', 'port'], NounType.Location, 0.85, true)
- add(['park', 'garden', 'plaza', 'square', 'mall'], NounType.Location, 0.85, true)
- add(['neighborhood', 'suburb', 'downtown', 'uptown'], NounType.Location, 0.80, true)
- add(['north', 'south', 'east', 'west', 'central'], NounType.Location, 0.70, true)
- add(['coastal', 'inland', 'urban', 'rural', 'remote'], NounType.Location, 0.70, true)
-
- // Common cities (for better semantic matching)
- add(['san francisco', 'new york', 'los angeles', 'chicago', 'boston'], NounType.Location, 0.95, true)
- add(['seattle', 'austin', 'denver', 'portland', 'miami'], NounType.Location, 0.95, true)
- add(['london', 'paris', 'berlin', 'tokyo', 'beijing'], NounType.Location, 0.95, true)
- add(['silicon valley', 'bay area', 'new york city', 'washington dc'], NounType.Location, 0.95, true)
-
- // ========== Document Type ==========
- add(['document', 'file', 'text', 'writing', 'manuscript'], NounType.Document, 0.95, true)
- add(['report', 'summary', 'brief', 'overview', 'analysis'], NounType.Document, 0.90, true)
- add(['article', 'essay', 'paper', 'publication', 'journal'], NounType.Document, 0.90, true)
- add(['book', 'ebook', 'novel', 'chapter', 'volume'], NounType.Document, 0.90, true)
- add(['manual', 'guide', 'handbook', 'reference', 'documentation'], NounType.Document, 0.90, true)
- add(['tutorial', 'walkthrough', 'instructions'], NounType.Document, 0.85, true)
- add(['specification', 'spec', 'standard', 'protocol'], NounType.Document, 0.85, true)
- add(['proposal', 'pitch', 'presentation', 'slide', 'deck'], NounType.Document, 0.85, true)
- add(['contract', 'agreement', 'license', 'terms', 'policy'], NounType.Document, 0.90, true)
- add(['invoice', 'receipt', 'statement', 'bill', 'voucher'], NounType.Document, 0.85, true)
- add(['form', 'application', 'survey', 'questionnaire'], NounType.Document, 0.85, true)
- add(['transcript', 'minutes', 'record', 'log', 'entry'], NounType.Document, 0.85, true)
- add(['note', 'memo', 'message', 'email', 'letter'], NounType.Document, 0.85, true)
- add(['whitepaper', 'thesis', 'dissertation', 'abstract'], NounType.Document, 0.90, true)
- add(['readme', 'changelog', 'wiki'], NounType.Document, 0.85, true)
- add(['cv', 'resume', 'portfolio', 'profile'], NounType.Document, 0.85, true)
-
- // Document synonyms
- add(['doc', 'docs', 'howto'], NounType.Document, 0.80, false)
-
- // ========== Media Type ==========
- add(['media', 'multimedia', 'content'], NounType.Media, 0.90, true)
- add(['image', 'photo', 'picture', 'photograph', 'illustration'], NounType.Media, 0.90, true)
- add(['graphic', 'icon', 'logo', 'banner', 'thumbnail'], NounType.Media, 0.85, true)
- add(['video', 'movie', 'film', 'clip', 'recording'], NounType.Media, 0.90, true)
- add(['animation', 'gif', 'stream', 'broadcast'], NounType.Media, 0.85, true)
- add(['audio', 'sound', 'music', 'song', 'track', 'album'], NounType.Media, 0.90, true)
- add(['podcast', 'episode', 'audiobook'], NounType.Media, 0.85, true)
- add(['screenshot', 'asset', 'resource', 'attachment'], NounType.Media, 0.80, true)
-
- // ========== Concept Type ==========
- add(['concept', 'idea', 'notion', 'theory', 'principle'], NounType.Concept, 0.90, true)
- add(['philosophy', 'ideology', 'belief', 'doctrine'], NounType.Concept, 0.85, true)
- add(['topic', 'subject', 'theme', 'matter', 'issue'], NounType.Concept, 0.85, true)
- add(['category', 'classification', 'taxonomy', 'domain'], NounType.Concept, 0.80, true)
- add(['field', 'discipline', 'specialty'], NounType.Concept, 0.85, true)
- add(['technology', 'tech', 'innovation', 'invention'], NounType.Concept, 0.90, true)
- add(['science', 'scientific', 'research'], NounType.Concept, 0.90, true)
-
- // Scientific domains
- add(['mathematics', 'math', 'statistics', 'algebra', 'calculus'], NounType.Concept, 0.85, true)
- add(['physics', 'quantum', 'mechanics', 'thermodynamics'], NounType.Concept, 0.85, true)
- add(['chemistry', 'biology', 'genetics', 'neuroscience'], NounType.Concept, 0.85, true)
- add(['engineering', 'architecture', 'design'], NounType.Concept, 0.85, true)
-
- // Computer science
- add(['computer science', 'programming', 'algorithm'], NounType.Concept, 0.90, true)
- add(['artificial intelligence', 'machine learning', 'deep learning'], NounType.Concept, 0.95, true)
- add(['ai', 'ml'], NounType.Concept, 0.90, true)
- add(['data science', 'analytics', 'big data'], NounType.Concept, 0.90, true)
- add(['natural language processing', 'computer vision'], NounType.Concept, 0.90, true)
-
- // Humanities
- add(['history', 'literature', 'poetry', 'fiction'], NounType.Concept, 0.85, true)
- add(['art', 'music', 'sports'], NounType.Concept, 0.85, true)
- add(['politics', 'economics', 'psychology', 'sociology'], NounType.Concept, 0.85, true)
- add(['religion', 'spiritual', 'philosophy'], NounType.Concept, 0.85, true)
-
- // ========== Event Type ==========
- add(['event', 'occasion', 'happening', 'occurrence'], NounType.Event, 0.90, true)
- add(['meeting', 'conference', 'summit', 'convention'], NounType.Event, 0.90, true)
- add(['seminar', 'symposium', 'forum', 'workshop'], NounType.Event, 0.90, true)
- add(['training', 'bootcamp', 'course', 'webinar'], NounType.Event, 0.85, true)
- add(['presentation', 'talk', 'lecture', 'session', 'class'], NounType.Event, 0.85, true)
- add(['party', 'celebration', 'gathering', 'ceremony'], NounType.Event, 0.85, true)
- add(['festival', 'carnival', 'fair', 'exhibition'], NounType.Event, 0.85, true)
- add(['concert', 'performance', 'show'], NounType.Event, 0.85, true)
- add(['game', 'match', 'tournament', 'championship', 'race'], NounType.Event, 0.85, true)
- add(['launch', 'release', 'premiere', 'debut', 'announcement'], NounType.Event, 0.85, true)
-
- // ========== Product Type ==========
- add(['product', 'item', 'goods', 'merchandise', 'commodity'], NounType.Product, 0.90, true)
- add(['offering', 'solution', 'package', 'bundle'], NounType.Product, 0.85, true)
- add(['software', 'app', 'application', 'program', 'tool'], NounType.Product, 0.90, true)
- add(['platform', 'system', 'framework', 'library'], NounType.Product, 0.85, true)
- add(['device', 'gadget', 'machine', 'equipment'], NounType.Product, 0.85, true)
- add(['hardware', 'component', 'part', 'accessory'], NounType.Product, 0.85, true)
- add(['vehicle', 'car', 'automobile', 'truck', 'bike'], NounType.Product, 0.85, true)
- add(['phone', 'smartphone', 'mobile', 'tablet'], NounType.Product, 0.90, true)
- add(['computer', 'laptop', 'desktop', 'pc', 'mac'], NounType.Product, 0.90, true)
- add(['watch', 'wearable', 'tracker', 'monitor'], NounType.Product, 0.85, true)
- add(['camera', 'lens', 'sensor', 'scanner'], NounType.Product, 0.85, true)
-
- // ========== Service Type ==========
- add(['service', 'support', 'assistance'], NounType.Service, 0.90, true)
- add(['consulting', 'advisory', 'guidance'], NounType.Service, 0.85, true)
- add(['maintenance', 'repair', 'installation', 'setup'], NounType.Service, 0.85, true)
- add(['hosting', 'cloud', 'saas', 'paas', 'iaas'], NounType.Service, 0.85, true)
- add(['delivery', 'shipping', 'logistics', 'transport'], NounType.Service, 0.85, true)
- add(['subscription', 'membership', 'plan'], NounType.Service, 0.85, true)
- add(['training', 'education', 'coaching', 'mentoring'], NounType.Service, 0.85, true)
- add(['healthcare', 'medical', 'dental', 'therapy'], NounType.Service, 0.85, true)
- add(['legal', 'accounting', 'financial', 'insurance'], NounType.Service, 0.85, true)
- add(['marketing', 'advertising', 'promotion'], NounType.Service, 0.85, true)
-
- // ========== User Type ==========
- add(['user', 'account', 'profile', 'identity'], NounType.Person, 0.90, true)
- add(['username', 'login', 'credential'], NounType.Person, 0.85, true)
- add(['subscriber', 'follower', 'fan', 'supporter'], NounType.Person, 0.85, true)
- add(['member', 'participant', 'contributor', 'author'], NounType.Person, 0.85, true)
- add(['viewer', 'reader', 'listener', 'watcher'], NounType.Person, 0.80, true)
- add(['player', 'gamer', 'competitor'], NounType.Person, 0.80, true)
- add(['guest', 'visitor', 'attendee'], NounType.Person, 0.80, true)
-
- // ========== Task & Project Types ==========
- add(['task', 'todo', 'action', 'activity', 'job'], NounType.Task, 0.85, true)
- add(['assignment', 'duty', 'work'], NounType.Task, 0.85, true)
- add(['ticket', 'issue', 'bug', 'defect', 'problem'], NounType.Task, 0.85, true)
- add(['feature', 'enhancement', 'improvement', 'request'], NounType.Task, 0.85, true)
- add(['project', 'program', 'initiative'], NounType.Project, 0.90, true)
- add(['campaign', 'drive', 'venture'], NounType.Project, 0.85, true)
- add(['plan', 'strategy', 'roadmap'], NounType.Project, 0.85, true)
- add(['milestone', 'deliverable', 'objective', 'goal'], NounType.Project, 0.85, true)
- add(['sprint', 'iteration', 'cycle', 'phase'], NounType.Project, 0.80, true)
-
- // ========== Other Types ==========
- add(['process', 'procedure', 'method', 'approach'], NounType.Process, 0.80, true)
- add(['workflow', 'pipeline', 'sequence'], NounType.Process, 0.80, true)
- add(['algorithm', 'logic', 'routine', 'operation'], NounType.Process, 0.75, true)
-
- add(['collection', 'set', 'group', 'batch'], NounType.Collection, 0.80, true)
- add(['list', 'array', 'series'], NounType.Collection, 0.80, true)
- add(['dataset', 'data', 'database'], NounType.Collection, 0.85, true)
- add(['repository', 'archive', 'library'], NounType.Collection, 0.80, true)
-
- add(['state', 'status', 'condition'], NounType.State, 0.75, true)
- add(['active', 'inactive', 'pending', 'completed'], NounType.State, 0.70, true)
-
- add(['role', 'position', 'title'], NounType.Role, 0.80, true)
- add(['permission', 'access', 'privilege'], NounType.Role, 0.75, true)
-
- add(['hypothesis', 'theory', 'conjecture'], NounType.Hypothesis, 0.85, true)
- add(['experiment', 'study', 'trial', 'test'], NounType.Experiment, 0.85, true)
- add(['regulation', 'rule', 'law', 'statute'], NounType.Regulation, 0.85, true)
- add(['interface', 'api', 'endpoint'], NounType.Interface, 0.85, true)
- add(['resource', 'asset', 'capacity'], NounType.Resource, 0.80, true)
-
- // ==================== VERB TYPES ====================
- // Now add all 127 VerbTypes with keywords and synonyms
-
- console.log('\n Adding verb keywords...')
-
- // ========== Core Relationship Types ==========
- addVerb(['related to', 'related', 'connected to', 'associated with', 'linked to'], VerbType.RelatedTo, 0.90, true)
- addVerb(['connection', 'association', 'link', 'relationship'], VerbType.RelatedTo, 0.85, false)
-
- addVerb(['contains', 'includes', 'has', 'comprises', 'encompasses'], VerbType.Contains, 0.95, true)
- addVerb(['holding', 'containing', 'including'], VerbType.Contains, 0.85, false)
-
- addVerb(['part of', 'belongs to', 'component of', 'element of', 'member of'], VerbType.PartOf, 0.95, true)
- addVerb(['within', 'inside', 'subset of'], VerbType.PartOf, 0.85, false)
-
- addVerb(['located at', 'positioned at', 'situated at', 'found at', 'based at'], VerbType.LocatedAt, 0.95, true)
- addVerb(['location', 'position', 'whereabouts'], VerbType.LocatedAt, 0.85, false)
-
- addVerb(['references', 'cites', 'refers to', 'mentions', 'points to'], VerbType.References, 0.95, true)
- addVerb(['citation', 'reference', 'pointer'], VerbType.References, 0.85, false)
-
- // ========== Temporal/Causal Types ==========
- addVerb(['precedes', 'comes before', 'happens before', 'leads to', 'prior to'], VerbType.Precedes, 0.90, true)
- addVerb(['preceding', 'earlier than', 'before'], VerbType.Precedes, 0.85, false)
-
- addVerb(['succeeds', 'comes after', 'follows', 'happens after', 'subsequent to'], VerbType.Precedes, 0.90, true)
- addVerb(['succeeding', 'later than', 'after'], VerbType.Precedes, 0.85, false)
-
- addVerb(['causes', 'results in', 'leads to', 'brings about', 'triggers'], VerbType.Causes, 0.95, true)
- addVerb(['influences', 'affects', 'impacts', 'produces'], VerbType.Causes, 0.90, true)
- addVerb(['causation', 'consequence', 'effect'], VerbType.Causes, 0.85, false)
-
- addVerb(['depends on', 'relies on', 'contingent on', 'conditional on'], VerbType.DependsOn, 0.95, true)
- addVerb(['dependency', 'reliance', 'dependence'], VerbType.DependsOn, 0.85, false)
-
- addVerb(['requires', 'needs', 'necessitates', 'demands', 'calls for'], VerbType.Requires, 0.95, true)
- addVerb(['requirement', 'necessity', 'prerequisite'], VerbType.Requires, 0.85, false)
-
- // ========== Creation/Transformation Types ==========
- addVerb(['creates', 'makes', 'builds', 'produces', 'generates'], VerbType.Creates, 0.95, true)
- addVerb(['constructs', 'develops', 'crafts', 'forms'], VerbType.Creates, 0.90, true)
- addVerb(['creation', 'production', 'generation'], VerbType.Creates, 0.85, false)
-
- addVerb(['transforms', 'converts', 'changes', 'morphs', 'alters'], VerbType.Transforms, 0.95, true)
- addVerb(['transformation', 'conversion', 'metamorphosis'], VerbType.Transforms, 0.85, false)
-
- addVerb(['becomes', 'turns into', 'evolves into', 'transitions to'], VerbType.Becomes, 0.95, true)
- addVerb(['becoming', 'transition', 'evolution'], VerbType.Becomes, 0.85, false)
-
- addVerb(['modifies', 'updates', 'changes', 'edits', 'adjusts'], VerbType.Modifies, 0.95, true)
- addVerb(['alters', 'amends', 'revises', 'tweaks'], VerbType.Modifies, 0.90, true)
- addVerb(['modification', 'update', 'change'], VerbType.Modifies, 0.85, false)
-
- addVerb(['consumes', 'uses up', 'depletes', 'exhausts', 'drains'], VerbType.Consumes, 0.95, true)
- addVerb(['consumption', 'usage', 'depletion'], VerbType.Consumes, 0.85, false)
-
- // ========== Ownership/Attribution Types ==========
- addVerb(['owns', 'possesses', 'holds', 'controls', 'has'], VerbType.Owns, 0.95, true)
- addVerb(['ownership', 'possession', 'control'], VerbType.Owns, 0.85, false)
-
- addVerb(['attributed to', 'credited to', 'ascribed to', 'assigned to'], VerbType.AttributedTo, 0.95, true)
- addVerb(['attribution', 'credit', 'acknowledgment'], VerbType.AttributedTo, 0.85, false)
-
- addVerb(['created by', 'made by', 'built by', 'authored by', 'developed by'], VerbType.Creates, 0.95, true)
- addVerb(['creator', 'author', 'maker'], VerbType.Creates, 0.85, false)
-
- addVerb(['belongs to', 'owned by', 'property of', 'part of'], VerbType.Owns, 0.95, true)
- addVerb(['belonging', 'membership'], VerbType.Owns, 0.85, false)
-
- // ========== Social/Organizational Types ==========
- addVerb(['member of', 'belongs to', 'affiliated with', 'part of'], VerbType.MemberOf, 0.95, true)
- addVerb(['works for', 'employed by', 'serves'], VerbType.MemberOf, 0.90, true)
- addVerb(['membership', 'affiliation'], VerbType.MemberOf, 0.85, false)
-
- addVerb(['works with', 'collaborates with', 'partners with', 'cooperates with'], VerbType.WorksWith, 0.95, true)
- addVerb(['teams with', 'joins forces with'], VerbType.WorksWith, 0.90, true)
- addVerb(['collaboration', 'partnership', 'cooperation'], VerbType.WorksWith, 0.85, false)
-
- addVerb(['friend of', 'friends with', 'befriends', 'friendly with'], VerbType.FriendOf, 0.95, true)
- addVerb(['friendship', 'companionship'], VerbType.FriendOf, 0.85, false)
-
- addVerb(['follows', 'tracks', 'monitors', 'subscribes to', 'watches'], VerbType.Follows, 0.95, true)
- addVerb(['following', 'follower', 'subscriber'], VerbType.Follows, 0.85, false)
-
- addVerb(['likes', 'enjoys', 'prefers', 'favors', 'appreciates'], VerbType.Likes, 0.95, true)
- addVerb(['fond of', 'partial to'], VerbType.Likes, 0.90, true)
-
- addVerb(['reports to', 'answers to', 'subordinate to', 'under'], VerbType.ReportsTo, 0.95, true)
- addVerb(['reporting', 'subordination'], VerbType.ReportsTo, 0.85, false)
-
- addVerb(['supervises', 'manages', 'oversees', 'directs', 'leads'], VerbType.ReportsTo, 0.95, true)
- addVerb(['supervision', 'management', 'oversight'], VerbType.ReportsTo, 0.85, false)
-
- addVerb(['mentors', 'coaches', 'guides', 'advises', 'teaches'], VerbType.Mentors, 0.95, true)
- addVerb(['mentorship', 'coaching', 'guidance'], VerbType.Mentors, 0.85, false)
-
- addVerb(['communicates with', 'talks to', 'corresponds with', 'exchanges with'], VerbType.Communicates, 0.95, true)
- addVerb(['speaks with', 'chats with', 'discusses with'], VerbType.Communicates, 0.90, true)
- addVerb(['communication', 'correspondence', 'dialogue'], VerbType.Communicates, 0.85, false)
-
- // ========== Descriptive/Functional Types ==========
- addVerb(['describes', 'explains', 'details', 'characterizes', 'portrays'], VerbType.Describes, 0.95, true)
- addVerb(['depicts', 'illustrates', 'outlines'], VerbType.Describes, 0.90, true)
- addVerb(['description', 'explanation', 'account'], VerbType.Describes, 0.85, false)
-
- addVerb(['defines', 'specifies', 'determines', 'establishes', 'sets'], VerbType.Defines, 0.95, true)
- addVerb(['definition', 'specification', 'determination'], VerbType.Defines, 0.85, false)
-
- addVerb(['categorizes', 'classifies', 'groups', 'sorts', 'organizes'], VerbType.Categorizes, 0.95, true)
- addVerb(['categorization', 'classification', 'taxonomy'], VerbType.Categorizes, 0.85, false)
-
- addVerb(['measures', 'quantifies', 'gauges', 'assesses', 'evaluates'], VerbType.Measures, 0.95, true)
- addVerb(['measurement', 'quantification', 'assessment'], VerbType.Measures, 0.85, false)
-
- addVerb(['evaluates', 'assesses', 'judges', 'appraises', 'reviews'], VerbType.Evaluates, 0.95, true)
- addVerb(['rates', 'scores', 'critiques'], VerbType.Evaluates, 0.90, true)
- addVerb(['evaluation', 'assessment', 'appraisal'], VerbType.Evaluates, 0.85, false)
-
- addVerb(['uses', 'utilizes', 'employs', 'applies', 'leverages'], VerbType.Uses, 0.95, true)
- addVerb(['usage', 'utilization', 'application'], VerbType.Uses, 0.85, false)
-
- addVerb(['implements', 'executes', 'realizes', 'enacts', 'carries out'], VerbType.Implements, 0.95, true)
- addVerb(['implementation', 'execution', 'realization'], VerbType.Implements, 0.85, false)
-
- addVerb(['extends', 'expands', 'broadens', 'enlarges', 'builds on'], VerbType.Extends, 0.95, true)
- addVerb(['enhances', 'augments', 'amplifies'], VerbType.Extends, 0.90, true)
- addVerb(['extension', 'expansion', 'enhancement'], VerbType.Extends, 0.85, false)
-
- // ========== Enhanced Relationship Types ==========
- addVerb(['inherits', 'derives from', 'inherits from', 'descended from'], VerbType.Inherits, 0.95, true)
- addVerb(['inheritance', 'derivation', 'legacy'], VerbType.Inherits, 0.85, false)
-
- addVerb(['conflicts with', 'contradicts', 'opposes', 'clashes with'], VerbType.Conflicts, 0.95, true)
- addVerb(['disagrees with', 'incompatible with'], VerbType.Conflicts, 0.90, true)
- addVerb(['conflict', 'contradiction', 'opposition'], VerbType.Conflicts, 0.85, false)
-
- addVerb(['synchronizes with', 'coordinates with', 'syncs with', 'aligns with'], VerbType.Synchronizes, 0.95, true)
- addVerb(['synchronization', 'coordination', 'alignment'], VerbType.Synchronizes, 0.85, false)
-
- addVerb(['competes with', 'rivals', 'contests', 'vies with'], VerbType.Competes, 0.95, true)
- addVerb(['competition', 'rivalry', 'contest'], VerbType.Competes, 0.85, false)
-
- return keywords
-}
-
-/**
- * Main build function
- */
-async function buildKeywordEmbeddings() {
- console.log('🔨 Building Keyword Embeddings for Semantic Type Inference\n')
- console.log('='.repeat(70))
-
- // Step 1: Build keyword list
- console.log('\n📝 Step 1: Building expanded keyword dictionary...')
- const keywords = buildExpandedKeywordList()
-
- const canonical = keywords.filter(k => k.isCanonical).length
- const synonyms = keywords.filter(k => !k.isCanonical).length
-
- console.log(`✅ Generated ${keywords.length} keywords (${canonical} canonical, ${synonyms} synonyms)`)
-
- // Step 2: Initialize embedder
- console.log('\n🎯 Step 2: Initializing TransformerEmbedding model...')
- const embedder = new TransformerEmbedding({ verbose: true })
- await embedder.init()
- console.log('✅ Embedder initialized')
-
- // Step 3: Generate embeddings
- console.log(`\n🚀 Step 3: Generating embeddings for ${keywords.length} keywords...`)
- console.log('(This may take 60-90 seconds)\n')
-
- const embeddings = []
- let processed = 0
- const startTime = Date.now()
-
- for (const def of keywords) {
- const embedding = await embedder.embed(def.keyword)
-
- embeddings.push({
- keyword: def.keyword,
- type: def.type,
- typeCategory: def.typeCategory,
- confidence: def.confidence,
- isCanonical: def.isCanonical,
- embedding: Array.from(embedding)
- })
-
- processed++
- if (processed % 50 === 0) {
- const elapsed = ((Date.now() - startTime) / 1000).toFixed(1)
- const rate = (processed / (Date.now() - startTime) * 1000).toFixed(1)
- const eta = ((keywords.length - processed) / parseFloat(rate)).toFixed(0)
- console.log(` Progress: ${processed}/${keywords.length} (${(processed/keywords.length*100).toFixed(1)}%) - ${rate}/sec - ETA: ${eta}s`)
- }
- }
-
- const totalTime = ((Date.now() - startTime) / 1000).toFixed(1)
- console.log(`\n✅ All embeddings generated in ${totalTime}s`)
-
- // Step 4: Generate TypeScript file
- console.log('\n📄 Step 4: Writing embeddedKeywordEmbeddings.ts...')
-
- const sizeKB = (embeddings.length * 384 * 4 / 1024).toFixed(1)
- const sizeMB = (parseFloat(sizeKB) / 1024).toFixed(2)
-
- // Calculate stats
- const nounKeywords = embeddings.filter(e => e.typeCategory === 'noun').length
- const verbKeywords = embeddings.filter(e => e.typeCategory === 'verb').length
- const canonicalKeywords = embeddings.filter(e => e.isCanonical).length
- const synonymKeywords = embeddings.filter(e => !e.isCanonical).length
-
- const output = `/**
- * Pre-computed Keyword Embeddings for Unified Semantic Type Inference
- *
- * Generated by: scripts/buildKeywordEmbeddings.ts
- * Generated on: ${new Date().toISOString()}
- * Total keywords: ${embeddings.length} (${nounKeywords} nouns + ${verbKeywords} verbs)
- * Canonical: ${canonicalKeywords}, Synonyms: ${synonymKeywords}
- * Embedding dimension: 384
- * Total size: ${sizeMB}MB
- *
- * This file contains pre-computed semantic embeddings for ALL type inference keywords.
- * Supports unified noun + verb semantic inference via SemanticTypeInference.
- * Used for O(log n) semantic matching via HNSW index.
- */
-
-import { NounType, VerbType } from '../types/graphTypes.js'
-import { Vector } from '../coreTypes.js'
-
-export interface KeywordEmbedding {
- keyword: string
- type: NounType | VerbType
- typeCategory: 'noun' | 'verb'
- confidence: number
- isCanonical: boolean
- embedding: Vector
-}
-
-// Use 'any' type to avoid TypeScript union complexity issues with 1050+ literal types
-const KEYWORD_EMBEDDINGS: any = ${JSON.stringify(embeddings, null, 2)}
-
-export function getKeywordEmbeddings(): KeywordEmbedding[] {
- return KEYWORD_EMBEDDINGS
-}
-
-export function getKeywordCount(): number {
- return KEYWORD_EMBEDDINGS.length
-}
-
-export function getNounKeywordCount(): number {
- return ${nounKeywords}
-}
-
-export function getVerbKeywordCount(): number {
- return ${verbKeywords}
-}
-
-export function getEmbeddingDimension(): number {
- return 384
-}
-`
-
- writeFileSync('src/neural/embeddedKeywordEmbeddings.ts', output, 'utf-8')
-
- console.log(`✅ Generated src/neural/embeddedKeywordEmbeddings.ts`)
- console.log(` Total keywords: ${embeddings.length} (${nounKeywords} nouns + ${verbKeywords} verbs)`)
- console.log(` Canonical: ${canonicalKeywords}, Synonyms: ${synonymKeywords}`)
- console.log(` Size: ${sizeMB}MB (${sizeKB}KB)`)
-
- console.log('\n' + '='.repeat(70))
- console.log('✅ Keyword embeddings build complete!')
- console.log('='.repeat(70))
-
- return embeddings.length
-}
-
-// Run if called directly
-if (import.meta.url === `file://${process.argv[1]}`) {
- buildKeywordEmbeddings()
- .then(count => {
- console.log(`\n🎉 Success! Generated embeddings for ${count} keywords.`)
- process.exit(0)
- })
- .catch(error => {
- console.error('\n❌ Build failed:', error.message)
- console.error(error.stack)
- process.exit(1)
- })
-}
-
-export { buildKeywordEmbeddings }
diff --git a/scripts/check-keyword-embeddings.cjs b/scripts/check-keyword-embeddings.cjs
deleted file mode 100644
index dab28aaa..00000000
--- a/scripts/check-keyword-embeddings.cjs
+++ /dev/null
@@ -1,31 +0,0 @@
-/**
- * Check if keyword embeddings need to be rebuilt
- * Exits with code 1 if rebuild needed, 0 if up-to-date
- */
-
-const fs = require('fs')
-const path = require('path')
-
-const EMBEDDED_FILE = 'src/neural/embeddedKeywordEmbeddings.ts'
-const BUILD_SCRIPT = 'scripts/buildKeywordEmbeddings.ts'
-
-const embeddedPath = path.join(process.cwd(), EMBEDDED_FILE)
-const buildScriptPath = path.join(process.cwd(), BUILD_SCRIPT)
-
-// Check if embedded file exists
-if (!fs.existsSync(embeddedPath)) {
- console.log('⚠️ Keyword embeddings file not found. Build required.')
- process.exit(1)
-}
-
-// Check if build script is newer than embedded file
-const embeddedStat = fs.statSync(embeddedPath)
-const buildScriptStat = fs.statSync(buildScriptPath)
-
-if (buildScriptStat.mtime > embeddedStat.mtime) {
- console.log('⚠️ Build script is newer than keyword embeddings. Rebuild required.')
- process.exit(1)
-}
-
-console.log('✅ Embedded keyword embeddings are up-to-date. Skipping rebuild.')
-process.exit(0)
diff --git a/scripts/download-model.cjs b/scripts/download-model.cjs
deleted file mode 100644
index 652c41ed..00000000
--- a/scripts/download-model.cjs
+++ /dev/null
@@ -1,175 +0,0 @@
-#!/usr/bin/env node
-/**
- * Download Model Assets
- *
- * Downloads the all-MiniLM-L6-v2 Q8 model from Hugging Face.
- * Run: node scripts/download-model.cjs
- */
-
-const fs = require('node:fs')
-const path = require('node:path')
-const https = require('node:https')
-
-const MODEL_DIR = path.join(__dirname, '..', 'assets', 'models', 'all-MiniLM-L6-v2-q8')
-const BASE_URL = 'https://huggingface.co/Xenova/all-MiniLM-L6-v2/resolve/main/onnx'
-
-const FILES = [
- {
- name: 'model_quantized.onnx',
- url: `${BASE_URL}/model_quantized.onnx`,
- dest: 'model.onnx',
- },
- {
- name: 'tokenizer.json',
- url: 'https://huggingface.co/Xenova/all-MiniLM-L6-v2/resolve/main/tokenizer.json',
- dest: 'tokenizer.json',
- },
- {
- name: 'config.json',
- url: 'https://huggingface.co/Xenova/all-MiniLM-L6-v2/resolve/main/config.json',
- dest: 'config.json',
- },
- {
- name: 'vocab.txt',
- url: 'https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main/vocab.txt',
- dest: 'vocab.txt',
- },
-]
-
-/**
- * Follow redirects and download file
- */
-function downloadFile(url, destPath, maxRedirects = 5) {
- return new Promise((resolve, reject) => {
- if (maxRedirects === 0) {
- reject(new Error('Too many redirects'))
- return
- }
-
- const doRequest = (reqUrl) => {
- const parsedUrl = new URL(reqUrl)
- const options = {
- hostname: parsedUrl.hostname,
- path: parsedUrl.pathname + parsedUrl.search,
- headers: {
- 'User-Agent': 'Brainy-Model-Downloader/1.0',
- },
- }
-
- https.get(options, (response) => {
- // Handle redirects
- if (response.statusCode >= 300 && response.statusCode < 400 && response.headers.location) {
- response.resume() // Consume response data to free memory
- const redirectUrl = response.headers.location.startsWith('http')
- ? response.headers.location
- : new URL(response.headers.location, reqUrl).toString()
- console.log(` ↳ Redirecting to: ${redirectUrl.slice(0, 80)}...`)
- downloadFile(redirectUrl, destPath, maxRedirects - 1)
- .then(resolve)
- .catch(reject)
- return
- }
-
- if (response.statusCode !== 200) {
- reject(new Error(`HTTP ${response.statusCode}`))
- return
- }
-
- const fileStream = fs.createWriteStream(destPath)
- let downloadedBytes = 0
- const totalBytes = parseInt(response.headers['content-length'] || '0', 10)
-
- response.on('data', (chunk) => {
- downloadedBytes += chunk.length
- if (totalBytes > 0) {
- const percent = Math.round((downloadedBytes / totalBytes) * 100)
- process.stdout.write(`\r Progress: ${percent}% (${Math.round(downloadedBytes / 1024 / 1024)}MB)`)
- }
- })
-
- response.pipe(fileStream)
-
- fileStream.on('finish', () => {
- fileStream.close()
- console.log(`\n ✅ Downloaded: ${path.basename(destPath)} (${Math.round(downloadedBytes / 1024 / 1024)}MB)`)
- resolve()
- })
-
- fileStream.on('error', (err) => {
- fs.unlink(destPath, () => {}) // Delete partial file
- reject(err)
- })
- }).on('error', reject)
- }
-
- doRequest(url)
- })
-}
-
-/**
- * Convert vocab.txt to vocab.json
- */
-function convertVocabToJson(vocabTxtPath, vocabJsonPath) {
- console.log('📝 Converting vocab.txt to vocab.json...')
- const content = fs.readFileSync(vocabTxtPath, 'utf-8')
- const lines = content.split('\n').filter(line => line.trim())
-
- const vocab = {}
- for (let i = 0; i < lines.length; i++) {
- vocab[lines[i]] = i
- }
-
- fs.writeFileSync(vocabJsonPath, JSON.stringify(vocab))
- console.log(` ✅ Created vocab.json with ${Object.keys(vocab).length} tokens`)
-
- // Remove vocab.txt since we have vocab.json
- fs.unlinkSync(vocabTxtPath)
-}
-
-async function main() {
- console.log('🔽 Downloading all-MiniLM-L6-v2 Q8 model assets...\n')
-
- // Create model directory
- fs.mkdirSync(MODEL_DIR, { recursive: true })
- console.log(`📁 Model directory: ${MODEL_DIR}\n`)
-
- // Download each file
- for (const file of FILES) {
- const destPath = path.join(MODEL_DIR, file.dest)
-
- // Check if already exists
- if (fs.existsSync(destPath)) {
- const stats = fs.statSync(destPath)
- if (stats.size > 0) {
- console.log(`⏭️ Skipping ${file.name} (already exists)`)
- continue
- }
- }
-
- console.log(`📥 Downloading ${file.name}...`)
- try {
- await downloadFile(file.url, destPath)
- } catch (error) {
- console.error(` ❌ Failed to download ${file.name}: ${error.message}`)
- process.exit(1)
- }
- }
-
- // Convert vocab.txt to vocab.json
- const vocabTxtPath = path.join(MODEL_DIR, 'vocab.txt')
- const vocabJsonPath = path.join(MODEL_DIR, 'vocab.json')
- if (fs.existsSync(vocabTxtPath) && !fs.existsSync(vocabJsonPath)) {
- convertVocabToJson(vocabTxtPath, vocabJsonPath)
- }
-
- console.log('\n✅ All model assets downloaded successfully!')
- console.log('\nModel files:')
- const files = fs.readdirSync(MODEL_DIR)
- for (const file of files) {
- const stats = fs.statSync(path.join(MODEL_DIR, file))
- const sizeMB = (stats.size / 1024 / 1024).toFixed(2)
- console.log(` - ${file}: ${sizeMB}MB`)
- }
-}
-
-main().catch(console.error)
diff --git a/scripts/download-models.cjs b/scripts/download-models.cjs
deleted file mode 100755
index 0a7f8523..00000000
--- a/scripts/download-models.cjs
+++ /dev/null
@@ -1,264 +0,0 @@
-#!/usr/bin/env node
-/**
- * Download and bundle models for offline usage
- */
-
-const fs = require('fs').promises
-const path = require('path')
-
-const MODEL_NAME = 'Xenova/all-MiniLM-L6-v2'
-const OUTPUT_DIR = './models'
-
-// Always download Q8 model only
-const downloadType = 'q8'
-
-async function downloadModels() {
- // Use dynamic import for ES modules in CommonJS
- const { pipeline, env } = await import('@huggingface/transformers')
-
- // Configure transformers.js to use local cache
- env.cacheDir = './models-cache'
- env.allowRemoteModels = true
-
- try {
- console.log('🧠 Brainy Model Downloader v2.8.0')
- console.log('===================================')
- console.log(` Model: ${MODEL_NAME}`)
- console.log(` Type: Q8 (optimized, 99% accuracy)`)
- console.log(` Cache: ${env.cacheDir}`)
- console.log('')
-
- // Create output directory
- await fs.mkdir(OUTPUT_DIR, { recursive: true })
-
- // Download Q8 model only
- console.log('📥 Downloading Q8 model (quantized, 33MB, 99% accuracy)...')
- await downloadModelVariant('q8')
-
- // Copy ALL model files from cache to our models directory
- console.log('📋 Copying model files to bundle directory...')
-
- const cacheDir = path.resolve(env.cacheDir)
- const outputDir = path.resolve(OUTPUT_DIR)
-
- console.log(` From: ${cacheDir}`)
- console.log(` To: ${outputDir}`)
-
- // Copy the entire cache directory structure to ensure we get ALL files
- // including tokenizer.json, config.json, and all ONNX model files
- const modelCacheDir = path.join(cacheDir, 'Xenova', 'all-MiniLM-L6-v2')
-
- if (await dirExists(modelCacheDir)) {
- const targetModelDir = path.join(outputDir, 'Xenova', 'all-MiniLM-L6-v2')
- console.log(` Copying complete model: Xenova/all-MiniLM-L6-v2`)
- await copyDirectory(modelCacheDir, targetModelDir)
- } else {
- throw new Error(`Model cache directory not found: ${modelCacheDir}`)
- }
-
- console.log('✅ Model bundling complete!')
- console.log(` Total size: ${await calculateDirectorySize(outputDir)} MB`)
- console.log(` Location: ${outputDir}`)
-
- // Create a marker file with downloaded model info
- const markerData = {
- model: MODEL_NAME,
- bundledAt: new Date().toISOString(),
- version: '2.8.0',
- downloadType: downloadType,
- models: {}
- }
-
- // Check which models were downloaded
- const fp32Path = path.join(outputDir, 'Xenova/all-MiniLM-L6-v2/onnx/model.onnx')
- const q8Path = path.join(outputDir, 'Xenova/all-MiniLM-L6-v2/onnx/model_quantized.onnx')
-
- if (await fileExists(fp32Path)) {
- const stats = await fs.stat(fp32Path)
- markerData.models.fp32 = {
- file: 'onnx/model.onnx',
- size: stats.size,
- sizeFormatted: `${Math.round(stats.size / (1024 * 1024))}MB`
- }
- }
-
- if (await fileExists(q8Path)) {
- const stats = await fs.stat(q8Path)
- markerData.models.q8 = {
- file: 'onnx/model_quantized.onnx',
- size: stats.size,
- sizeFormatted: `${Math.round(stats.size / (1024 * 1024))}MB`
- }
- }
-
- await fs.writeFile(
- path.join(outputDir, '.brainy-models-bundled'),
- JSON.stringify(markerData, null, 2)
- )
-
- console.log('')
- console.log('✅ Download complete! Available models:')
- if (markerData.models.fp32) {
- console.log(` • FP32: ${markerData.models.fp32.sizeFormatted} (full precision)`)
- }
- if (markerData.models.q8) {
- console.log(` • Q8: ${markerData.models.q8.sizeFormatted} (quantized, 75% smaller)`)
- }
- console.log('')
- console.log('Air-gap deployment ready! 🚀')
-
- } catch (error) {
- console.error('❌ Error downloading models:', error)
- process.exit(1)
- }
-}
-
-// Download a specific model variant
-async function downloadModelVariant(dtype) {
- const { pipeline } = await import('@huggingface/transformers')
-
- try {
- // Load the model to force download
- const extractor = await pipeline('feature-extraction', MODEL_NAME, {
- dtype: dtype,
- cache_dir: './models-cache'
- })
-
- // Test the model
- const testResult = await extractor(['Hello world!'], {
- pooling: 'mean',
- normalize: true
- })
-
- console.log(` ✅ ${dtype.toUpperCase()} model downloaded and tested (${testResult.data.length} dimensions)`)
-
- // Dispose to free memory
- if (extractor.dispose) {
- await extractor.dispose()
- }
-
- } catch (error) {
- console.error(` ❌ Failed to download ${dtype} model:`, error)
- throw error
- }
-}
-
-async function findModelDirectories(baseDir, modelName) {
- const dirs = []
-
- try {
- // Convert model name to expected directory structure
- const modelPath = modelName.replace('/', '--')
-
- async function searchDirectory(currentDir) {
- try {
- const entries = await fs.readdir(currentDir, { withFileTypes: true })
-
- for (const entry of entries) {
- if (entry.isDirectory()) {
- const fullPath = path.join(currentDir, entry.name)
-
- // Check if this directory contains model files
- if (entry.name.includes(modelPath) || entry.name === 'onnx') {
- const hasModelFiles = await containsModelFiles(fullPath)
- if (hasModelFiles) {
- dirs.push(fullPath)
- }
- }
-
- // Recursively search subdirectories
- await searchDirectory(fullPath)
- }
- }
- } catch (error) {
- // Ignore access errors
- }
- }
-
- await searchDirectory(baseDir)
- } catch (error) {
- console.warn('Warning: Error searching for model directories:', error)
- }
-
- return dirs
-}
-
-async function containsModelFiles(dir) {
- try {
- const files = await fs.readdir(dir)
- return files.some(file =>
- file.endsWith('.onnx') ||
- file.endsWith('.json') ||
- file === 'config.json' ||
- file === 'tokenizer.json'
- )
- } catch (error) {
- return false
- }
-}
-
-async function dirExists(dir) {
- try {
- const stats = await fs.stat(dir)
- return stats.isDirectory()
- } catch (error) {
- return false
- }
-}
-
-async function fileExists(file) {
- try {
- const stats = await fs.stat(file)
- return stats.isFile()
- } catch (error) {
- return false
- }
-}
-
-async function copyDirectory(src, dest) {
- await fs.mkdir(dest, { recursive: true })
- const entries = await fs.readdir(src, { withFileTypes: true })
-
- for (const entry of entries) {
- const srcPath = path.join(src, entry.name)
- const destPath = path.join(dest, entry.name)
-
- if (entry.isDirectory()) {
- await copyDirectory(srcPath, destPath)
- } else {
- await fs.copyFile(srcPath, destPath)
- }
- }
-}
-
-async function calculateDirectorySize(dir) {
- let size = 0
-
- async function calculateSize(currentDir) {
- try {
- const entries = await fs.readdir(currentDir, { withFileTypes: true })
-
- for (const entry of entries) {
- const fullPath = path.join(currentDir, entry.name)
-
- if (entry.isDirectory()) {
- await calculateSize(fullPath)
- } else {
- const stats = await fs.stat(fullPath)
- size += stats.size
- }
- }
- } catch (error) {
- // Ignore access errors
- }
- }
-
- await calculateSize(dir)
- return Math.round(size / (1024 * 1024))
-}
-
-// Run the download
-downloadModels().catch(error => {
- console.error('Fatal error:', error)
- process.exit(1)
-})
\ No newline at end of file
diff --git a/scripts/ensure-models.js b/scripts/ensure-models.js
deleted file mode 100644
index 536a2341..00000000
--- a/scripts/ensure-models.js
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env node
-/**
- * Ensures transformer models are available for production
- * This script handles model availability in multiple ways:
- * 1. Check if models exist locally
- * 2. Download from CDN if needed
- * 3. Verify model integrity
- */
-
-import { existsSync } from 'fs'
-import { readFile, mkdir, writeFile } from 'fs/promises'
-import { join, dirname } from 'path'
-import { createHash } from 'crypto'
-import { fileURLToPath } from 'url'
-
-const __dirname = dirname(fileURLToPath(import.meta.url))
-const PROJECT_ROOT = join(__dirname, '..')
-
-// Model configuration
-const MODEL_CONFIG = {
- name: 'Xenova/all-MiniLM-L6-v2',
- files: {
- 'onnx/model.onnx': {
- size: 90555481, // 86.3 MB
- sha256: 'expected_hash_here' // We'd compute this from actual model
- },
- 'tokenizer.json': {
- size: 711661,
- sha256: 'expected_hash_here'
- },
- 'tokenizer_config.json': {
- size: 366,
- sha256: 'expected_hash_here'
- },
- 'config.json': {
- size: 650,
- sha256: 'expected_hash_here'
- }
- }
-}
-
-// CDN URLs for model files (would be your own CDN in production)
-const CDN_BASE = 'https://cdn.soulcraft.com/models'
-
-async function ensureModels() {
- const modelsDir = join(PROJECT_ROOT, 'models', 'Xenova', 'all-MiniLM-L6-v2')
-
- console.log('🔍 Checking for transformer models...')
-
- // Check if all model files exist
- let missingFiles = []
- for (const [filePath, info] of Object.entries(MODEL_CONFIG.files)) {
- const fullPath = join(modelsDir, filePath)
- if (!existsSync(fullPath)) {
- missingFiles.push(filePath)
- }
- }
-
- if (missingFiles.length === 0) {
- console.log('✅ All model files present')
-
- // Optionally verify integrity
- if (process.env.VERIFY_MODELS === 'true') {
- console.log('🔐 Verifying model integrity...')
- // Add hash verification here
- }
-
- return true
- }
-
- console.log(`⚠️ Missing ${missingFiles.length} model files`)
-
- // In production, models should be pre-bundled
- if (process.env.NODE_ENV === 'production' && !process.env.ALLOW_MODEL_DOWNLOAD) {
- throw new Error(
- 'Critical: Transformer models not found in production. ' +
- 'Run "npm run download-models" during build stage.'
- )
- }
-
- // Development: offer to download
- if (process.env.CI !== 'true') {
- console.log('📥 Would download models from CDN in development')
- console.log(' Run: npm run download-models')
- }
-
- return false
-}
-
-// Export for use in main code
-export async function verifyModelsAvailable() {
- try {
- return await ensureModels()
- } catch (error) {
- console.error('❌ Model verification failed:', error.message)
- return false
- }
-}
-
-// Run if called directly
-if (import.meta.url === `file://${process.argv[1]}`) {
- ensureModels()
- .then(success => process.exit(success ? 0 : 1))
- .catch(error => {
- console.error(error)
- process.exit(1)
- })
-}
\ No newline at end of file
diff --git a/scripts/prepare-models.js b/scripts/prepare-models.js
deleted file mode 100644
index 7e99128e..00000000
--- a/scripts/prepare-models.js
+++ /dev/null
@@ -1,387 +0,0 @@
-#!/usr/bin/env node
-/**
- * Prepare Models Script
- *
- * Intelligently handles model preparation for different deployment scenarios:
- * 1. Development: Models download automatically on first use
- * 2. Docker/CI: Pre-download during build stage
- * 3. Serverless: Bundle with deployment package
- * 4. Production: Verify models exist, fail fast if missing
- */
-
-import { existsSync } from 'fs'
-import { readFile, mkdir, writeFile, stat } from 'fs/promises'
-import { join, dirname } from 'path'
-import { fileURLToPath } from 'url'
-import { pipeline, env } from '@huggingface/transformers'
-import { execSync } from 'child_process'
-import https from 'https'
-import { createWriteStream } from 'fs'
-import { promisify } from 'util'
-import { finished } from 'stream'
-
-const streamFinished = promisify(finished)
-const __dirname = dirname(fileURLToPath(import.meta.url))
-
-// Model configuration
-const MODEL_CONFIG = {
- name: 'Xenova/all-MiniLM-L6-v2',
- expectedFiles: [
- 'config.json',
- 'tokenizer.json',
- 'tokenizer_config.json',
- 'onnx/model.onnx'
- ],
- fallbackUrls: {
- // GitHub Releases (our backup)
- github: 'https://github.com/soulcraftlabs/brainy-models/releases/download/v1.0/all-MiniLM-L6-v2.tar.gz',
- // Future CDN
- cdn: 'https://models.soulcraft.com/brainy/all-MiniLM-L6-v2.tar.gz'
- }
-}
-
-class ModelPreparer {
- constructor() {
- this.modelsDir = join(__dirname, '..', 'models')
- this.modelPath = join(this.modelsDir, ...MODEL_CONFIG.name.split('/'))
- }
-
- /**
- * Main entry point - intelligently prepares models based on context
- */
- async prepare() {
- console.log('🧠 Brainy Model Preparation')
- console.log('===========================')
-
- // Detect deployment context
- const context = this.detectContext()
- console.log(`📍 Context: ${context}`)
-
- switch (context) {
- case 'production':
- return await this.prepareProduction()
- case 'docker':
- return await this.prepareDocker()
- case 'ci':
- return await this.prepareCI()
- case 'development':
- return await this.prepareDevelopment()
- default:
- return await this.prepareDefault()
- }
- }
-
- /**
- * Detect the deployment context
- */
- detectContext() {
- // Check environment variables
- if (process.env.NODE_ENV === 'production') return 'production'
- if (process.env.DOCKER_BUILD === 'true') return 'docker'
- if (process.env.CI === 'true') return 'ci'
- if (process.env.NODE_ENV === 'development') return 'development'
-
- // Check for Docker build context
- if (existsSync('/.dockerenv')) return 'docker'
-
- // Check for common CI indicators
- if (process.env.GITHUB_ACTIONS || process.env.GITLAB_CI) return 'ci'
-
- // Default to development
- return 'development'
- }
-
- /**
- * Production: Models MUST exist, fail fast if not
- */
- async prepareProduction() {
- console.log('🏭 Production mode - verifying models...')
-
- const modelExists = await this.verifyModels()
-
- if (!modelExists) {
- console.error('❌ CRITICAL: Models not found in production!')
- console.error(' Models must be pre-downloaded during build stage.')
- console.error(' Run: npm run download-models')
- process.exit(1)
- }
-
- console.log('✅ Models verified for production')
- return true
- }
-
- /**
- * Docker: Download models during build stage
- */
- async prepareDocker() {
- console.log('🐳 Docker build - downloading models...')
-
- // Check if already exists
- if (await this.verifyModels()) {
- console.log('✅ Models already present')
- return true
- }
-
- // Download models
- return await this.downloadModels()
- }
-
- /**
- * CI: Download models for testing
- */
- async prepareCI() {
- console.log('🔧 CI environment - downloading models for tests...')
-
- // Check cache first
- if (await this.checkCICache()) {
- console.log('✅ Using cached models')
- return true
- }
-
- // Download and cache
- const success = await this.downloadModels()
- if (success) {
- await this.saveCICache()
- }
- return success
- }
-
- /**
- * Development: Optional download, will auto-download on first use
- */
- async prepareDevelopment() {
- console.log('💻 Development mode')
-
- if (await this.verifyModels()) {
- console.log('✅ Models already downloaded')
- return true
- }
-
- console.log('ℹ️ Models will download automatically on first use')
- console.log(' To pre-download now: npm run download-models')
-
- // Ask if they want to download now
- if (process.stdout.isTTY && !process.env.SKIP_PROMPT) {
- const readline = await import('readline')
- const rl = readline.createInterface({
- input: process.stdin,
- output: process.stdout
- })
-
- return new Promise((resolve) => {
- rl.question('Download models now? (y/N): ', async (answer) => {
- rl.close()
- if (answer.toLowerCase() === 'y') {
- resolve(await this.downloadModels())
- } else {
- resolve(true)
- }
- })
- })
- }
-
- return true
- }
-
- /**
- * Default: Try to be smart about it
- */
- async prepareDefault() {
- console.log('🤖 Auto-detecting best approach...')
-
- if (await this.verifyModels()) {
- console.log('✅ Models found')
- return true
- }
-
- // If running as part of install, don't download
- if (process.env.npm_lifecycle_event === 'postinstall') {
- console.log('ℹ️ Skipping download during install (will download on first use)')
- return true
- }
-
- // Otherwise download
- return await this.downloadModels()
- }
-
- /**
- * Verify all required model files exist
- */
- async verifyModels() {
- for (const file of MODEL_CONFIG.expectedFiles) {
- const filePath = join(this.modelPath, file)
- if (!existsSync(filePath)) {
- return false
- }
- }
-
- // Verify model.onnx size (should be ~87MB)
- const modelOnnxPath = join(this.modelPath, 'onnx', 'model.onnx')
- if (existsSync(modelOnnxPath)) {
- const stats = await stat(modelOnnxPath)
- const sizeMB = Math.round(stats.size / (1024 * 1024))
- if (sizeMB < 80 || sizeMB > 100) {
- console.warn(`⚠️ Model size unexpected: ${sizeMB}MB (expected ~87MB)`)
- return false
- }
- }
-
- return true
- }
-
- /**
- * Download models with fallback sources
- */
- async downloadModels() {
- console.log('📥 Downloading transformer models...')
-
- // Try transformers.js first (Hugging Face)
- try {
- await this.downloadFromTransformers()
- console.log('✅ Downloaded from Hugging Face')
- return true
- } catch (error) {
- console.warn('⚠️ Hugging Face download failed:', error.message)
- }
-
- // Try GitHub releases
- try {
- await this.downloadFromGitHub()
- console.log('✅ Downloaded from GitHub')
- return true
- } catch (error) {
- console.warn('⚠️ GitHub download failed:', error.message)
- }
-
- // Try CDN
- try {
- await this.downloadFromCDN()
- console.log('✅ Downloaded from CDN')
- return true
- } catch (error) {
- console.warn('⚠️ CDN download failed:', error.message)
- }
-
- console.error('❌ All download sources failed')
- return false
- }
-
- /**
- * Download using transformers.js (official Hugging Face)
- */
- async downloadFromTransformers() {
- env.cacheDir = this.modelsDir
- env.allowRemoteModels = true
-
- console.log(' Source: Hugging Face')
- console.log(' Model:', MODEL_CONFIG.name)
-
- // Load pipeline to trigger download
- const extractor = await pipeline('feature-extraction', MODEL_CONFIG.name)
-
- // Test it works
- const test = await extractor('test', { pooling: 'mean', normalize: true })
- console.log(` ✓ Model test passed (dims: ${test.data.length})`)
-
- return true
- }
-
- /**
- * Download from GitHub releases (our backup)
- */
- async downloadFromGitHub() {
- const url = MODEL_CONFIG.fallbackUrls.github
- console.log(' Source: GitHub Releases')
-
- // Download tar.gz
- const tempFile = join(this.modelsDir, 'temp-model.tar.gz')
- await this.downloadFile(url, tempFile)
-
- // Extract
- await mkdir(this.modelPath, { recursive: true })
- execSync(`tar -xzf ${tempFile} -C ${this.modelPath}`, { stdio: 'inherit' })
-
- // Cleanup
- await unlink(tempFile)
-
- return true
- }
-
- /**
- * Download from CDN (future)
- */
- async downloadFromCDN() {
- const url = MODEL_CONFIG.fallbackUrls.cdn
- console.log(' Source: Soulcraft CDN')
-
- // Similar to GitHub approach
- throw new Error('CDN not yet available')
- }
-
- /**
- * Download a file from URL
- */
- async downloadFile(url, destination) {
- await mkdir(dirname(destination), { recursive: true })
-
- return new Promise((resolve, reject) => {
- const file = createWriteStream(destination)
-
- https.get(url, (response) => {
- if (response.statusCode !== 200) {
- reject(new Error(`HTTP ${response.statusCode}`))
- return
- }
-
- response.pipe(file)
-
- file.on('finish', () => {
- file.close()
- resolve()
- })
- }).on('error', reject)
- })
- }
-
- /**
- * Check CI cache for models
- */
- async checkCICache() {
- // GitHub Actions cache
- if (process.env.GITHUB_ACTIONS) {
- const cachePath = process.env.RUNNER_TEMP + '/brainy-models'
- if (existsSync(cachePath)) {
- // Copy from cache
- execSync(`cp -r ${cachePath}/* ${this.modelsDir}/`, { stdio: 'inherit' })
- return true
- }
- }
-
- return false
- }
-
- /**
- * Save models to CI cache
- */
- async saveCICache() {
- // GitHub Actions cache
- if (process.env.GITHUB_ACTIONS) {
- const cachePath = process.env.RUNNER_TEMP + '/brainy-models'
- await mkdir(cachePath, { recursive: true })
- execSync(`cp -r ${this.modelsDir}/* ${cachePath}/`, { stdio: 'inherit' })
- }
- }
-}
-
-// Run the preparer
-const preparer = new ModelPreparer()
-preparer.prepare()
- .then(success => {
- if (!success) {
- process.exit(1)
- }
- })
- .catch(error => {
- console.error('❌ Fatal error:', error)
- process.exit(1)
- })
\ No newline at end of file
diff --git a/scripts/setup-dev.sh b/scripts/setup-dev.sh
new file mode 100755
index 00000000..f4c221a6
--- /dev/null
+++ b/scripts/setup-dev.sh
@@ -0,0 +1,224 @@
+#!/bin/bash
+# Development environment setup script for Brainy
+#
+# This script installs all dependencies needed to build Brainy,
+# including the Candle WASM embedding engine.
+#
+# Usage:
+# ./scripts/setup-dev.sh
+#
+# Requirements:
+# - sudo access (for system packages)
+# - Internet connection
+
+set -euo pipefail
+
+# Colors
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m'
+
+echo -e "${BLUE}======================================${NC}"
+echo -e "${BLUE} Brainy Development Setup${NC}"
+echo -e "${BLUE}======================================${NC}"
+echo ""
+
+# Detect OS
+detect_os() {
+ if [[ "$OSTYPE" == "linux-gnu"* ]]; then
+ if command -v apt-get &> /dev/null; then
+ echo "debian"
+ elif command -v dnf &> /dev/null; then
+ echo "fedora"
+ elif command -v pacman &> /dev/null; then
+ echo "arch"
+ else
+ echo "linux-unknown"
+ fi
+ elif [[ "$OSTYPE" == "darwin"* ]]; then
+ echo "macos"
+ else
+ echo "unknown"
+ fi
+}
+
+OS=$(detect_os)
+echo -e "${GREEN}Detected OS: ${OS}${NC}"
+
+# Install system dependencies
+install_system_deps() {
+ echo -e "\n${YELLOW}Installing system dependencies...${NC}"
+
+ case $OS in
+ debian)
+ sudo apt-get update
+ sudo apt-get install -y build-essential pkg-config libssl-dev curl
+ ;;
+ fedora)
+ sudo dnf install -y gcc gcc-c++ make openssl-devel pkgconfig curl
+ ;;
+ arch)
+ sudo pacman -S --needed base-devel openssl pkg-config curl
+ ;;
+ macos)
+ if ! command -v gcc &> /dev/null; then
+ echo -e "${YELLOW}Installing Xcode command line tools...${NC}"
+ xcode-select --install 2>/dev/null || true
+ fi
+ ;;
+ *)
+ echo -e "${RED}Unknown OS. Please install build tools manually:${NC}"
+ echo " - C compiler (gcc or clang)"
+ echo " - pkg-config"
+ echo " - OpenSSL development headers"
+ exit 1
+ ;;
+ esac
+
+ echo -e "${GREEN}System dependencies installed.${NC}"
+}
+
+# Install Rust
+install_rust() {
+ echo -e "\n${YELLOW}Checking Rust installation...${NC}"
+
+ if command -v rustc &> /dev/null; then
+ RUST_VERSION=$(rustc --version)
+ echo -e "${GREEN}Rust already installed: ${RUST_VERSION}${NC}"
+ else
+ echo -e "${YELLOW}Installing Rust...${NC}"
+ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+ source "$HOME/.cargo/env"
+ echo -e "${GREEN}Rust installed: $(rustc --version)${NC}"
+ fi
+
+ # Ensure cargo env is loaded
+ if [ -f "$HOME/.cargo/env" ]; then
+ source "$HOME/.cargo/env"
+ fi
+}
+
+# Install Rust WASM tools
+install_wasm_tools() {
+ echo -e "\n${YELLOW}Installing WASM build tools...${NC}"
+
+ # Add WASM target
+ if ! rustup target list --installed | grep -q wasm32-unknown-unknown; then
+ echo "Adding wasm32-unknown-unknown target..."
+ rustup target add wasm32-unknown-unknown
+ else
+ echo -e "${GREEN}WASM target already installed.${NC}"
+ fi
+
+ # Install wasm-pack
+ if ! command -v wasm-pack &> /dev/null; then
+ echo "Installing wasm-pack..."
+ cargo install wasm-pack
+ else
+ echo -e "${GREEN}wasm-pack already installed: $(wasm-pack --version)${NC}"
+ fi
+}
+
+# Install Node.js dependencies
+install_node_deps() {
+ echo -e "\n${YELLOW}Checking Node.js...${NC}"
+
+ if ! command -v node &> /dev/null; then
+ echo -e "${RED}Node.js not found. Please install Node.js 20+ first.${NC}"
+ echo "Visit: https://nodejs.org/"
+ exit 1
+ fi
+
+ NODE_VERSION=$(node --version)
+ echo -e "${GREEN}Node.js: ${NODE_VERSION}${NC}"
+
+ echo -e "\n${YELLOW}Installing npm dependencies...${NC}"
+ npm install
+ echo -e "${GREEN}npm dependencies installed.${NC}"
+}
+
+# Download model files
+download_models() {
+ echo -e "\n${YELLOW}Downloading model files...${NC}"
+
+ MODEL_DIR="assets/models/all-MiniLM-L6-v2"
+
+ if [ -f "$MODEL_DIR/model.safetensors" ] && [ -f "$MODEL_DIR/tokenizer.json" ]; then
+ echo -e "${GREEN}Model files already present.${NC}"
+ return
+ fi
+
+ mkdir -p "$MODEL_DIR"
+
+ HF_URL="https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main"
+
+ echo "Downloading model.safetensors..."
+ curl -L "$HF_URL/model.safetensors" -o "$MODEL_DIR/model.safetensors"
+
+ echo "Downloading tokenizer.json..."
+ curl -L "$HF_URL/tokenizer.json" -o "$MODEL_DIR/tokenizer.json"
+
+ echo "Downloading config.json..."
+ curl -L "$HF_URL/config.json" -o "$MODEL_DIR/config.json"
+
+ echo -e "${GREEN}Model files downloaded.${NC}"
+}
+
+# Build Candle WASM
+build_candle() {
+ echo -e "\n${YELLOW}Building Candle WASM...${NC}"
+
+ if [ -f "src/embeddings/wasm/pkg/candle_embeddings_bg.wasm" ]; then
+ echo -e "${GREEN}Candle WASM already built. Use 'npm run build:candle' to rebuild.${NC}"
+ return
+ fi
+
+ npm run build:candle
+ echo -e "${GREEN}Candle WASM built.${NC}"
+}
+
+# Build TypeScript
+build_typescript() {
+ echo -e "\n${YELLOW}Building TypeScript...${NC}"
+ npm run build
+ echo -e "${GREEN}TypeScript built.${NC}"
+}
+
+# Run tests
+run_tests() {
+ echo -e "\n${YELLOW}Running tests...${NC}"
+ npm run test:unit
+ echo -e "${GREEN}Tests passed.${NC}"
+}
+
+# Main
+main() {
+ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
+ cd "$PROJECT_ROOT"
+
+ install_system_deps
+ install_rust
+ install_wasm_tools
+ install_node_deps
+ download_models
+ build_candle
+ build_typescript
+
+ echo ""
+ echo -e "${GREEN}======================================${NC}"
+ echo -e "${GREEN} Setup Complete!${NC}"
+ echo -e "${GREEN}======================================${NC}"
+ echo ""
+ echo "You can now:"
+ echo " npm run build # Build TypeScript"
+ echo " npm run build:candle # Rebuild Candle WASM"
+ echo " npm run test:all # Run all tests"
+ echo " npm run test:wasm # Test WASM embeddings"
+ echo " npm run test:bun:compile # Test Bun compile"
+ echo ""
+}
+
+main "$@"
diff --git a/src/brainy.ts b/src/brainy.ts
index 1015bac3..cd45f57f 100644
--- a/src/brainy.ts
+++ b/src/brainy.ts
@@ -1600,31 +1600,6 @@ export class Brainy implements BrainyInterface {
const params: FindParams =
typeof query === 'string' ? await this.parseNaturalQuery(query) : query
- // Phase 3: Automatic type inference for 40% latency reduction
- if (params.query && !params.type && this.index instanceof TypeAwareHNSWIndex) {
- // Import Phase 3 components dynamically
- const { getQueryPlanner } = await import('./query/typeAwareQueryPlanner.js')
- const planner = getQueryPlanner()
- const plan = await planner.planQuery(params.query)
-
- // Use inferred types if confidence is sufficient
- if (plan.confidence > 0.6) {
- params.type = plan.targetTypes.length === 1
- ? plan.targetTypes[0]
- : plan.targetTypes
-
- // Log for analytics (production-friendly)
- if (this.config.verbose) {
- console.log(
- `[Phase 3] Inferred types: ${plan.routing} ` +
- `(${plan.targetTypes.length} types, ` +
- `${(plan.confidence * 100).toFixed(0)}% confidence, ` +
- `${plan.estimatedSpeedup.toFixed(1)}x estimated speedup)`
- )
- }
- }
- }
-
// Zero-config validation - only enforces universal truths
const { validateFindParams, recordQueryPerformance } = await import('./utils/paramValidation.js')
validateFindParams(params)
diff --git a/src/embeddings/EmbeddingManager.ts b/src/embeddings/EmbeddingManager.ts
index 500130c3..ea88ffd7 100644
--- a/src/embeddings/EmbeddingManager.ts
+++ b/src/embeddings/EmbeddingManager.ts
@@ -2,11 +2,11 @@
* Unified Embedding Manager
*
* THE single source of truth for all embedding operations in Brainy.
- * Uses direct ONNX WASM inference for universal compatibility.
+ * Uses Candle WASM inference for universal compatibility.
*
* Features:
* - Singleton pattern ensures ONE model instance
- * - Direct ONNX WASM (no transformers.js dependency)
+ * - Candle WASM (no transformers.js or ONNX Runtime dependency)
* - Bundled model (no runtime downloads)
* - Works everywhere: Node.js, Bun, Bun --compile, browsers
* - Memory monitoring
@@ -34,7 +34,7 @@ let globalInitPromise: Promise | null = null
/**
* Unified Embedding Manager - Clean, simple, reliable
*
- * Now powered by direct ONNX WASM for universal compatibility.
+ * Now powered by Candle WASM for universal compatibility.
*/
export class EmbeddingManager {
private engine: WASMEmbeddingEngine
diff --git a/src/embeddings/candle-wasm/.cargo/config.toml b/src/embeddings/candle-wasm/.cargo/config.toml
new file mode 100644
index 00000000..04d5419b
--- /dev/null
+++ b/src/embeddings/candle-wasm/.cargo/config.toml
@@ -0,0 +1,7 @@
+# Cargo configuration for WASM builds
+#
+# This enables getrandom's WASM support for both 0.2 and 0.3 versions
+
+[target.wasm32-unknown-unknown]
+# Enable getrandom JS support for WASM (for getrandom 0.3)
+rustflags = ['--cfg', 'getrandom_backend="wasm_js"']
diff --git a/src/embeddings/candle-wasm/Cargo.toml b/src/embeddings/candle-wasm/Cargo.toml
new file mode 100644
index 00000000..6c9d3390
--- /dev/null
+++ b/src/embeddings/candle-wasm/Cargo.toml
@@ -0,0 +1,55 @@
+[package]
+name = "candle-embeddings"
+version = "0.1.0"
+edition = "2021"
+description = "WASM-based sentence embeddings using Candle and all-MiniLM-L6-v2"
+license = "MIT"
+
+[lib]
+crate-type = ["cdylib", "rlib"]
+
+[dependencies]
+# Candle ML framework
+candle-core = "0.8"
+candle-nn = "0.8"
+candle-transformers = "0.8"
+
+# HuggingFace tokenizer with WASM support
+# Use unstable_wasm feature which provides fancy-regex instead of onig
+tokenizers = { version = "0.20", default-features = false, features = ["unstable_wasm"] }
+
+# WASM bindings
+wasm-bindgen = "0.2"
+wasm-bindgen-futures = "0.4"
+js-sys = "0.3"
+web-sys = { version = "0.3", features = ["console"] }
+
+# Serialization for model loading
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+
+# Error handling
+anyhow = "1.0"
+
+# Async
+futures = "0.3"
+
+# WASM compatibility - force getrandom with js/wasm_js features
+# getrandom 0.2 (from tokenizers->rand) needs "js" feature
+# getrandom 0.3 (from candle->rand 0.9) needs "wasm_js" feature + rustflags
+[target.'cfg(target_arch = "wasm32")'.dependencies]
+getrandom_02 = { package = "getrandom", version = "0.2", features = ["js"] }
+getrandom = { version = "0.3", features = ["wasm_js"] }
+
+[dev-dependencies]
+wasm-bindgen-test = "0.3"
+
+[profile.release]
+opt-level = "z" # Optimize for size
+lto = true # Link-time optimization
+codegen-units = 1 # Single codegen unit for better optimization
+panic = "abort" # Abort on panic (smaller binary)
+
+[features]
+default = []
+simd = [] # Enable SIMD when browser support is available
diff --git a/src/embeddings/candle-wasm/src/lib.rs b/src/embeddings/candle-wasm/src/lib.rs
new file mode 100644
index 00000000..34f0fcab
--- /dev/null
+++ b/src/embeddings/candle-wasm/src/lib.rs
@@ -0,0 +1,402 @@
+//! Candle-based sentence embeddings for WASM
+//!
+//! This crate provides WASM-compatible sentence embeddings using HuggingFace's Candle framework.
+//! It supports the all-MiniLM-L6-v2 model for generating 384-dimensional embeddings.
+//!
+//! ## Features
+//! - Model weights embedded at compile time (zero runtime downloads)
+//! - Single WASM file contains everything
+//! - Works in all environments: Node.js, Bun, Bun compile, browsers
+//!
+//! ## Usage from JavaScript
+//! ```js
+//! import init, { EmbeddingEngine } from './candle_embeddings.js';
+//!
+//! await init();
+//! const engine = EmbeddingEngine.create_with_embedded_model();
+//!
+//! const embedding = engine.embed("Hello world");
+//! const embeddings = engine.embed_batch(["Hello", "World"]);
+//! ```
+
+use candle_core::{DType, Device, Tensor};
+use candle_nn::VarBuilder;
+use candle_transformers::models::bert::{BertModel, Config as BertConfig};
+use js_sys::{Array, Float32Array};
+use tokenizers::Tokenizer;
+use wasm_bindgen::prelude::*;
+
+/// Embedded model assets (compiled into WASM at build time)
+/// These files are included from assets/models/all-MiniLM-L6-v2/
+/// Path is relative to this lib.rs file: src/embeddings/candle-wasm/src/lib.rs
+const EMBEDDED_MODEL: &[u8] = include_bytes!("../../../../assets/models/all-MiniLM-L6-v2/model.safetensors");
+const EMBEDDED_TOKENIZER: &[u8] = include_bytes!("../../../../assets/models/all-MiniLM-L6-v2/tokenizer.json");
+const EMBEDDED_CONFIG: &[u8] = include_bytes!("../../../../assets/models/all-MiniLM-L6-v2/config.json");
+
+/// Model configuration constants for all-MiniLM-L6-v2
+const HIDDEN_SIZE: usize = 384;
+const MAX_SEQUENCE_LENGTH: usize = 256;
+
+/// Pooling strategy for aggregating token embeddings
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub enum PoolingStrategy {
+ /// Mean pooling over all tokens (default for sentence-transformers)
+ Mean,
+ /// Use the [CLS] token embedding
+ Cls,
+}
+
+/// WASM-compatible embedding engine
+#[wasm_bindgen]
+pub struct EmbeddingEngine {
+ model: Option,
+ tokenizer: Option,
+ device: Device,
+ pooling: PoolingStrategy,
+}
+
+#[wasm_bindgen]
+impl EmbeddingEngine {
+ /// Create a new embedding engine instance (not loaded)
+ #[wasm_bindgen(constructor)]
+ pub fn new() -> Self {
+ EmbeddingEngine {
+ model: None,
+ tokenizer: None,
+ device: Device::Cpu,
+ pooling: PoolingStrategy::Mean,
+ }
+ }
+
+ /// Create a new engine with the embedded model already loaded
+ /// This is the recommended way to create an engine - zero external dependencies
+ #[wasm_bindgen]
+ pub fn create_with_embedded_model() -> Result {
+ let mut engine = EmbeddingEngine::new();
+ engine.load_embedded()?;
+ Ok(engine)
+ }
+
+ /// Load the embedded model (compiled into WASM)
+ #[wasm_bindgen]
+ pub fn load_embedded(&mut self) -> Result<(), JsValue> {
+ self.load(EMBEDDED_MODEL, EMBEDDED_TOKENIZER, EMBEDDED_CONFIG)
+ }
+
+ /// Load the model and tokenizer from bytes (for custom models)
+ ///
+ /// # Arguments
+ /// * `model_bytes` - SafeTensors format model weights
+ /// * `tokenizer_bytes` - tokenizer.json contents
+ /// * `config_bytes` - config.json contents
+ #[wasm_bindgen]
+ pub fn load(
+ &mut self,
+ model_bytes: &[u8],
+ tokenizer_bytes: &[u8],
+ config_bytes: &[u8],
+ ) -> Result<(), JsValue> {
+ // Parse config
+ let config: BertConfig = serde_json::from_slice(config_bytes)
+ .map_err(|e| JsValue::from_str(&format!("Failed to parse config: {}", e)))?;
+
+ // Load model from SafeTensors
+ let tensors = candle_core::safetensors::load_buffer(model_bytes, &self.device)
+ .map_err(|e| JsValue::from_str(&format!("Failed to load safetensors: {}", e)))?;
+
+ let vb = VarBuilder::from_tensors(tensors, DType::F32, &self.device);
+
+ let model = BertModel::load(vb, &config)
+ .map_err(|e| JsValue::from_str(&format!("Failed to create model: {}", e)))?;
+
+ // Load tokenizer
+ let tokenizer = Tokenizer::from_bytes(tokenizer_bytes)
+ .map_err(|e| JsValue::from_str(&format!("Failed to load tokenizer: {:?}", e)))?;
+
+ self.model = Some(model);
+ self.tokenizer = Some(tokenizer);
+
+ Ok(())
+ }
+
+ /// Check if the engine is ready for inference
+ #[wasm_bindgen]
+ pub fn is_ready(&self) -> bool {
+ self.model.is_some() && self.tokenizer.is_some()
+ }
+
+ /// Generate embedding for a single text
+ ///
+ /// Returns a Float32Array of 384 dimensions
+ #[wasm_bindgen]
+ pub fn embed(&self, text: &str) -> Result {
+ let texts = vec![text.to_string()];
+ let embeddings = self.embed_internal(&texts)?;
+
+ if let Some(first) = embeddings.into_iter().next() {
+ let arr = Float32Array::new_with_length(first.len() as u32);
+ arr.copy_from(&first);
+ Ok(arr)
+ } else {
+ Err(JsValue::from_str("No embedding generated"))
+ }
+ }
+
+ /// Generate embeddings for multiple texts
+ ///
+ /// Takes a JavaScript Array of strings
+ /// Returns a JavaScript Array of Float32Array
+ #[wasm_bindgen]
+ pub fn embed_batch(&self, texts: &Array) -> Result {
+ // Convert JS Array to Vec
+ let mut rust_texts: Vec = Vec::with_capacity(texts.length() as usize);
+ for i in 0..texts.length() {
+ let item = texts.get(i);
+ let text = item
+ .as_string()
+ .ok_or_else(|| JsValue::from_str(&format!("Item at index {} is not a string", i)))?;
+ rust_texts.push(text);
+ }
+
+ if rust_texts.is_empty() {
+ return Ok(Array::new());
+ }
+
+ // Get embeddings
+ let embeddings = self.embed_internal(&rust_texts)?;
+
+ // Convert to JS Array of Float32Array
+ let result = Array::new_with_length(embeddings.len() as u32);
+ for (i, embedding) in embeddings.into_iter().enumerate() {
+ let arr = Float32Array::new_with_length(embedding.len() as u32);
+ arr.copy_from(&embedding);
+ result.set(i as u32, arr.into());
+ }
+
+ Ok(result)
+ }
+
+ /// Internal embedding function that works with Rust types
+ fn embed_internal(&self, texts: &[String]) -> Result>, JsValue> {
+ let model = self
+ .model
+ .as_ref()
+ .ok_or_else(|| JsValue::from_str("Model not loaded. Call load_embedded() first."))?;
+ let tokenizer = self
+ .tokenizer
+ .as_ref()
+ .ok_or_else(|| JsValue::from_str("Tokenizer not loaded. Call load_embedded() first."))?;
+
+ // Tokenize all texts
+ let encodings = tokenizer
+ .encode_batch(texts.to_vec(), true)
+ .map_err(|e| JsValue::from_str(&format!("Tokenization failed: {:?}", e)))?;
+
+ let batch_size = encodings.len();
+ if batch_size == 0 {
+ return Ok(vec![]);
+ }
+
+ // Find max sequence length in batch
+ let max_len = encodings
+ .iter()
+ .map(|e| e.get_ids().len())
+ .max()
+ .unwrap_or(0)
+ .min(MAX_SEQUENCE_LENGTH);
+
+ // Prepare input tensors
+ let mut input_ids: Vec = Vec::with_capacity(batch_size * max_len);
+ let mut attention_mask: Vec = Vec::with_capacity(batch_size * max_len);
+ let mut token_type_ids: Vec = Vec::with_capacity(batch_size * max_len);
+
+ for encoding in &encodings {
+ let ids = encoding.get_ids();
+ let mask = encoding.get_attention_mask();
+ let types = encoding.get_type_ids();
+
+ let seq_len = ids.len().min(max_len);
+
+ // Add tokens
+ for i in 0..seq_len {
+ input_ids.push(ids[i] as i64);
+ attention_mask.push(mask[i] as i64);
+ token_type_ids.push(types[i] as i64);
+ }
+
+ // Pad to max_len
+ for _ in seq_len..max_len {
+ input_ids.push(0);
+ attention_mask.push(0);
+ token_type_ids.push(0);
+ }
+ }
+
+ // Create tensors
+ let input_ids = Tensor::from_vec(input_ids, (batch_size, max_len), &self.device)
+ .map_err(|e| JsValue::from_str(&format!("Failed to create input_ids tensor: {}", e)))?;
+
+ let attention_mask_tensor =
+ Tensor::from_vec(attention_mask.clone(), (batch_size, max_len), &self.device)
+ .map_err(|e| {
+ JsValue::from_str(&format!("Failed to create attention_mask tensor: {}", e))
+ })?;
+
+ let token_type_ids = Tensor::from_vec(token_type_ids, (batch_size, max_len), &self.device)
+ .map_err(|e| {
+ JsValue::from_str(&format!("Failed to create token_type_ids tensor: {}", e))
+ })?;
+
+ // Run model inference
+ let output = model
+ .forward(&input_ids, &token_type_ids, Some(&attention_mask_tensor))
+ .map_err(|e| JsValue::from_str(&format!("Model inference failed: {}", e)))?;
+
+ // Apply pooling
+ let embeddings = match self.pooling {
+ PoolingStrategy::Mean => {
+ self.mean_pooling(&output, &attention_mask_tensor, batch_size, max_len)?
+ }
+ PoolingStrategy::Cls => {
+ // Get [CLS] token (first token) embeddings
+ output
+ .narrow(1, 0, 1)
+ .map_err(|e| JsValue::from_str(&format!("CLS extraction failed: {}", e)))?
+ .squeeze(1)
+ .map_err(|e| JsValue::from_str(&format!("Squeeze failed: {}", e)))?
+ }
+ };
+
+ // Normalize embeddings (L2 normalization)
+ let embeddings = self.l2_normalize(&embeddings)?;
+
+ // Convert to Vec>
+ let embeddings_flat = embeddings
+ .to_vec2::()
+ .map_err(|e| JsValue::from_str(&format!("Failed to extract embeddings: {}", e)))?;
+
+ Ok(embeddings_flat)
+ }
+
+ /// Mean pooling over token embeddings, weighted by attention mask
+ fn mean_pooling(
+ &self,
+ token_embeddings: &Tensor,
+ attention_mask: &Tensor,
+ batch_size: usize,
+ seq_len: usize,
+ ) -> Result {
+ // Expand attention mask to match embedding dimensions
+ // attention_mask: [batch, seq] -> [batch, seq, hidden]
+ let mask = attention_mask
+ .unsqueeze(2)
+ .map_err(|e| JsValue::from_str(&format!("Unsqueeze failed: {}", e)))?
+ .expand((batch_size, seq_len, HIDDEN_SIZE))
+ .map_err(|e| JsValue::from_str(&format!("Expand failed: {}", e)))?
+ .to_dtype(DType::F32)
+ .map_err(|e| JsValue::from_str(&format!("Dtype conversion failed: {}", e)))?;
+
+ // Multiply embeddings by mask
+ let masked = token_embeddings
+ .mul(&mask)
+ .map_err(|e| JsValue::from_str(&format!("Mask multiplication failed: {}", e)))?;
+
+ // Sum over sequence dimension
+ let summed = masked
+ .sum(1)
+ .map_err(|e| JsValue::from_str(&format!("Sum failed: {}", e)))?;
+
+ // Sum attention mask for normalization
+ let mask_sum = mask
+ .sum(1)
+ .map_err(|e| JsValue::from_str(&format!("Mask sum failed: {}", e)))?
+ .clamp(1e-9, f64::INFINITY)
+ .map_err(|e| JsValue::from_str(&format!("Clamp failed: {}", e)))?;
+
+ // Divide by mask sum
+ summed
+ .div(&mask_sum)
+ .map_err(|e| JsValue::from_str(&format!("Division failed: {}", e)))
+ }
+
+ /// L2 normalize embeddings
+ fn l2_normalize(&self, embeddings: &Tensor) -> Result {
+ let norm = embeddings
+ .sqr()
+ .map_err(|e| JsValue::from_str(&format!("Sqr failed: {}", e)))?
+ .sum_keepdim(1)
+ .map_err(|e| JsValue::from_str(&format!("Sum keepdim failed: {}", e)))?
+ .sqrt()
+ .map_err(|e| JsValue::from_str(&format!("Sqrt failed: {}", e)))?
+ .clamp(1e-12, f64::INFINITY)
+ .map_err(|e| JsValue::from_str(&format!("Norm clamp failed: {}", e)))?;
+
+ embeddings
+ .broadcast_div(&norm)
+ .map_err(|e| JsValue::from_str(&format!("Normalize division failed: {}", e)))
+ }
+
+ /// Get the embedding dimension (384 for all-MiniLM-L6-v2)
+ #[wasm_bindgen]
+ pub fn dimension(&self) -> usize {
+ HIDDEN_SIZE
+ }
+
+ /// Get the maximum sequence length
+ #[wasm_bindgen]
+ pub fn max_sequence_length(&self) -> usize {
+ MAX_SEQUENCE_LENGTH
+ }
+}
+
+impl Default for EmbeddingEngine {
+ fn default() -> Self {
+ Self::new()
+ }
+}
+
+/// Calculate cosine similarity between two embeddings
+#[wasm_bindgen]
+pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
+ if a.len() != b.len() || a.is_empty() {
+ return 0.0;
+ }
+
+ let mut dot = 0.0f32;
+ let mut norm_a = 0.0f32;
+ let mut norm_b = 0.0f32;
+
+ for i in 0..a.len() {
+ dot += a[i] * b[i];
+ norm_a += a[i] * a[i];
+ norm_b += b[i] * b[i];
+ }
+
+ if norm_a == 0.0 || norm_b == 0.0 {
+ return 0.0;
+ }
+
+ dot / (norm_a.sqrt() * norm_b.sqrt())
+}
+
+#[cfg(test)]
+mod tests {
+ use super::*;
+
+ #[test]
+ fn test_cosine_similarity() {
+ let a = vec![1.0, 0.0, 0.0];
+ let b = vec![1.0, 0.0, 0.0];
+ assert!((cosine_similarity(&a, &b) - 1.0).abs() < 1e-6);
+
+ let c = vec![0.0, 1.0, 0.0];
+ assert!(cosine_similarity(&a, &c).abs() < 1e-6);
+ }
+
+ #[test]
+ fn test_engine_creation() {
+ let engine = EmbeddingEngine::new();
+ assert!(!engine.is_ready());
+ assert_eq!(engine.dimension(), 384);
+ }
+}
diff --git a/src/embeddings/wasm/AssetLoader.ts b/src/embeddings/wasm/AssetLoader.ts
index 23285ad0..32fd0d93 100644
--- a/src/embeddings/wasm/AssetLoader.ts
+++ b/src/embeddings/wasm/AssetLoader.ts
@@ -1,13 +1,11 @@
/**
* Asset Loader
*
- * Resolves paths to model files (ONNX model, vocabulary) across environments.
- * Handles Node.js, Bun, and bundled scenarios.
+ * @deprecated This class is no longer used. Model weights are now embedded
+ * in the Candle WASM binary at compile time. Kept for backward compatibility.
*
- * Asset Resolution Order:
- * 1. Environment variable: BRAINY_MODEL_PATH
- * 2. Package-relative: node_modules/@soulcraft/brainy/assets/models/
- * 3. Project-relative: ./assets/models/
+ * Previously: Resolved paths to ONNX model files across environments.
+ * Now: Use CandleEmbeddingEngine which loads embedded model automatically.
*/
import { MODEL_CONSTANTS } from './types.js'
diff --git a/src/embeddings/wasm/CandleEmbeddingEngine.ts b/src/embeddings/wasm/CandleEmbeddingEngine.ts
new file mode 100644
index 00000000..1fe5d108
--- /dev/null
+++ b/src/embeddings/wasm/CandleEmbeddingEngine.ts
@@ -0,0 +1,312 @@
+/**
+ * Candle-based Embedding Engine
+ *
+ * TypeScript wrapper for the Candle WASM embedding module.
+ * Pure Rust/WASM implementation with model weights embedded at compile time.
+ * Works with Bun, Node.js, Bun compile, and browsers.
+ *
+ * Key features:
+ * - Model weights embedded in WASM at compile time (zero runtime downloads)
+ * - Single WASM file contains everything (~90MB)
+ * - Works in all environments: Node.js, Bun, Bun compile, browsers
+ * - Tokenization, mean pooling, and normalization in Rust
+ */
+
+import { EmbeddingResult, EngineStats, MODEL_CONSTANTS } from './types.js'
+
+// Type declaration for Bun global
+declare const Bun: {
+ file(path: string): { arrayBuffer(): Promise }
+} | undefined
+
+// Type definitions for the WASM module
+interface CandleWasmModule {
+ EmbeddingEngine: {
+ new (): CandleEngineInstance
+ create_with_embedded_model(): CandleEngineInstance
+ }
+ cosine_similarity: (a: Float32Array, b: Float32Array) => number
+}
+
+interface CandleEngineInstance {
+ load_embedded(): void
+ load(modelBytes: Uint8Array, tokenizerBytes: Uint8Array, configBytes: Uint8Array): void
+ is_ready(): boolean
+ embed(text: string): Float32Array
+ embed_batch(texts: string[]): Float32Array[]
+ dimension(): number
+ max_sequence_length(): number
+ free(): void
+}
+
+// Global singleton
+let globalInstance: CandleEmbeddingEngine | null = null
+let globalInitPromise: Promise | null = null
+
+/**
+ * Candle-based embedding engine
+ *
+ * Uses the Candle ML framework (Rust/WASM) for inference.
+ * Model weights are embedded in the WASM binary - no external files needed.
+ * Supports all-MiniLM-L6-v2 with 384-dimensional embeddings.
+ */
+export class CandleEmbeddingEngine {
+ private wasmModule: CandleWasmModule | null = null
+ private engine: CandleEngineInstance | null = null
+ private initialized = false
+ private embedCount = 0
+ private totalProcessingTimeMs = 0
+
+ private constructor() {
+ // Private constructor for singleton
+ }
+
+ /**
+ * Get the singleton instance
+ */
+ static getInstance(): CandleEmbeddingEngine {
+ if (!globalInstance) {
+ globalInstance = new CandleEmbeddingEngine()
+ }
+ return globalInstance
+ }
+
+ /**
+ * Initialize the embedding engine
+ */
+ async initialize(): Promise {
+ if (this.initialized) {
+ return
+ }
+
+ if (globalInitPromise) {
+ await globalInitPromise
+ return
+ }
+
+ globalInitPromise = this.performInit()
+
+ try {
+ await globalInitPromise
+ } finally {
+ globalInitPromise = null
+ }
+ }
+
+ /**
+ * Perform actual initialization
+ *
+ * Model weights are embedded in WASM - no file loading required.
+ */
+ private async performInit(): Promise {
+ const startTime = Date.now()
+ console.log('🚀 Initializing Candle Embedding Engine...')
+
+ try {
+ // Load the WASM module
+ console.log('📦 Loading Candle WASM module (includes embedded model)...')
+ const wasmModule = await this.loadWasmModule()
+ this.wasmModule = wasmModule
+
+ // Create engine with embedded model - no external files needed!
+ console.log('🧠 Creating engine with embedded model...')
+ this.engine = wasmModule.EmbeddingEngine.create_with_embedded_model()
+
+ if (!this.engine.is_ready()) {
+ throw new Error('Engine failed to initialize')
+ }
+
+ this.initialized = true
+ const initTime = Date.now() - startTime
+ console.log(`✅ Candle Embedding Engine ready in ${initTime}ms`)
+ } catch (error) {
+ this.initialized = false
+ this.engine = null
+ this.wasmModule = null
+ throw new Error(
+ `Failed to initialize Candle Embedding Engine: ${error instanceof Error ? error.message : String(error)}`
+ )
+ }
+ }
+
+ /**
+ * Load the WASM module
+ *
+ * The WASM file contains everything: runtime code + model weights.
+ */
+ private async loadWasmModule(): Promise {
+ try {
+ // Dynamic import of the WASM package
+ const wasmPkg = await import('./pkg/candle_embeddings.js')
+
+ // Determine if we're in Node.js or browser
+ const isNode = typeof process !== 'undefined' && process.versions?.node
+
+ if (isNode) {
+ // Server-side: load WASM bytes from file and use initSync
+ const path = await import('node:path')
+ const { fileURLToPath } = await import('node:url')
+
+ const thisDir = path.dirname(fileURLToPath(import.meta.url))
+ const wasmPath = path.join(thisDir, 'pkg', 'candle_embeddings_bg.wasm')
+
+ // Check if running in Bun (for Bun.file() support in compiled binaries)
+ const isBun = typeof Bun !== 'undefined'
+ let wasmBytes: Buffer | ArrayBuffer
+
+ if (isBun) {
+ // Bun runtime or compiled: Use Bun.file() which works in compiled binaries
+ wasmBytes = await Bun.file(wasmPath).arrayBuffer()
+ } else {
+ // Node.js: Use fs.readFileSync()
+ const fs = await import('node:fs')
+ if (!fs.existsSync(wasmPath)) {
+ throw new Error(`WASM file not found: ${wasmPath}`)
+ }
+ wasmBytes = fs.readFileSync(wasmPath)
+ }
+
+ wasmPkg.initSync({ module: wasmBytes })
+ } else {
+ // In browser: use default async init which uses fetch
+ await wasmPkg.default()
+ }
+
+ return wasmPkg as unknown as CandleWasmModule
+ } catch (error) {
+ throw new Error(
+ `Failed to load Candle WASM module. Make sure to run 'npm run build:candle' first. ` +
+ `Error: ${error instanceof Error ? error.message : String(error)}`
+ )
+ }
+ }
+
+ /**
+ * Generate embedding for text
+ */
+ async embed(text: string): Promise {
+ const result = await this.embedWithMetadata(text)
+ return result.embedding
+ }
+
+ /**
+ * Generate embedding with metadata
+ */
+ async embedWithMetadata(text: string): Promise {
+ if (!this.initialized) {
+ await this.initialize()
+ }
+
+ if (!this.engine) {
+ throw new Error('Engine not properly initialized')
+ }
+
+ const startTime = Date.now()
+
+ const embedding = this.engine.embed(text)
+ const embeddingArray = Array.from(embedding)
+
+ const processingTimeMs = Date.now() - startTime
+ this.embedCount++
+ this.totalProcessingTimeMs += processingTimeMs
+
+ return {
+ embedding: embeddingArray,
+ tokenCount: 0, // Candle handles tokenization internally
+ processingTimeMs,
+ }
+ }
+
+ /**
+ * Batch embed multiple texts
+ */
+ async embedBatch(texts: string[]): Promise {
+ if (!this.initialized) {
+ await this.initialize()
+ }
+
+ if (!this.engine) {
+ throw new Error('Engine not properly initialized')
+ }
+
+ if (texts.length === 0) {
+ return []
+ }
+
+ const embeddings = this.engine.embed_batch(texts)
+ this.embedCount += texts.length
+
+ return embeddings.map((e) => Array.from(e))
+ }
+
+ /**
+ * Check if initialized
+ */
+ isInitialized(): boolean {
+ return this.initialized
+ }
+
+ /**
+ * Get engine statistics
+ */
+ getStats(): EngineStats {
+ return {
+ initialized: this.initialized,
+ embedCount: this.embedCount,
+ totalProcessingTimeMs: this.totalProcessingTimeMs,
+ avgProcessingTimeMs: this.embedCount > 0 ? this.totalProcessingTimeMs / this.embedCount : 0,
+ modelName: MODEL_CONSTANTS.MODEL_NAME,
+ }
+ }
+
+ /**
+ * Dispose and free resources
+ */
+ async dispose(): Promise {
+ if (this.engine) {
+ this.engine.free()
+ this.engine = null
+ }
+ this.wasmModule = null
+ this.initialized = false
+ }
+
+ /**
+ * Reset singleton (for testing)
+ */
+ static resetInstance(): void {
+ if (globalInstance) {
+ globalInstance.dispose()
+ }
+ globalInstance = null
+ globalInitPromise = null
+ }
+}
+
+/**
+ * Calculate cosine similarity between two embeddings
+ */
+export function cosineSimilarity(a: number[], b: number[]): number {
+ if (a.length !== b.length || a.length === 0) {
+ return 0
+ }
+
+ let dot = 0
+ let normA = 0
+ let normB = 0
+
+ for (let i = 0; i < a.length; i++) {
+ dot += a[i] * b[i]
+ normA += a[i] * a[i]
+ normB += b[i] * b[i]
+ }
+
+ if (normA === 0 || normB === 0) {
+ return 0
+ }
+
+ return dot / (Math.sqrt(normA) * Math.sqrt(normB))
+}
+
+// Export singleton access
+export const candleEmbeddingEngine = CandleEmbeddingEngine.getInstance()
diff --git a/src/embeddings/wasm/ONNXInferenceEngine.ts b/src/embeddings/wasm/ONNXInferenceEngine.ts
deleted file mode 100644
index 0f9d7b1a..00000000
--- a/src/embeddings/wasm/ONNXInferenceEngine.ts
+++ /dev/null
@@ -1,193 +0,0 @@
-/**
- * ONNX Inference Engine
- *
- * Direct ONNX Runtime Web wrapper for running model inference.
- * Uses WASM backend for universal compatibility (Node.js, Bun, Browser).
- *
- * This replaces transformers.js dependency with direct ONNX control.
- */
-
-import * as ort from 'onnxruntime-web'
-import { InferenceConfig, MODEL_CONSTANTS } from './types.js'
-
-// Configure ONNX Runtime for WASM-only
-ort.env.wasm.numThreads = 1 // Single-threaded for stability
-ort.env.wasm.simd = true // Enable SIMD where available
-
-/**
- * ONNX Inference Engine using onnxruntime-web
- */
-export class ONNXInferenceEngine {
- private session: ort.InferenceSession | null = null
- private initialized = false
- private modelPath: string
- private config: InferenceConfig
-
- constructor(config: Partial = {}) {
- this.modelPath = config.modelPath ?? ''
- this.config = {
- modelPath: this.modelPath,
- numThreads: config.numThreads ?? 1,
- enableSimd: config.enableSimd ?? true,
- enableCpuMemArena: config.enableCpuMemArena ?? false,
- }
- }
-
- /**
- * Initialize the ONNX session
- */
- async initialize(modelPath?: string): Promise {
- if (this.initialized && this.session) {
- return
- }
-
- const path = modelPath ?? this.modelPath
- if (!path) {
- throw new Error('Model path is required')
- }
-
- try {
- // Configure session options
- const sessionOptions: ort.InferenceSession.SessionOptions = {
- executionProviders: ['wasm'],
- graphOptimizationLevel: 'all',
- enableCpuMemArena: this.config.enableCpuMemArena,
- // Additional WASM-specific options
- executionMode: 'sequential',
- }
-
- // Load model from file path or URL
- this.session = await ort.InferenceSession.create(path, sessionOptions)
-
- this.initialized = true
- } catch (error) {
- this.initialized = false
- this.session = null
- throw new Error(
- `Failed to initialize ONNX session: ${error instanceof Error ? error.message : String(error)}`
- )
- }
- }
-
- /**
- * Run inference on tokenized input
- *
- * @param inputIds - Token IDs [batchSize, seqLen]
- * @param attentionMask - Attention mask [batchSize, seqLen]
- * @param tokenTypeIds - Token type IDs [batchSize, seqLen] (optional, defaults to zeros)
- * @returns Hidden states [batchSize, seqLen, hiddenSize]
- */
- async infer(
- inputIds: number[][],
- attentionMask: number[][],
- tokenTypeIds?: number[][]
- ): Promise {
- if (!this.session) {
- throw new Error('Session not initialized. Call initialize() first.')
- }
-
- const batchSize = inputIds.length
- const seqLen = inputIds[0].length
-
- // Convert to BigInt64Array (ONNX int64 type)
- const inputIdsFlat = new BigInt64Array(batchSize * seqLen)
- const attentionMaskFlat = new BigInt64Array(batchSize * seqLen)
- const tokenTypeIdsFlat = new BigInt64Array(batchSize * seqLen)
-
- for (let b = 0; b < batchSize; b++) {
- for (let s = 0; s < seqLen; s++) {
- const idx = b * seqLen + s
- inputIdsFlat[idx] = BigInt(inputIds[b][s])
- attentionMaskFlat[idx] = BigInt(attentionMask[b][s])
- tokenTypeIdsFlat[idx] = tokenTypeIds
- ? BigInt(tokenTypeIds[b][s])
- : BigInt(0)
- }
- }
-
- // Create ONNX tensors
- const inputIdsTensor = new ort.Tensor('int64', inputIdsFlat, [batchSize, seqLen])
- const attentionMaskTensor = new ort.Tensor('int64', attentionMaskFlat, [batchSize, seqLen])
- const tokenTypeIdsTensor = new ort.Tensor('int64', tokenTypeIdsFlat, [batchSize, seqLen])
-
- try {
- // Run inference
- const feeds = {
- input_ids: inputIdsTensor,
- attention_mask: attentionMaskTensor,
- token_type_ids: tokenTypeIdsTensor,
- }
-
- const results = await this.session.run(feeds)
-
- // Extract last_hidden_state (the output we need for mean pooling)
- // Model outputs: last_hidden_state [batch, seq, hidden] and pooler_output [batch, hidden]
- const output = results.last_hidden_state ?? results.token_embeddings
-
- if (!output) {
- throw new Error('Model did not return expected output tensor')
- }
-
- return output.data as Float32Array
- } finally {
- // Dispose tensors to free memory
- inputIdsTensor.dispose()
- attentionMaskTensor.dispose()
- tokenTypeIdsTensor.dispose()
- }
- }
-
- /**
- * Infer single sequence (convenience method)
- */
- async inferSingle(
- inputIds: number[],
- attentionMask: number[],
- tokenTypeIds?: number[]
- ): Promise {
- return this.infer(
- [inputIds],
- [attentionMask],
- tokenTypeIds ? [tokenTypeIds] : undefined
- )
- }
-
- /**
- * Check if initialized
- */
- isInitialized(): boolean {
- return this.initialized
- }
-
- /**
- * Get model input/output names (for debugging)
- */
- getModelInfo(): { inputs: readonly string[]; outputs: readonly string[] } | null {
- if (!this.session) {
- return null
- }
-
- return {
- inputs: this.session.inputNames,
- outputs: this.session.outputNames,
- }
- }
-
- /**
- * Dispose of the session and free resources
- */
- async dispose(): Promise {
- if (this.session) {
- // Release the session
- this.session = null
- }
- this.initialized = false
- }
-}
-
-/**
- * Create an inference engine with default configuration
- */
-export function createInferenceEngine(modelPath: string): ONNXInferenceEngine {
- return new ONNXInferenceEngine({ modelPath })
-}
diff --git a/src/embeddings/wasm/WASMEmbeddingEngine.ts b/src/embeddings/wasm/WASMEmbeddingEngine.ts
index 3675daa8..5adb2baa 100644
--- a/src/embeddings/wasm/WASMEmbeddingEngine.ts
+++ b/src/embeddings/wasm/WASMEmbeddingEngine.ts
@@ -1,25 +1,23 @@
/**
* WASM Embedding Engine
*
- * The main embedding engine that combines all components:
- * - WordPieceTokenizer: Text → Token IDs
- * - ONNXInferenceEngine: Token IDs → Hidden States
- * - EmbeddingPostProcessor: Hidden States → Normalized Embedding
- *
- * This replaces transformers.js with a clean, production-grade implementation.
+ * The main embedding engine using Candle (Rust/WASM) for inference.
+ * This provides sentence embeddings using the all-MiniLM-L6-v2 model.
*
* Features:
* - Singleton pattern (one model instance)
* - Lazy initialization
* - Batch processing support
- * - Zero runtime dependencies
+ * - Works with Bun compile (no dynamic imports)
+ * - Pure WASM - no native dependencies
+ *
+ * Migration from ONNX Runtime:
+ * This implementation replaces the previous ONNX-based engine with Candle WASM.
+ * The interface remains identical for backward compatibility.
*/
-import { WordPieceTokenizer } from './WordPieceTokenizer.js'
-import { ONNXInferenceEngine } from './ONNXInferenceEngine.js'
-import { EmbeddingPostProcessor } from './EmbeddingPostProcessor.js'
-import { getAssetLoader } from './AssetLoader.js'
-import { EmbeddingResult, EngineStats, MODEL_CONSTANTS } from './types.js'
+import { CandleEmbeddingEngine } from './CandleEmbeddingEngine.js'
+import { EmbeddingResult, EngineStats } from './types.js'
// Global singleton instance
let globalInstance: WASMEmbeddingEngine | null = null
@@ -27,17 +25,17 @@ let globalInitPromise: Promise | null = null
/**
* WASM-based embedding engine
+ *
+ * Uses Candle (HuggingFace's Rust ML framework) for inference.
+ * Supports all-MiniLM-L6-v2 with 384-dimensional embeddings.
*/
export class WASMEmbeddingEngine {
- private tokenizer: WordPieceTokenizer | null = null
- private inference: ONNXInferenceEngine | null = null
- private postProcessor: EmbeddingPostProcessor | null = null
+ private candleEngine: CandleEmbeddingEngine
private initialized = false
- private embedCount = 0
- private totalProcessingTimeMs = 0
private constructor() {
- // Private constructor for singleton
+ // Get the Candle engine singleton
+ this.candleEngine = CandleEmbeddingEngine.getInstance()
}
/**
@@ -51,7 +49,7 @@ export class WASMEmbeddingEngine {
}
/**
- * Initialize all components
+ * Initialize the engine
*/
async initialize(): Promise {
// Already initialized
@@ -79,178 +77,50 @@ export class WASMEmbeddingEngine {
* Perform actual initialization
*/
private async performInit(): Promise {
- const startTime = Date.now()
- console.log('🚀 Initializing WASM Embedding Engine...')
-
- try {
- const assetLoader = getAssetLoader()
-
- // Verify assets exist
- const verification = await assetLoader.verifyAssets()
- if (!verification.valid) {
- throw new Error(
- `Missing model assets:\n${verification.errors.join('\n')}\n\n` +
- `Expected model at: ${verification.modelPath}\n` +
- `Expected vocab at: ${verification.vocabPath}\n\n` +
- `Run 'npm run download-model' to download the model files.`
- )
- }
-
- // Load vocabulary and create tokenizer
- console.log('📖 Loading vocabulary...')
- const vocab = await assetLoader.loadVocab()
- this.tokenizer = new WordPieceTokenizer(vocab)
- console.log(`✅ Vocabulary loaded: ${this.tokenizer.vocabSize} tokens`)
-
- // Initialize ONNX inference engine
- console.log('🧠 Loading ONNX model...')
- const modelPath = await assetLoader.getModelPath()
- this.inference = new ONNXInferenceEngine({ modelPath })
- await this.inference.initialize(modelPath)
- console.log('✅ ONNX model loaded')
-
- // Create post-processor
- this.postProcessor = new EmbeddingPostProcessor(MODEL_CONSTANTS.HIDDEN_SIZE)
-
- this.initialized = true
- const initTime = Date.now() - startTime
- console.log(`✅ WASM Embedding Engine ready in ${initTime}ms`)
-
- } catch (error) {
- this.initialized = false
- this.tokenizer = null
- this.inference = null
- this.postProcessor = null
- throw new Error(
- `Failed to initialize WASM Embedding Engine: ${error instanceof Error ? error.message : String(error)}`
- )
- }
+ await this.candleEngine.initialize()
+ this.initialized = true
}
/**
* Generate embedding for text
*/
async embed(text: string): Promise {
- const result = await this.embedWithMetadata(text)
- return result.embedding
+ return this.candleEngine.embed(text)
}
/**
* Generate embedding with metadata
*/
async embedWithMetadata(text: string): Promise {
- // Ensure initialized
- if (!this.initialized) {
- await this.initialize()
- }
-
- if (!this.tokenizer || !this.inference || !this.postProcessor) {
- throw new Error('Engine not properly initialized')
- }
-
- const startTime = Date.now()
-
- // 1. Tokenize
- const tokenized = this.tokenizer.encode(text)
-
- // 2. Run inference
- const hiddenStates = await this.inference.inferSingle(
- tokenized.inputIds,
- tokenized.attentionMask,
- tokenized.tokenTypeIds
- )
-
- // 3. Post-process (mean pool + normalize)
- const embedding = this.postProcessor.process(
- hiddenStates,
- tokenized.attentionMask,
- tokenized.inputIds.length
- )
-
- const processingTimeMs = Date.now() - startTime
- this.embedCount++
- this.totalProcessingTimeMs += processingTimeMs
-
- return {
- embedding: Array.from(embedding),
- tokenCount: tokenized.tokenCount,
- processingTimeMs,
- }
+ return this.candleEngine.embedWithMetadata(text)
}
/**
* Batch embed multiple texts
*/
async embedBatch(texts: string[]): Promise {
- // Ensure initialized
- if (!this.initialized) {
- await this.initialize()
- }
-
- if (!this.tokenizer || !this.inference || !this.postProcessor) {
- throw new Error('Engine not properly initialized')
- }
-
- if (texts.length === 0) {
- return []
- }
-
- // Tokenize all texts
- const batch = this.tokenizer.encodeBatch(texts)
- const seqLen = batch.inputIds[0].length
-
- // Run batch inference
- const hiddenStates = await this.inference.infer(
- batch.inputIds,
- batch.attentionMask,
- batch.tokenTypeIds
- )
-
- // Post-process each result
- const embeddings = this.postProcessor.processBatch(
- hiddenStates,
- batch.attentionMask,
- texts.length,
- seqLen
- )
-
- this.embedCount += texts.length
-
- return embeddings.map(e => Array.from(e))
+ return this.candleEngine.embedBatch(texts)
}
/**
* Check if initialized
*/
isInitialized(): boolean {
- return this.initialized
+ return this.initialized && this.candleEngine.isInitialized()
}
/**
* Get engine statistics
*/
getStats(): EngineStats {
- return {
- initialized: this.initialized,
- embedCount: this.embedCount,
- totalProcessingTimeMs: this.totalProcessingTimeMs,
- avgProcessingTimeMs: this.embedCount > 0
- ? this.totalProcessingTimeMs / this.embedCount
- : 0,
- modelName: MODEL_CONSTANTS.MODEL_NAME,
- }
+ return this.candleEngine.getStats()
}
/**
* Dispose and free resources
*/
async dispose(): Promise {
- if (this.inference) {
- await this.inference.dispose()
- this.inference = null
- }
- this.tokenizer = null
- this.postProcessor = null
+ await this.candleEngine.dispose()
this.initialized = false
}
@@ -263,6 +133,7 @@ export class WASMEmbeddingEngine {
}
globalInstance = null
globalInitPromise = null
+ CandleEmbeddingEngine.resetInstance()
}
}
diff --git a/src/embeddings/wasm/index.ts b/src/embeddings/wasm/index.ts
index f5b01bb8..014f5bb5 100644
--- a/src/embeddings/wasm/index.ts
+++ b/src/embeddings/wasm/index.ts
@@ -1,11 +1,15 @@
/**
* WASM Embedding Engine - Public Exports
*
- * Clean, production-grade embedding engine using direct ONNX WASM.
- * No transformers.js dependency, no runtime downloads, works everywhere.
+ * Clean, production-grade embedding engine using Candle (Rust/WASM).
+ * No ONNX Runtime dependency, no dynamic imports, works everywhere.
+ *
+ * Bun Compile Support:
+ * When compiled with `bun build --compile`, the WASM module is automatically
+ * embedded into the binary. No external files or runtime downloads needed.
*/
-// Main engine
+// Main engine (delegates to Candle)
export {
WASMEmbeddingEngine,
wasmEmbeddingEngine,
@@ -14,9 +18,15 @@ export {
getEmbeddingStats,
} from './WASMEmbeddingEngine.js'
-// Components (for advanced use)
+// Candle engine (direct access)
+export {
+ CandleEmbeddingEngine,
+ candleEmbeddingEngine,
+ cosineSimilarity,
+} from './CandleEmbeddingEngine.js'
+
+// Legacy components (for backward compatibility - not needed with Candle)
export { WordPieceTokenizer, createTokenizer } from './WordPieceTokenizer.js'
-export { ONNXInferenceEngine, createInferenceEngine } from './ONNXInferenceEngine.js'
export { EmbeddingPostProcessor, createPostProcessor } from './EmbeddingPostProcessor.js'
export { AssetLoader, getAssetLoader, createAssetLoader } from './AssetLoader.js'
diff --git a/src/embeddings/wasm/types.ts b/src/embeddings/wasm/types.ts
index cebc666f..f59b2f87 100644
--- a/src/embeddings/wasm/types.ts
+++ b/src/embeddings/wasm/types.ts
@@ -1,11 +1,13 @@
/**
* Type definitions for WASM Embedding Engine
*
- * Clean, production-grade types for direct ONNX WASM embeddings.
+ * Clean, production-grade types for Candle WASM embeddings.
+ * Model weights are embedded in WASM at compile time.
*/
/**
* Tokenizer configuration for WordPiece
+ * @deprecated Tokenization now happens in Rust/WASM - kept for backward compatibility
*/
export interface TokenizerConfig {
/** Vocabulary mapping word → token ID */
@@ -18,7 +20,7 @@ export interface TokenizerConfig {
sepTokenId: number
/** [PAD] token ID (0 for BERT-based models) */
padTokenId: number
- /** Maximum sequence length (512 for all-MiniLM-L6-v2) */
+ /** Maximum sequence length (256 for all-MiniLM-L6-v2 in Candle) */
maxLength: number
/** Whether to lowercase input (true for uncased models) */
doLowerCase: boolean
@@ -26,6 +28,7 @@ export interface TokenizerConfig {
/**
* Result of tokenization
+ * @deprecated Tokenization now happens in Rust/WASM - kept for backward compatibility
*/
export interface TokenizedInput {
/** Token IDs including [CLS] and [SEP] */
@@ -39,10 +42,11 @@ export interface TokenizedInput {
}
/**
- * ONNX inference engine configuration
+ * Inference engine configuration
+ * @deprecated Model is now embedded in WASM - kept for backward compatibility
*/
export interface InferenceConfig {
- /** Path to ONNX model file */
+ /** Path to model file (not used with embedded model) */
modelPath: string
/** Path to WASM files directory */
wasmPath?: string
@@ -50,7 +54,7 @@ export interface InferenceConfig {
numThreads: number
/** Enable SIMD if available */
enableSimd: boolean
- /** Enable CPU memory arena (false for memory efficiency) */
+ /** Enable CPU memory arena */
enableCpuMemArena: boolean
}
@@ -60,7 +64,7 @@ export interface InferenceConfig {
export interface EmbeddingResult {
/** 384-dimensional embedding vector */
embedding: number[]
- /** Number of tokens processed */
+ /** Number of tokens processed (0 when using Candle - handled internally) */
tokenCount: number
/** Processing time in milliseconds */
processingTimeMs: number
@@ -116,7 +120,7 @@ export const SPECIAL_TOKENS = {
*/
export const MODEL_CONSTANTS = {
HIDDEN_SIZE: 384,
- MAX_SEQUENCE_LENGTH: 512,
+ MAX_SEQUENCE_LENGTH: 256, // Candle uses 256 for efficiency
VOCAB_SIZE: 30522,
MODEL_NAME: 'all-MiniLM-L6-v2',
} as const
diff --git a/src/index.ts b/src/index.ts
index 7117fc48..a8d0e9e8 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -486,18 +486,6 @@ import { getNounTypes, getVerbTypes, getNounTypeMap, getVerbTypeMap } from './ut
// Export BrainyTypes for complete type management
import { BrainyTypes, TypeSuggestion, suggestType } from './utils/brainyTypes.js'
-// Export Semantic Type Inference - THE ONE unified system (nouns + verbs)
-import {
- inferTypes,
- inferNouns,
- inferVerbs,
- inferIntent,
- getSemanticTypeInference,
- SemanticTypeInference,
- type TypeInference,
- type SemanticTypeInferenceOptions
-} from './query/semanticTypeInference.js'
-
export {
NounType,
VerbType,
@@ -507,20 +495,11 @@ export {
getVerbTypeMap,
// BrainyTypes - complete type management
BrainyTypes,
- suggestType,
- // Semantic Type Inference - Unified noun + verb inference
- inferTypes, // Main function - returns all types (nouns + verbs)
- inferNouns, // Convenience - noun types only
- inferVerbs, // Convenience - verb types only
- inferIntent, // Best for query understanding - returns {nouns, verbs}
- getSemanticTypeInference,
- SemanticTypeInference
+ suggestType
}
export type {
- TypeSuggestion,
- TypeInference,
- SemanticTypeInferenceOptions
+ TypeSuggestion
}
// Export MCP (Model Control Protocol) components
diff --git a/src/neural/SmartRelationshipExtractor.ts b/src/neural/SmartRelationshipExtractor.ts
index d3475ca5..0967eab4 100644
--- a/src/neural/SmartRelationshipExtractor.ts
+++ b/src/neural/SmartRelationshipExtractor.ts
@@ -10,10 +10,9 @@
* - Comprehensive relationship intelligence built-in
*
* Ensemble Architecture:
- * - VerbExactMatchSignal (40%) - Explicit keywords and phrases
- * - VerbEmbeddingSignal (35%) - Neural similarity with verb embeddings
- * - VerbPatternSignal (20%) - Regex patterns and structures
- * - VerbContextSignal (5%) - Entity type pair hints
+ * - VerbEmbeddingSignal (55%) - Neural similarity with verb embeddings
+ * - VerbPatternSignal (30%) - Regex patterns and structures
+ * - VerbContextSignal (15%) - Entity type pair hints
*
* Performance:
* - Parallel signal execution (~15-20ms total)
@@ -24,11 +23,9 @@
import type { Brainy } from '../brainy.js'
import type { VerbType, NounType } from '../types/graphTypes.js'
-import { VerbExactMatchSignal } from './signals/VerbExactMatchSignal.js'
import { VerbEmbeddingSignal } from './signals/VerbEmbeddingSignal.js'
import { VerbPatternSignal } from './signals/VerbPatternSignal.js'
import { VerbContextSignal } from './signals/VerbContextSignal.js'
-import type { VerbSignal as ExactVerbSignal } from './signals/VerbExactMatchSignal.js'
import type { VerbSignal as EmbeddingVerbSignal } from './signals/VerbEmbeddingSignal.js'
import type { VerbSignal as PatternVerbSignal } from './signals/VerbPatternSignal.js'
import type { VerbSignal as ContextVerbSignal } from './signals/VerbContextSignal.js'
@@ -40,7 +37,7 @@ export interface RelationshipExtractionResult {
type: VerbType
confidence: number
weight: number
- source: 'ensemble' | 'exact-match' | 'pattern' | 'embedding' | 'context'
+ source: 'ensemble' | 'pattern' | 'embedding' | 'context'
evidence: string
metadata?: {
signalResults?: Array<{
@@ -61,10 +58,9 @@ export interface SmartRelationshipExtractorOptions {
enableEnsemble?: boolean // Use ensemble vs single best signal (default: true)
cacheSize?: number // LRU cache size (default: 2000)
weights?: { // Custom signal weights (must sum to 1.0)
- exactMatch?: number // Default: 0.40
- embedding?: number // Default: 0.35
- pattern?: number // Default: 0.20
- context?: number // Default: 0.05
+ embedding?: number // Default: 0.55
+ pattern?: number // Default: 0.30
+ context?: number // Default: 0.15
}
}
@@ -72,7 +68,7 @@ export interface SmartRelationshipExtractorOptions {
* Internal signal result wrapper
*/
interface SignalResult {
- signal: 'exact-match' | 'embedding' | 'pattern' | 'context'
+ signal: 'embedding' | 'pattern' | 'context'
type: VerbType | null
confidence: number
weight: number
@@ -97,7 +93,6 @@ export class SmartRelationshipExtractor {
private options: Required> & { weights: Required