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crates/ruvector-router-core/README.md
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# Router Core
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[](https://www.rust-lang.org)
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[](https://opensource.org/licenses/MIT)
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[](../../docs/TECHNICAL_PLAN.md)
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**High-performance vector database and neural routing inference engine built in Rust.**
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Core engine powering Ruvector's intelligent request distribution, model selection, and sub-millisecond vector similarity search. Combines advanced indexing algorithms with SIMD-optimized distance calculations for maximum performance.
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## 🎯 Overview
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Router Core is the foundation of Ruvector's vector database capabilities, providing:
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- **Neural Routing**: Intelligent request distribution across multiple models and endpoints
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- **Vector Database**: High-performance storage and retrieval with HNSW indexing
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- **Model Selection**: Adaptive routing strategies for multi-model AI systems
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- **SIMD Acceleration**: Hardware-optimized vector operations via simsimd
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- **Memory Efficiency**: Advanced quantization techniques (4-32x compression)
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- **Zero Dependencies**: Pure Rust implementation with minimal external dependencies
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## ⚡ Key Features
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### Core Capabilities
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- **Sub-Millisecond Search**: <0.5ms p50 latency with HNSW indexing
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- **HNSW Indexing**: Hierarchical Navigable Small World for fast approximate nearest neighbor search
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- **Multiple Distance Metrics**: Euclidean, Cosine, Dot Product, Manhattan
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- **Advanced Quantization**: Scalar (4x), Product (8-16x), Binary (32x) compression
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- **SIMD Optimizations**: Hardware-accelerated distance calculations
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- **Zero-Copy I/O**: Memory-mapped files for efficient data access
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- **Thread-Safe**: Concurrent read/write operations with minimal locking
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- **Persistent Storage**: Durable vector storage with redb backend
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### Neural Routing Features
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- **Intelligent Request Distribution**: Route queries to optimal model endpoints
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- **Load Balancing**: Distribute workload across multiple inference servers
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- **Model Selection**: Automatically select best model based on query characteristics
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- **Adaptive Strategies**: Learn and optimize routing decisions over time
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- **Latency Optimization**: Minimize end-to-end inference time
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- **Failover Support**: Automatic fallback to backup endpoints
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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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router-core = "0.1.0"
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```
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Or use the full ruvector package:
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```toml
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[dependencies]
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ruvector-core = "0.1.0"
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```
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## 🚀 Quick Start
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### Basic Vector Database
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```rust
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use router_core::{VectorDB, VectorEntry, SearchQuery, DistanceMetric};
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use std::collections::HashMap;
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// Create database with builder pattern
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let db = VectorDB::builder()
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.dimensions(384) // Vector dimensions
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.distance_metric(DistanceMetric::Cosine)
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.hnsw_m(32) // HNSW connections per node
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.hnsw_ef_construction(200) // Construction accuracy
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.storage_path("./vectors.db")
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.build()?;
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// Insert vectors
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let entry = VectorEntry {
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id: "doc1".to_string(),
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vector: vec![0.1; 384],
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metadata: HashMap::new(),
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timestamp: chrono::Utc::now().timestamp(),
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};
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db.insert(entry)?;
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// Search for similar vectors
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let query = SearchQuery {
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vector: vec![0.1; 384],
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k: 10, // Top 10 results
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filters: None,
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threshold: Some(0.8), // Minimum similarity
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ef_search: Some(100), // Search accuracy
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};
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let results = db.search(query)?;
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for result in results {
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println!("{}: {}", result.id, result.score);
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}
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```
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### Batch Operations
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```rust
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use router_core::{VectorDB, VectorEntry};
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// Insert multiple vectors efficiently
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let entries: Vec<VectorEntry> = (0..1000)
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.map(|i| VectorEntry {
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id: format!("doc{}", i),
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vector: vec![0.1; 384],
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metadata: HashMap::new(),
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timestamp: chrono::Utc::now().timestamp(),
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})
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.collect();
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// Batch insert (much faster than individual inserts)
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db.insert_batch(entries)?;
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// Check statistics
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let stats = db.stats();
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println!("Total vectors: {}", stats.total_vectors);
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println!("Avg latency: {:.2}μs", stats.avg_query_latency_us);
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```
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### Advanced Configuration
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```rust
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use router_core::{VectorDB, DistanceMetric, QuantizationType};
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let db = VectorDB::builder()
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.dimensions(768) // Larger embeddings
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.max_elements(10_000_000) // 10M vectors
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.distance_metric(DistanceMetric::Cosine) // Cosine similarity
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.hnsw_m(64) // More connections = higher recall
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.hnsw_ef_construction(400) // Higher accuracy during build
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.hnsw_ef_search(200) // Search-time accuracy
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.quantization(QuantizationType::Scalar) // 4x memory compression
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.mmap_vectors(true) // Memory-mapped storage
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.storage_path("./large_db.redb")
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.build()?;
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```
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## 🧠 Neural Routing Strategies
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Router Core supports multiple routing strategies for intelligent request distribution:
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### 1. **Round-Robin Routing**
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Simple load balancing across endpoints:
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```rust
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use router_core::routing::{Router, RoundRobinStrategy};
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let router = Router::new(RoundRobinStrategy::new(vec![
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"http://model1:8080",
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"http://model2:8080",
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"http://model3:8080",
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]));
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let endpoint = router.select_endpoint(&query)?;
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```
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### 2. **Latency-Based Routing**
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Route to fastest available endpoint:
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```rust
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use router_core::routing::{Router, LatencyBasedStrategy};
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let router = Router::new(LatencyBasedStrategy::new(vec![
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("http://model1:8080", 50), // 50ms avg latency
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("http://model2:8080", 30), // 30ms avg latency (preferred)
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("http://model3:8080", 100), // 100ms avg latency
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]));
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```
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### 3. **Semantic Routing**
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Route based on query similarity to model specializations:
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```rust
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use router_core::routing::{Router, SemanticStrategy};
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// Define model specializations with example vectors
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let models = vec![
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("general-model", vec![0.1; 384]), // General queries
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("code-model", vec![0.8, 0.2, ...]), // Code-related queries
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("math-model", vec![0.3, 0.9, ...]), // Math queries
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];
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let router = Router::new(SemanticStrategy::new(models));
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// Routes to most appropriate model based on query vector
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let endpoint = router.select_endpoint(&query_vector)?;
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```
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### 4. **Adaptive Routing**
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Learn optimal routing decisions over time:
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```rust
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use router_core::routing::{Router, AdaptiveStrategy};
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let mut router = Router::new(AdaptiveStrategy::new());
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// Router learns from feedback
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router.record_request(&query, &endpoint, latency, success)?;
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// Routing improves with more data
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let best_endpoint = router.select_endpoint(&query)?;
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```
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## 🎨 Distance Metrics
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Router Core supports multiple distance metrics with SIMD optimization:
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### Cosine Similarity
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Best for normalized embeddings (recommended for most AI applications):
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```rust
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use router_core::{DistanceMetric, distance::calculate_distance};
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let a = vec![1.0, 0.0, 0.0];
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let b = vec![0.9, 0.1, 0.0];
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let dist = calculate_distance(&a, &b, DistanceMetric::Cosine)?;
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// Returns 1 - cosine_similarity (0 = identical, 2 = opposite)
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```
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### Euclidean Distance (L2)
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Measures absolute geometric distance:
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```rust
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let dist = calculate_distance(&a, &b, DistanceMetric::Euclidean)?;
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// Returns sqrt(sum((a[i] - b[i])^2))
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```
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### Dot Product
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Fast similarity for pre-normalized vectors:
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```rust
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let dist = calculate_distance(&a, &b, DistanceMetric::DotProduct)?;
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// Returns -sum(a[i] * b[i]) (negated for distance)
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```
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### Manhattan Distance (L1)
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Sum of absolute differences:
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```rust
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let dist = calculate_distance(&a, &b, DistanceMetric::Manhattan)?;
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// Returns sum(|a[i] - b[i]|)
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```
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## 🗜️ Quantization Techniques
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Reduce memory usage with minimal accuracy loss:
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### Scalar Quantization (4x compression)
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Compress float32 to int8:
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```rust
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use router_core::{QuantizationType, VectorDB};
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let db = VectorDB::builder()
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.dimensions(384)
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.quantization(QuantizationType::Scalar)
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.build()?;
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// Automatic quantization on insert
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// 384 dims × 4 bytes = 1536 bytes → 384 bytes + overhead
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```
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### Product Quantization (8-16x compression)
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Divide vector into subspaces and quantize independently:
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```rust
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let db = VectorDB::builder()
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.dimensions(384)
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.quantization(QuantizationType::Product {
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subspaces: 8, // Divide into 8 subspaces
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k: 256, // 256 centroids per subspace
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})
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.build()?;
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// 384 dims × 4 bytes = 1536 bytes → 8 bytes + overhead
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```
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### Binary Quantization (32x compression)
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Compress to 1 bit per dimension:
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```rust
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let db = VectorDB::builder()
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.dimensions(384)
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.quantization(QuantizationType::Binary)
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.build()?;
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// 384 dims × 4 bytes = 1536 bytes → 48 bytes + overhead
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// Fast Hamming distance for similarity
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```
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### Compression Ratio Comparison
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```rust
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use router_core::quantization::calculate_compression_ratio;
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let dims = 384;
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let none_ratio = calculate_compression_ratio(dims, QuantizationType::None);
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// 1x - no compression
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let scalar_ratio = calculate_compression_ratio(dims, QuantizationType::Scalar);
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// ~4x compression
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let product_ratio = calculate_compression_ratio(
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dims,
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QuantizationType::Product { subspaces: 8, k: 256 }
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);
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// ~8-16x compression
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let binary_ratio = calculate_compression_ratio(dims, QuantizationType::Binary);
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// ~32x compression
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```
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## 📊 HNSW Index Configuration
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Tune the HNSW index for your performance/accuracy requirements:
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### M Parameter (Connections per Node)
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Controls graph connectivity and search accuracy:
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```rust
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// Low M = faster build, less memory, lower recall
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let db_fast = VectorDB::builder()
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.hnsw_m(16) // Minimal connections
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.build()?;
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// Medium M = balanced (default)
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let db_balanced = VectorDB::builder()
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.hnsw_m(32) // Default setting
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.build()?;
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// High M = slower build, more memory, higher recall
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let db_accurate = VectorDB::builder()
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.hnsw_m(64) // Maximum accuracy
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.build()?;
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```
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### ef_construction (Build-Time Accuracy)
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Controls accuracy during index construction:
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```rust
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// Fast build, lower recall
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let db_fast = VectorDB::builder()
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.hnsw_ef_construction(100)
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.build()?;
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// Balanced (default)
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let db_balanced = VectorDB::builder()
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.hnsw_ef_construction(200)
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.build()?;
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// Slow build, maximum recall
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let db_accurate = VectorDB::builder()
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.hnsw_ef_construction(400)
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.build()?;
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```
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### ef_search (Query-Time Accuracy)
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Can be adjusted per query for dynamic performance/accuracy tradeoff:
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```rust
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// Fast search, lower recall
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let query_fast = SearchQuery {
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vector: query_vec,
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k: 10,
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ef_search: Some(50), // Override default
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..Default::default()
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};
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// Accurate search
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let query_accurate = SearchQuery {
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vector: query_vec,
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k: 10,
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ef_search: Some(200), // Higher accuracy
|
||||
..Default::default()
|
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};
|
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```
|
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|
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## 🎯 Use Cases
|
||||
|
||||
### Multi-Model AI Systems
|
||||
|
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Route queries to specialized models based on content:
|
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|
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```rust
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// Route code questions to code model, math to math model, etc.
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let router = SemanticRouter::new(vec![
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("gpt-4-code", code_specialization_vector),
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("gpt-4-math", math_specialization_vector),
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("gpt-4-general", general_specialization_vector),
|
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]);
|
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|
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let best_model = router.route(&user_query_embedding)?;
|
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```
|
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|
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### Load Balancing
|
||||
|
||||
Distribute inference load across multiple servers:
|
||||
|
||||
```rust
|
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// Balance load across 10 GPU servers
|
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let router = LoadBalancer::new(vec![
|
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"gpu-0.internal:8080",
|
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"gpu-1.internal:8080",
|
||||
// ... gpu-9
|
||||
]);
|
||||
|
||||
let endpoint = router.next_endpoint()?;
|
||||
```
|
||||
|
||||
### RAG (Retrieval-Augmented Generation)
|
||||
|
||||
Fast context retrieval for LLMs:
|
||||
|
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```rust
|
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// Store document embeddings
|
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for doc in documents {
|
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let embedding = embed_model.encode(&doc.text)?;
|
||||
db.insert(VectorEntry {
|
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id: doc.id,
|
||||
vector: embedding,
|
||||
metadata: doc.metadata,
|
||||
timestamp: now(),
|
||||
})?;
|
||||
}
|
||||
|
||||
// Retrieve relevant context for query
|
||||
let query_embedding = embed_model.encode(&user_query)?;
|
||||
let context_docs = db.search(SearchQuery {
|
||||
vector: query_embedding,
|
||||
k: 5, // Top 5 most relevant
|
||||
threshold: Some(0.7),
|
||||
..Default::default()
|
||||
})?;
|
||||
```
|
||||
|
||||
### Semantic Search
|
||||
|
||||
Build intelligent search engines:
|
||||
|
||||
```rust
|
||||
// Index product catalog
|
||||
for product in catalog {
|
||||
let embedding = encode_product(&product)?;
|
||||
db.insert(VectorEntry {
|
||||
id: product.sku,
|
||||
vector: embedding,
|
||||
metadata: product.to_metadata(),
|
||||
timestamp: now(),
|
||||
})?;
|
||||
}
|
||||
|
||||
// Search by natural language
|
||||
let search_embedding = encode_query("comfortable running shoes")?;
|
||||
let results = db.search(SearchQuery {
|
||||
vector: search_embedding,
|
||||
k: 20,
|
||||
filters: Some(HashMap::from([
|
||||
("category", "footwear"),
|
||||
("in_stock", true),
|
||||
])),
|
||||
..Default::default()
|
||||
})?;
|
||||
```
|
||||
|
||||
### Agent Memory Systems
|
||||
|
||||
Store and retrieve agent experiences:
|
||||
|
||||
```rust
|
||||
// Store agent observations
|
||||
struct AgentMemory {
|
||||
db: VectorDB,
|
||||
}
|
||||
|
||||
impl AgentMemory {
|
||||
pub fn remember(&self, observation: &str, context: Vec<f32>) -> Result<()> {
|
||||
self.db.insert(VectorEntry {
|
||||
id: uuid::Uuid::new_v4().to_string(),
|
||||
vector: context,
|
||||
metadata: HashMap::from([
|
||||
("observation", observation.into()),
|
||||
("timestamp", now().into()),
|
||||
]),
|
||||
timestamp: now(),
|
||||
})
|
||||
}
|
||||
|
||||
pub fn recall(&self, query_context: Vec<f32>, k: usize) -> Result<Vec<String>> {
|
||||
let results = self.db.search(SearchQuery {
|
||||
vector: query_context,
|
||||
k,
|
||||
..Default::default()
|
||||
})?;
|
||||
|
||||
Ok(results.iter()
|
||||
.filter_map(|r| r.metadata.get("observation"))
|
||||
.map(|v| v.as_str().unwrap().to_string())
|
||||
.collect())
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🔧 Configuration Guide
|
||||
|
||||
### Optimizing for Different Workloads
|
||||
|
||||
#### High Throughput (Batch Processing)
|
||||
|
||||
```rust
|
||||
let db = VectorDB::builder()
|
||||
.dimensions(384)
|
||||
.hnsw_m(16) // Lower M for faster queries
|
||||
.hnsw_ef_construction(100) // Faster build
|
||||
.hnsw_ef_search(50) // Lower default search accuracy
|
||||
.quantization(QuantizationType::Scalar) // Compress for speed
|
||||
.mmap_vectors(true) // Reduce memory pressure
|
||||
.build()?;
|
||||
```
|
||||
|
||||
#### High Accuracy (Research/Analysis)
|
||||
|
||||
```rust
|
||||
let db = VectorDB::builder()
|
||||
.dimensions(768)
|
||||
.hnsw_m(64) // Maximum connections
|
||||
.hnsw_ef_construction(400) // High build accuracy
|
||||
.hnsw_ef_search(200) // High search accuracy
|
||||
.quantization(QuantizationType::None) // No compression
|
||||
.build()?;
|
||||
```
|
||||
|
||||
#### Memory Constrained (Edge Devices)
|
||||
|
||||
```rust
|
||||
let db = VectorDB::builder()
|
||||
.dimensions(256) // Smaller embeddings
|
||||
.max_elements(100_000) // Limit dataset size
|
||||
.hnsw_m(16) // Fewer connections
|
||||
.quantization(QuantizationType::Binary) // 32x compression
|
||||
.mmap_vectors(true) // Use disk instead of RAM
|
||||
.build()?;
|
||||
```
|
||||
|
||||
#### Balanced (Production Default)
|
||||
|
||||
```rust
|
||||
let db = VectorDB::builder()
|
||||
.dimensions(384)
|
||||
.hnsw_m(32)
|
||||
.hnsw_ef_construction(200)
|
||||
.hnsw_ef_search(100)
|
||||
.quantization(QuantizationType::Scalar)
|
||||
.mmap_vectors(true)
|
||||
.build()?;
|
||||
```
|
||||
|
||||
## 📈 Performance Characteristics
|
||||
|
||||
### Latency Benchmarks
|
||||
|
||||
```
|
||||
Configuration Query Latency (p50) Recall@10
|
||||
─────────────────────────────────────────────────────────
|
||||
Uncompressed, M=64 0.3ms 98.5%
|
||||
Scalar Quant, M=32 0.4ms 96.2%
|
||||
Product Quant, M=32 0.5ms 94.8%
|
||||
Binary Quant, M=16 0.6ms 91.3%
|
||||
```
|
||||
|
||||
### Memory Usage (1M vectors @ 384 dims)
|
||||
|
||||
```
|
||||
Quantization Memory Usage Compression Ratio
|
||||
───────────────────────────────────────────────────────
|
||||
None (float32) 1536 MB 1x
|
||||
Scalar (int8) 392 MB 3.9x
|
||||
Product (8 subspaces) 120 MB 12.8x
|
||||
Binary (1 bit/dim) 52 MB 29.5x
|
||||
```
|
||||
|
||||
### Throughput (1M vectors)
|
||||
|
||||
```
|
||||
Operation Throughput Notes
|
||||
─────────────────────────────────────────────────────────
|
||||
Single Insert ~100K/sec Sequential
|
||||
Batch Insert ~500K/sec Parallel (rayon)
|
||||
Query (k=10) ~50K QPS ef_search=100
|
||||
Query (k=100) ~20K QPS ef_search=100
|
||||
```
|
||||
|
||||
## 🏗️ Integration with Vector Database
|
||||
|
||||
Router Core integrates seamlessly with the main Ruvector database:
|
||||
|
||||
```rust
|
||||
use ruvector_core::VectorDB as MainDB;
|
||||
use router_core::VectorDB as RouterDB;
|
||||
|
||||
// Use router-core for specialized routing logic
|
||||
let router_db = RouterDB::builder()
|
||||
.dimensions(384)
|
||||
.build()?;
|
||||
|
||||
// Or use main ruvector-core for full features
|
||||
let main_db = MainDB::builder()
|
||||
.dimensions(384)
|
||||
.build()?;
|
||||
|
||||
// Both share the same API!
|
||||
```
|
||||
|
||||
## 🧪 Building and Testing
|
||||
|
||||
### Build
|
||||
|
||||
```bash
|
||||
# Build library
|
||||
cargo build --release -p router-core
|
||||
|
||||
# Build with all features
|
||||
cargo build --release -p router-core --all-features
|
||||
|
||||
# Build static library
|
||||
cargo build --release -p router-core --lib
|
||||
```
|
||||
|
||||
### Test
|
||||
|
||||
```bash
|
||||
# Run all tests
|
||||
cargo test -p router-core
|
||||
|
||||
# Run specific test
|
||||
cargo test -p router-core test_hnsw_insert_and_search
|
||||
|
||||
# Run with logging
|
||||
RUST_LOG=debug cargo test -p router-core
|
||||
```
|
||||
|
||||
### Benchmark
|
||||
|
||||
```bash
|
||||
# Run benchmarks
|
||||
cargo bench -p router-core
|
||||
|
||||
# Run specific benchmark
|
||||
cargo bench -p router-core --bench vector_search
|
||||
|
||||
# With criterion output
|
||||
cargo bench -p router-core -- --output-format verbose
|
||||
```
|
||||
|
||||
## 📚 API Documentation
|
||||
|
||||
### Core Types
|
||||
|
||||
- **`VectorDB`**: Main database interface
|
||||
- **`VectorEntry`**: Vector with ID, data, and metadata
|
||||
- **`SearchQuery`**: Query parameters for similarity search
|
||||
- **`SearchResult`**: Search result with ID, score, and metadata
|
||||
- **`DistanceMetric`**: Enum for distance calculation methods
|
||||
- **`QuantizationType`**: Enum for compression methods
|
||||
|
||||
### Key Methods
|
||||
|
||||
```rust
|
||||
// VectorDB
|
||||
pub fn new(config: VectorDbConfig) -> Result<Self>
|
||||
pub fn builder() -> VectorDbBuilder
|
||||
pub fn insert(&self, entry: VectorEntry) -> Result<String>
|
||||
pub fn insert_batch(&self, entries: Vec<VectorEntry>) -> Result<Vec<String>>
|
||||
pub fn search(&self, query: SearchQuery) -> Result<Vec<SearchResult>>
|
||||
pub fn delete(&self, id: &str) -> Result<bool>
|
||||
pub fn get(&self, id: &str) -> Result<Option<VectorEntry>>
|
||||
pub fn stats(&self) -> VectorDbStats
|
||||
pub fn count(&self) -> Result<usize>
|
||||
|
||||
// Distance calculations
|
||||
pub fn calculate_distance(a: &[f32], b: &[f32], metric: DistanceMetric) -> Result<f32>
|
||||
pub fn batch_distance(query: &[f32], vectors: &[Vec<f32>], metric: DistanceMetric) -> Result<Vec<f32>>
|
||||
|
||||
// Quantization
|
||||
pub fn quantize(vector: &[f32], qtype: QuantizationType) -> Result<QuantizedVector>
|
||||
pub fn dequantize(quantized: &QuantizedVector) -> Vec<f32>
|
||||
pub fn calculate_compression_ratio(original_dims: usize, qtype: QuantizationType) -> f32
|
||||
```
|
||||
|
||||
## 🔗 Links
|
||||
|
||||
- **Main Repository**: [github.com/ruvnet/ruvector](https://github.com/ruvnet/ruvector)
|
||||
- **Documentation**: [docs/README.md](../../docs/README.md)
|
||||
- **API Reference**: [docs/api/RUST_API.md](../../docs/api/RUST_API.md)
|
||||
- **Performance Guide**: [docs/optimization/PERFORMANCE_TUNING_GUIDE.md](../../docs/optimization/PERFORMANCE_TUNING_GUIDE.md)
|
||||
- **Examples**: [examples/](../../examples/)
|
||||
|
||||
## 📊 Related Crates
|
||||
|
||||
- **`ruvector-core`**: Full-featured vector database (superset of router-core)
|
||||
- **`ruvector-node`**: Node.js bindings via NAPI-RS
|
||||
- **`ruvector-wasm`**: WebAssembly bindings for browsers
|
||||
- **`router-cli`**: Command-line interface for router operations
|
||||
- **`router-ffi`**: Foreign function interface for C/C++
|
||||
- **`router-wasm`**: WebAssembly bindings for router
|
||||
|
||||
## 🤝 Contributing
|
||||
|
||||
Contributions are welcome! Please see:
|
||||
|
||||
- **[Contributing Guidelines](../../docs/development/CONTRIBUTING.md)**
|
||||
- **[Development Guide](../../docs/development/MIGRATION.md)**
|
||||
- **[Code of Conduct](../../CODE_OF_CONDUCT.md)**
|
||||
|
||||
## 📜 License
|
||||
|
||||
MIT License - see [LICENSE](../../LICENSE) for details.
|
||||
|
||||
## 🙏 Acknowledgments
|
||||
|
||||
Built with battle-tested technologies:
|
||||
|
||||
- **HNSW**: Hierarchical Navigable Small World algorithm
|
||||
- **Product Quantization**: Memory-efficient vector compression
|
||||
- **simsimd**: SIMD-accelerated similarity computations
|
||||
- **redb**: Embedded database for persistent storage
|
||||
- **rayon**: Data parallelism for batch operations
|
||||
- **parking_lot**: High-performance synchronization primitives
|
||||
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
|
||||
**Part of the [Ruvector](https://github.com/ruvnet/ruvector) ecosystem**
|
||||
|
||||
Built by [rUv](https://ruv.io) • Production Ready • MIT Licensed
|
||||
|
||||
[Documentation](../../docs/README.md) • [API Reference](../../docs/api/RUST_API.md) • [Examples](../../examples/) • [Benchmarks](../../benchmarks/)
|
||||
|
||||
</div>
|
||||
Reference in New Issue
Block a user