git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
250 lines
6.0 KiB
Markdown
250 lines
6.0 KiB
Markdown
# @ruvector/router
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Semantic router for AI agents - vector-based intent matching with HNSW indexing and SIMD acceleration.
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## Features
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- **Semantic Intent Matching**: Route queries to intents based on meaning, not keywords
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- **HNSW Indexing**: Fast approximate nearest neighbor search
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- **SIMD Optimized**: Native Rust performance with vectorized operations
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- **Quantization**: Memory-efficient storage for large intent sets
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- **Multi-Platform**: Works on Linux, macOS, and Windows
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## Installation
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```bash
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npm install @ruvector/router
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```
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The package automatically installs the correct native binary for your platform.
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## Quick Start
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```typescript
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import { SemanticRouter } from '@ruvector/router';
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// Create router
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const router = new SemanticRouter({ dimension: 384 });
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// Add intents with example utterances
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router.addIntent({
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name: 'weather',
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utterances: [
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'What is the weather today?',
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'Will it rain tomorrow?',
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'How hot will it be?'
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],
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metadata: { handler: 'weather_agent' }
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});
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router.addIntent({
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name: 'greeting',
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utterances: [
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'Hello',
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'Hi there',
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'Good morning',
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'Hey'
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],
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metadata: { handler: 'greeting_agent' }
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});
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router.addIntent({
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name: 'help',
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utterances: [
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'I need help',
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'Can you assist me?',
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'What can you do?'
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],
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metadata: { handler: 'help_agent' }
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});
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// Route a query
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const results = await router.route('What will the weather be like this weekend?');
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console.log(results[0].intent); // 'weather'
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console.log(results[0].score); // 0.92
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console.log(results[0].metadata); // { handler: 'weather_agent' }
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```
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## API Reference
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### `SemanticRouter`
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Main class for semantic routing.
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#### Constructor
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```typescript
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new SemanticRouter(config: RouterConfig)
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```
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**RouterConfig:**
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| Property | Type | Default | Description |
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|----------|------|---------|-------------|
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| `dimension` | number | required | Embedding dimension size |
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| `metric` | string | 'cosine' | Distance metric: 'cosine', 'euclidean', 'dot' |
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| `m` | number | 16 | HNSW M parameter |
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| `efConstruction` | number | 200 | HNSW ef_construction |
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| `quantization` | boolean | false | Enable memory-efficient quantization |
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#### Methods
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##### `addIntent(intent: Intent): void`
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Add an intent to the router.
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```typescript
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router.addIntent({
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name: 'booking',
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utterances: ['Book a flight', 'Reserve a hotel'],
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metadata: { department: 'travel' }
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});
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```
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##### `route(query: string | Float32Array, k?: number): Promise<RouteResult[]>`
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Route a query to matching intents.
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```typescript
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const results = await router.route('I want to book a vacation');
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// [{ intent: 'booking', score: 0.89, metadata: {...} }]
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```
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##### `routeWithEmbedding(embedding: Float32Array, k?: number): RouteResult[]`
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Route with a pre-computed embedding (synchronous).
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```typescript
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const embedding = await getEmbedding('query text');
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const results = router.routeWithEmbedding(embedding, 3);
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```
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##### `removeIntent(name: string): boolean`
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Remove an intent from the router.
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##### `getIntents(): string[]`
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Get all registered intent names.
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##### `clear(): void`
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Remove all intents.
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##### `save(path: string): Promise<void>`
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Persist router state to disk.
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##### `load(path: string): Promise<void>`
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Load router state from disk.
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### Types
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#### `Intent`
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```typescript
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interface Intent {
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name: string; // Unique intent identifier
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utterances: string[]; // Example utterances
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embedding?: Float32Array | number[]; // Pre-computed embedding
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metadata?: Record<string, unknown>; // Custom metadata
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}
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```
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#### `RouteResult`
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```typescript
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interface RouteResult {
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intent: string; // Matched intent name
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score: number; // Similarity score (0-1)
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metadata?: Record<string, unknown>; // Intent metadata
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}
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```
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## Use Cases
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### Chatbot Intent Detection
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```typescript
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const router = new SemanticRouter({ dimension: 384 });
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// Define intents
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const intents = [
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{ name: 'faq', utterances: ['What are your hours?', 'How do I contact support?'] },
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{ name: 'order', utterances: ['Track my order', 'Where is my package?'] },
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{ name: 'return', utterances: ['I want to return this', 'How do I get a refund?'] }
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];
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intents.forEach(i => router.addIntent(i));
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// Handle user message
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async function handleMessage(text: string) {
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const [result] = await router.route(text);
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switch(result.intent) {
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case 'faq': return handleFAQ(text);
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case 'order': return handleOrder(text);
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case 'return': return handleReturn(text);
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default: return handleUnknown(text);
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}
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}
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```
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### Multi-Agent Orchestration
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```typescript
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const agents = {
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'code': new CodeAgent(),
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'research': new ResearchAgent(),
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'creative': new CreativeAgent()
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};
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const router = new SemanticRouter({ dimension: 768 });
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router.addIntent({
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name: 'code',
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utterances: ['Write code', 'Debug this', 'Implement a function'],
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metadata: { agent: 'code' }
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});
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router.addIntent({
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name: 'research',
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utterances: ['Find information', 'Search for', 'Look up'],
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metadata: { agent: 'research' }
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});
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// Route task to best agent
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async function routeTask(task: string) {
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const [result] = await router.route(task);
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const agent = agents[result.metadata.agent];
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return agent.execute(task);
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}
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```
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## Platform Support
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| Platform | Architecture | Package |
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|----------|--------------|---------|
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| Linux | x64 | `@ruvector/router-linux-x64-gnu` |
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| Linux | ARM64 | `@ruvector/router-linux-arm64-gnu` |
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| macOS | x64 | `@ruvector/router-darwin-x64` |
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| macOS | ARM64 | `@ruvector/router-darwin-arm64` |
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| Windows | x64 | `@ruvector/router-win32-x64-msvc` |
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## Performance
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- **Routing**: < 1ms per query with HNSW
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- **Throughput**: 100,000+ routes/second
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- **Memory**: ~1KB per intent + embeddings
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## Related Packages
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- [`@ruvector/core`](https://www.npmjs.com/package/@ruvector/core) - Vector database
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- [`@ruvector/tiny-dancer`](https://www.npmjs.com/package/@ruvector/tiny-dancer) - Neural routing
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- [`@ruvector/gnn`](https://www.npmjs.com/package/@ruvector/gnn) - Graph Neural Networks
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## License
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MIT
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