391 lines
11 KiB
Markdown
391 lines
11 KiB
Markdown
# Ruvector Tiny Dancer Core
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[](https://crates.io/crates/ruvector-tiny-dancer-core)
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[](https://docs.rs/ruvector-tiny-dancer-core)
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[](https://opensource.org/licenses/MIT)
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[](https://github.com/ruvnet/ruvector/actions)
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[](https://www.rust-lang.org)
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Production-grade AI agent routing system with FastGRNN neural inference for **70-85% LLM cost reduction**.
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## 🚀 Introduction
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**The Problem**: AI applications often send every request to expensive, powerful models, even when simpler models could handle the task. This wastes money and resources.
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**The Solution**: Tiny Dancer acts as a smart traffic controller for your AI requests. It quickly analyzes each request and decides whether to route it to a fast, cheap model or a powerful, expensive one.
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**How It Works**:
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1. You send a request with potential responses (candidates)
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2. Tiny Dancer scores each candidate in microseconds
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3. High-confidence candidates go to lightweight models (fast & cheap)
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4. Low-confidence candidates go to powerful models (accurate but expensive)
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**The Result**: Save 70-85% on AI costs while maintaining quality.
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**Real-World Example**: Instead of sending 100 memory items to GPT-4 for evaluation, Tiny Dancer filters them down to the top 3-5 in microseconds, then sends only those to the expensive model.
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## ✨ Features
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- ⚡ **Sub-millisecond Latency**: 144ns feature extraction, 7.5µs model inference
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- 💰 **70-85% Cost Reduction**: Intelligent routing to appropriately-sized models
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- 🧠 **FastGRNN Architecture**: <1MB models with 80-90% sparsity
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- 🔒 **Circuit Breaker**: Graceful degradation with automatic recovery
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- 📊 **Uncertainty Quantification**: Conformal prediction for reliable routing
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- 🗄️ **AgentDB Integration**: Persistent SQLite storage with WAL mode
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- 🎯 **Multi-Signal Scoring**: Semantic similarity, recency, frequency, success rate
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- 🔧 **Model Optimization**: INT8 quantization, magnitude pruning
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## 📊 Benchmark Results
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```
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Feature Extraction:
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10 candidates: 1.73µs (173ns per candidate)
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50 candidates: 9.44µs (189ns per candidate)
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100 candidates: 18.48µs (185ns per candidate)
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Model Inference:
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Single: 7.50µs
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Batch 10: 74.94µs (7.49µs per item)
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Batch 100: 735.45µs (7.35µs per item)
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Complete Routing:
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10 candidates: 8.83µs
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50 candidates: 48.23µs
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100 candidates: 92.86µs
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```
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## 🚀 Quick Start
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### Installation
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Add to your `Cargo.toml`:
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```toml
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[dependencies]
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ruvector-tiny-dancer-core = "0.1.1"
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```
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### Basic Usage
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```rust
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use ruvector_tiny_dancer_core::{
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Router,
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types::{RouterConfig, RoutingRequest, Candidate},
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};
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use std::collections::HashMap;
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// Create router
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let config = RouterConfig {
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model_path: "./models/fastgrnn.safetensors".to_string(),
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confidence_threshold: 0.85,
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max_uncertainty: 0.15,
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enable_circuit_breaker: true,
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..Default::default()
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};
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let router = Router::new(config)?;
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// Prepare candidates
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let candidates = vec![
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Candidate {
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id: "candidate-1".to_string(),
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embedding: vec![0.5; 384],
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metadata: HashMap::new(),
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created_at: chrono::Utc::now().timestamp(),
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access_count: 10,
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success_rate: 0.95,
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},
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];
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// Route request
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let request = RoutingRequest {
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query_embedding: vec![0.5; 384],
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candidates,
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metadata: None,
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};
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let response = router.route(request)?;
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// Process decisions
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for decision in response.decisions {
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println!("Candidate: {}", decision.candidate_id);
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println!("Confidence: {:.2}", decision.confidence);
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println!("Use lightweight: {}", decision.use_lightweight);
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println!("Inference time: {}µs", response.inference_time_us);
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}
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```
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## 📚 Tutorials
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### Tutorial 1: Basic Routing
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```rust
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use ruvector_tiny_dancer_core::{Router, types::*};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Create default router
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let router = Router::default()?;
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// Create a simple request
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let request = RoutingRequest {
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query_embedding: vec![0.9; 384],
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candidates: vec![
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Candidate {
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id: "high-quality".to_string(),
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embedding: vec![0.85; 384],
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metadata: Default::default(),
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created_at: chrono::Utc::now().timestamp(),
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access_count: 100,
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success_rate: 0.98,
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}
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],
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metadata: None,
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};
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// Route and inspect results
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let response = router.route(request)?;
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let decision = &response.decisions[0];
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if decision.use_lightweight {
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println!("✅ High confidence - route to lightweight model");
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} else {
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println!("⚠️ Low confidence - route to powerful model");
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}
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Ok(())
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}
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```
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### Tutorial 2: Feature Engineering
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```rust
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use ruvector_tiny_dancer_core::feature_engineering::{FeatureEngineer, FeatureConfig};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Custom feature weights
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let config = FeatureConfig {
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similarity_weight: 0.5, // Prioritize semantic similarity
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recency_weight: 0.3, // Recent items are important
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frequency_weight: 0.1,
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success_weight: 0.05,
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metadata_weight: 0.05,
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recency_decay: 0.001,
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};
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let engineer = FeatureEngineer::with_config(config);
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// Extract features
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let query = vec![0.5; 384];
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let candidate = Candidate { /* ... */ };
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let features = engineer.extract_features(&query, &candidate, None)?;
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println!("Semantic similarity: {:.4}", features.semantic_similarity);
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println!("Recency score: {:.4}", features.recency_score);
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println!("Combined score: {:.4}",
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features.features.iter().sum::<f32>());
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Ok(())
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}
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```
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### Tutorial 3: Circuit Breaker
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```rust
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use ruvector_tiny_dancer_core::Router;
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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let router = Router::default()?;
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// Check circuit breaker status
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match router.circuit_breaker_status() {
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Some(true) => {
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println!("✅ Circuit closed - system healthy");
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// Normal routing
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}
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Some(false) => {
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println!("⚠️ Circuit open - using fallback");
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// Route to default powerful model
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}
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None => {
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println!("Circuit breaker disabled");
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}
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}
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Ok(())
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}
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```
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### Tutorial 4: Model Optimization
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```rust
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use ruvector_tiny_dancer_core::model::{FastGRNN, FastGRNNConfig};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Create model
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let config = FastGRNNConfig {
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input_dim: 5,
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hidden_dim: 8,
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output_dim: 1,
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..Default::default()
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};
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let mut model = FastGRNN::new(config)?;
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println!("Original size: {} bytes", model.size_bytes());
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// Apply quantization
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model.quantize()?;
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println!("After quantization: {} bytes", model.size_bytes());
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// Apply pruning
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model.prune(0.9)?; // 90% sparsity
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println!("After pruning: {} bytes", model.size_bytes());
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Ok(())
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}
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```
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### Tutorial 5: SQLite Storage
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```rust
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use ruvector_tiny_dancer_core::storage::Storage;
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Create storage
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let storage = Storage::new("./routing.db")?;
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// Insert candidate
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let candidate = Candidate { /* ... */ };
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storage.insert_candidate(&candidate)?;
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// Query candidates
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let candidates = storage.query_candidates(50)?;
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println!("Retrieved {} candidates", candidates.len());
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// Record routing
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storage.record_routing(
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"candidate-1",
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&vec![0.5; 384],
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0.92, // confidence
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true, // use_lightweight
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0.08, // uncertainty
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8_500, // inference_time_us
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)?;
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// Get statistics
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let stats = storage.get_statistics()?;
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println!("Total routes: {}", stats.total_routes);
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println!("Lightweight: {}", stats.lightweight_routes);
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println!("Avg inference: {:.2}µs", stats.avg_inference_time_us);
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Ok(())
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}
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```
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## 🎯 Advanced Usage
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### Hot Model Reloading
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```rust
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// Reload model without downtime
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router.reload_model()?;
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```
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### Custom Configuration
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```rust
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let config = RouterConfig {
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model_path: "./models/custom.safetensors".to_string(),
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confidence_threshold: 0.90, // Higher threshold
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max_uncertainty: 0.10, // Lower tolerance
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enable_circuit_breaker: true,
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circuit_breaker_threshold: 3, // Faster circuit opening
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enable_quantization: true,
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database_path: Some("./data/routing.db".to_string()),
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};
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```
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### Batch Processing
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```rust
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let inputs = vec![
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vec![0.5; 5],
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vec![0.3; 5],
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vec![0.8; 5],
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];
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let scores = model.forward_batch(&inputs)?;
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// Process 3 inputs in ~22µs total
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```
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## 📈 Performance Optimization
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### SIMD Acceleration
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Feature extraction uses `simsimd` for hardware-accelerated similarity:
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- Cosine similarity: **144ns** (384-dim vectors)
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- Batch processing: **Linear scaling** with candidate count
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### Zero-Copy Operations
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- Memory-mapped models with `memmap2`
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- Zero-allocation inference paths
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- Efficient buffer reuse
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### Parallel Processing
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- Rayon-based parallel feature extraction
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- Batch inference for multiple candidates
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- Concurrent storage operations with WAL
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## 🔧 Configuration
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `confidence_threshold` | 0.85 | Minimum confidence for lightweight routing |
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| `max_uncertainty` | 0.15 | Maximum uncertainty tolerance |
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| `circuit_breaker_threshold` | 5 | Failures before circuit opens |
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| `recency_decay` | 0.001 | Exponential decay rate for recency |
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## 📊 Cost Analysis
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For 10,000 daily queries at $0.02 per query:
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| Scenario | Reduction | Daily Savings | Annual Savings |
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|----------|-----------|---------------|----------------|
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| Conservative | 70% | $132 | $48,240 |
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| Aggressive | 85% | $164 | $59,876 |
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**Break-even**: ~2 months with typical engineering costs
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## 🔗 Related Projects
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- **WASM**: [ruvector-tiny-dancer-wasm](../ruvector-tiny-dancer-wasm) - Browser/edge deployment
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- **Node.js**: [ruvector-tiny-dancer-node](../ruvector-tiny-dancer-node) - TypeScript bindings
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- **Ruvector**: [ruvector-core](../ruvector-core) - Vector database
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## 📚 Resources
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- **Documentation**: [docs.rs/ruvector-tiny-dancer-core](https://docs.rs/ruvector-tiny-dancer-core)
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- **GitHub**: [github.com/ruvnet/ruvector](https://github.com/ruvnet/ruvector)
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- **Website**: [ruv.io](https://ruv.io)
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- **Examples**: [github.com/ruvnet/ruvector/tree/main/examples](https://github.com/ruvnet/ruvector/tree/main/examples)
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## 🤝 Contributing
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Contributions are welcome! Please see [CONTRIBUTING.md](../../CONTRIBUTING.md) for guidelines.
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## 📄 License
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MIT License - see [LICENSE](../../LICENSE) for details.
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## 🙏 Acknowledgments
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- FastGRNN architecture inspired by Microsoft Research
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- RouteLLM for routing methodology
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- Cloudflare Workers for WASM deployment patterns
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---
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Built with ❤️ by the [Ruvector Team](https://github.com/ruvnet)
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