Ruvector Tiny Dancer Core

Production-grade AI agent routing system with FastGRNN neural inference for 70-85% LLM cost reduction.

🚀 Introduction

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.

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.

How It Works:

1. You send a request with potential responses (candidates)
2. Tiny Dancer scores each candidate in microseconds
3. High-confidence candidates go to lightweight models (fast & cheap)
4. Low-confidence candidates go to powerful models (accurate but expensive)

The Result: Save 70-85% on AI costs while maintaining quality.

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.

✨ Features

• ⚡ Sub-millisecond Latency: 144ns feature extraction, 7.5µs model inference
• 💰 70-85% Cost Reduction: Intelligent routing to appropriately-sized models
• 🧠 FastGRNN Architecture: <1MB models with 80-90% sparsity
• 🔒 Circuit Breaker: Graceful degradation with automatic recovery
• 📊 Uncertainty Quantification: Conformal prediction for reliable routing
• 🗄️ AgentDB Integration: Persistent SQLite storage with WAL mode
• 🎯 Multi-Signal Scoring: Semantic similarity, recency, frequency, success rate
• 🔧 Model Optimization: INT8 quantization, magnitude pruning

📊 Benchmark Results

Feature Extraction:
  10 candidates:   1.73µs  (173ns per candidate)
  50 candidates:   9.44µs  (189ns per candidate)
  100 candidates:  18.48µs (185ns per candidate)

Model Inference:
  Single:          7.50µs
  Batch 10:        74.94µs  (7.49µs per item)
  Batch 100:       735.45µs (7.35µs per item)

Complete Routing:
  10 candidates:   8.83µs
  50 candidates:   48.23µs
  100 candidates:  92.86µs

🚀 Quick Start

Installation

Add to your Cargo.toml:

[dependencies]
ruvector-tiny-dancer-core = "0.1.1"

Basic Usage

use ruvector_tiny_dancer_core::{
    Router,
    types::{RouterConfig, RoutingRequest, Candidate},
};
use std::collections::HashMap;

// Create router
let config = RouterConfig {
    model_path: "./models/fastgrnn.safetensors".to_string(),
    confidence_threshold: 0.85,
    max_uncertainty: 0.15,
    enable_circuit_breaker: true,
    ..Default::default()
};

let router = Router::new(config)?;

// Prepare candidates
let candidates = vec![
    Candidate {
        id: "candidate-1".to_string(),
        embedding: vec![0.5; 384],
        metadata: HashMap::new(),
        created_at: chrono::Utc::now().timestamp(),
        access_count: 10,
        success_rate: 0.95,
    },
];

// Route request
let request = RoutingRequest {
    query_embedding: vec![0.5; 384],
    candidates,
    metadata: None,
};

let response = router.route(request)?;

// Process decisions
for decision in response.decisions {
    println!("Candidate: {}", decision.candidate_id);
    println!("Confidence: {:.2}", decision.confidence);
    println!("Use lightweight: {}", decision.use_lightweight);
    println!("Inference time: {}µs", response.inference_time_us);
}

📚 Tutorials

Tutorial 1: Basic Routing

use ruvector_tiny_dancer_core::{Router, types::*};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create default router
    let router = Router::default()?;

    // Create a simple request
    let request = RoutingRequest {
        query_embedding: vec![0.9; 384],
        candidates: vec![
            Candidate {
                id: "high-quality".to_string(),
                embedding: vec![0.85; 384],
                metadata: Default::default(),
                created_at: chrono::Utc::now().timestamp(),
                access_count: 100,
                success_rate: 0.98,
            }
        ],
        metadata: None,
    };

    // Route and inspect results
    let response = router.route(request)?;
    let decision = &response.decisions[0];

    if decision.use_lightweight {
        println!("✅ High confidence - route to lightweight model");
    } else {
        println!("⚠️ Low confidence - route to powerful model");
    }

    Ok(())
}

Tutorial 2: Feature Engineering

use ruvector_tiny_dancer_core::feature_engineering::{FeatureEngineer, FeatureConfig};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Custom feature weights
    let config = FeatureConfig {
        similarity_weight: 0.5,  // Prioritize semantic similarity
        recency_weight: 0.3,     // Recent items are important
        frequency_weight: 0.1,
        success_weight: 0.05,
        metadata_weight: 0.05,
        recency_decay: 0.001,
    };

    let engineer = FeatureEngineer::with_config(config);

    // Extract features
    let query = vec![0.5; 384];
    let candidate = Candidate { /* ... */ };
    let features = engineer.extract_features(&query, &candidate, None)?;

    println!("Semantic similarity: {:.4}", features.semantic_similarity);
    println!("Recency score: {:.4}", features.recency_score);
    println!("Combined score: {:.4}",
        features.features.iter().sum::<f32>());

    Ok(())
}

Tutorial 3: Circuit Breaker

use ruvector_tiny_dancer_core::Router;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let router = Router::default()?;

    // Check circuit breaker status
    match router.circuit_breaker_status() {
        Some(true) => {
            println!("✅ Circuit closed - system healthy");
            // Normal routing
        }
        Some(false) => {
            println!("⚠️ Circuit open - using fallback");
            // Route to default powerful model
        }
        None => {
            println!("Circuit breaker disabled");
        }
    }

    Ok(())
}

Tutorial 4: Model Optimization

use ruvector_tiny_dancer_core::model::{FastGRNN, FastGRNNConfig};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create model
    let config = FastGRNNConfig {
        input_dim: 5,
        hidden_dim: 8,
        output_dim: 1,
        ..Default::default()
    };

    let mut model = FastGRNN::new(config)?;

    println!("Original size: {} bytes", model.size_bytes());

    // Apply quantization
    model.quantize()?;
    println!("After quantization: {} bytes", model.size_bytes());

    // Apply pruning
    model.prune(0.9)?;  // 90% sparsity
    println!("After pruning: {} bytes", model.size_bytes());

    Ok(())
}

Tutorial 5: SQLite Storage

use ruvector_tiny_dancer_core::storage::Storage;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create storage
    let storage = Storage::new("./routing.db")?;

    // Insert candidate
    let candidate = Candidate { /* ... */ };
    storage.insert_candidate(&candidate)?;

    // Query candidates
    let candidates = storage.query_candidates(50)?;
    println!("Retrieved {} candidates", candidates.len());

    // Record routing
    storage.record_routing(
        "candidate-1",
        &vec![0.5; 384],
        0.92,      // confidence
        true,      // use_lightweight
        0.08,      // uncertainty
        8_500,     // inference_time_us
    )?;

    // Get statistics
    let stats = storage.get_statistics()?;
    println!("Total routes: {}", stats.total_routes);
    println!("Lightweight: {}", stats.lightweight_routes);
    println!("Avg inference: {:.2}µs", stats.avg_inference_time_us);

    Ok(())
}

🎯 Advanced Usage

Hot Model Reloading

// Reload model without downtime
router.reload_model()?;

Custom Configuration

let config = RouterConfig {
    model_path: "./models/custom.safetensors".to_string(),
    confidence_threshold: 0.90,  // Higher threshold
    max_uncertainty: 0.10,       // Lower tolerance
    enable_circuit_breaker: true,
    circuit_breaker_threshold: 3, // Faster circuit opening
    enable_quantization: true,
    database_path: Some("./data/routing.db".to_string()),
};

Batch Processing

let inputs = vec![
    vec![0.5; 5],
    vec![0.3; 5],
    vec![0.8; 5],
];

let scores = model.forward_batch(&inputs)?;
// Process 3 inputs in ~22µs total

📈 Performance Optimization

SIMD Acceleration

Feature extraction uses simsimd for hardware-accelerated similarity:

• Cosine similarity: 144ns (384-dim vectors)
• Batch processing: Linear scaling with candidate count

Zero-Copy Operations

• Memory-mapped models with memmap2
• Zero-allocation inference paths
• Efficient buffer reuse

Parallel Processing

• Rayon-based parallel feature extraction
• Batch inference for multiple candidates
• Concurrent storage operations with WAL

🔧 Configuration

📊 Cost Analysis

For 10,000 daily queries at $0.02 per query:

Break-even: ~2 months with typical engineering costs

🔗 Related Projects

• WASM: ruvector-tiny-dancer-wasm - Browser/edge deployment
• Node.js: ruvector-tiny-dancer-node - TypeScript bindings
• Ruvector: ruvector-core - Vector database

📚 Resources

• Documentation: docs.rs/ruvector-tiny-dancer-core
• GitHub: github.com/ruvnet/ruvector
• Website: ruv.io
• Examples: github.com/ruvnet/ruvector/tree/main/examples

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

• FastGRNN architecture inspired by Microsoft Research
• RouteLLM for routing methodology
• Cloudflare Workers for WASM deployment patterns

Built with ❤️ by the Ruvector Team