# Ruvector Core

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**High-performance Rust vector database engine with HNSW indexing, quantization, and SIMD optimizations.**

`ruvector-core` is the foundational Rust library powering [Ruvector](https://github.com/ruvnet/ruvector)—a next-generation vector database built for extreme performance and universal deployment. This crate provides the core vector database engine with state-of-the-art algorithms optimized for modern hardware.

## 🌟 Why Ruvector Core?

- ⚡ **Blazing Fast**: <0.5ms p50 query latency with HNSW indexing
- 🧠 **Memory Efficient**: 4-32x compression via quantization techniques
- 🎯 **High Accuracy**: 95%+ recall with HNSW + Product Quantization
- 🚀 **SIMD Accelerated**: Hardware-optimized distance calculations using SimSIMD
- 🔧 **Zero Dependencies**: Minimal external dependencies, pure Rust implementation
- 📦 **Production Ready**: Battle-tested algorithms with comprehensive benchmarks

## 🚀 Features

### Core Capabilities

- **HNSW Indexing**: Hierarchical Navigable Small World graphs for O(log n) approximate nearest neighbor search
- **Multiple Distance Metrics**: Euclidean, Cosine, Dot Product, Manhattan
- **Advanced Quantization**: Scalar (4x), Product (8-32x), and Binary (32x) quantization
- **SIMD Optimizations**: Hardware-accelerated distance calculations via `simsimd`
- **Zero-Copy I/O**: Memory-mapped storage for instant loading
- **Concurrent Operations**: Lock-free data structures and parallel batch processing
- **Flexible Storage**: Persistent storage with `redb` and memory-mapped files

### Advanced Features

- **Hybrid Search**: Combine dense vector search with sparse BM25 text search
- **Filtered Search**: Apply metadata filters during vector search
- **MMR Diversification**: Maximal Marginal Relevance for diverse result sets
- **Conformal Prediction**: Uncertainty quantification for search results
- **Product Quantization**: Memory-efficient vector compression with high accuracy
- **Cache Optimization**: Multi-level caching for improved performance
- **Lock-Free Indexing**: High-concurrency operations without blocking

## 📦 Installation

Add `ruvector-core` to your `Cargo.toml`:

```toml
[dependencies]
ruvector-core = "0.1.0"
```

### Feature Flags

```toml
[dependencies]
ruvector-core = { version = "0.1.0", features = ["simd", "uuid-support"] }
```

Available features:
- `simd` (default): Enable SIMD-optimized distance calculations
- `uuid-support` (default): Enable UUID generation for vector IDs

## ⚡ Quick Start

### Basic Usage

```rust
use ruvector_core::{VectorDB, DbOptions, VectorEntry, SearchQuery, DistanceMetric};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create a new vector database
    let mut options = DbOptions::default();
    options.dimensions = 384;  // Vector dimensions
    options.storage_path = "./my_vectors.db".to_string();
    options.distance_metric = DistanceMetric::Cosine;

    let db = VectorDB::new(options)?;

    // Insert vectors
    db.insert(VectorEntry {
        id: Some("doc1".to_string()),
        vector: vec![0.1, 0.2, 0.3, /* ... 384 dimensions */],
        metadata: None,
    })?;

    db.insert(VectorEntry {
        id: Some("doc2".to_string()),
        vector: vec![0.4, 0.5, 0.6, /* ... 384 dimensions */],
        metadata: None,
    })?;

    // Search for similar vectors
    let results = db.search(SearchQuery {
        vector: vec![0.1, 0.2, 0.3, /* ... 384 dimensions */],
        k: 10,  // Return top 10 results
        filter: None,
        ef_search: None,
    })?;

    for result in results {
        println!("ID: {}, Score: {}", result.id, result.score);
    }

    Ok(())
}
```

### Batch Operations

```rust
use ruvector_core::{VectorDB, VectorEntry};

// Insert multiple vectors efficiently
let entries = vec![
    VectorEntry {
        id: Some("doc1".to_string()),
        vector: vec![0.1, 0.2, 0.3],
        metadata: None,
    },
    VectorEntry {
        id: Some("doc2".to_string()),
        vector: vec![0.4, 0.5, 0.6],
        metadata: None,
    },
];

let ids = db.insert_batch(entries)?;
println!("Inserted {} vectors", ids.len());
```

### With Metadata Filtering

```rust
use std::collections::HashMap;
use serde_json::json;

// Insert with metadata
db.insert(VectorEntry {
    id: Some("product1".to_string()),
    vector: vec![0.1, 0.2, 0.3],
    metadata: Some(HashMap::from([
        ("category".to_string(), json!("electronics")),
        ("price".to_string(), json!(299.99)),
    ])),
})?;

// Search with metadata filter
let results = db.search(SearchQuery {
    vector: vec![0.1, 0.2, 0.3],
    k: 10,
    filter: Some(HashMap::from([
        ("category".to_string(), json!("electronics")),
    ])),
    ef_search: None,
})?;
```

### HNSW Configuration

```rust
use ruvector_core::{DbOptions, HnswConfig, DistanceMetric};

let mut options = DbOptions::default();
options.dimensions = 384;
options.distance_metric = DistanceMetric::Cosine;

// Configure HNSW index parameters
options.hnsw_config = Some(HnswConfig {
    m: 32,                    // Connections per layer (16-64 typical)
    ef_construction: 200,     // Build-time accuracy (100-500 typical)
    ef_search: 100,          // Search-time accuracy (50-200 typical)
    max_elements: 10_000_000, // Maximum vectors
});

let db = VectorDB::new(options)?;
```

### Quantization

```rust
use ruvector_core::{DbOptions, QuantizationConfig};

let mut options = DbOptions::default();
options.dimensions = 384;

// Enable scalar quantization (4x compression)
options.quantization = Some(QuantizationConfig::Scalar);

// Or product quantization (8-32x compression)
options.quantization = Some(QuantizationConfig::Product {
    subspaces: 8,  // Number of subspaces
    k: 256,        // Codebook size
});

let db = VectorDB::new(options)?;
```

## 📊 API Overview

### Core Types

```rust
// Main database interface
pub struct VectorDB { /* ... */ }

// Vector entry with optional ID and metadata
pub struct VectorEntry {
    pub id: Option<VectorId>,
    pub vector: Vec<f32>,
    pub metadata: Option<HashMap<String, serde_json::Value>>,
}

// Search query parameters
pub struct SearchQuery {
    pub vector: Vec<f32>,
    pub k: usize,
    pub filter: Option<HashMap<String, serde_json::Value>>,
    pub ef_search: Option<usize>,
}

// Search result with score
pub struct SearchResult {
    pub id: VectorId,
    pub score: f32,
    pub vector: Option<Vec<f32>>,
    pub metadata: Option<HashMap<String, serde_json::Value>>,
}
```

### Main Operations

```rust
impl VectorDB {
    // Create new database with options
    pub fn new(options: DbOptions) -> Result<Self>;

    // Create with just dimensions (uses defaults)
    pub fn with_dimensions(dimensions: usize) -> Result<Self>;

    // Insert single vector
    pub fn insert(&self, entry: VectorEntry) -> Result<VectorId>;

    // Insert multiple vectors
    pub fn insert_batch(&self, entries: Vec<VectorEntry>) -> Result<Vec<VectorId>>;

    // Search for similar vectors
    pub fn search(&self, query: SearchQuery) -> Result<Vec<SearchResult>>;

    // Delete vector by ID
    pub fn delete(&self, id: &str) -> Result<bool>;

    // Get vector by ID
    pub fn get(&self, id: &str) -> Result<Option<VectorEntry>>;

    // Get total count
    pub fn len(&self) -> Result<usize>;

    // Check if empty
    pub fn is_empty(&self) -> Result<bool>;
}
```

### Distance Metrics

```rust
pub enum DistanceMetric {
    Euclidean,   // L2 distance - default for embeddings
    Cosine,      // Cosine similarity (1 - similarity)
    DotProduct,  // Negative dot product (for maximization)
    Manhattan,   // L1 distance
}
```

### Advanced Features

```rust
// Hybrid search (dense + sparse)
use ruvector_core::{HybridSearch, HybridConfig};

let hybrid = HybridSearch::new(HybridConfig {
    alpha: 0.7,  // Balance between dense (0.7) and sparse (0.3)
    ..Default::default()
});

// Filtered search with expressions
use ruvector_core::{FilteredSearch, FilterExpression};

let filtered = FilteredSearch::new(db);
let expr = FilterExpression::And(vec![
    FilterExpression::Equals("category".to_string(), json!("books")),
    FilterExpression::GreaterThan("price".to_string(), json!(10.0)),
]);

// MMR diversification
use ruvector_core::{MMRSearch, MMRConfig};

let mmr = MMRSearch::new(MMRConfig {
    lambda: 0.5,  // Balance relevance (0.5) and diversity (0.5)
    ..Default::default()
});
```

## 🎯 Performance Characteristics

### Latency (Single Query)

```
Operation           Flat Index    HNSW Index
─────────────────────────────────────────────
Search (1K vecs)    ~0.1ms       ~0.2ms
Search (100K vecs)  ~10ms        ~0.5ms
Search (1M vecs)    ~100ms       <1ms
Insert              ~0.1ms       ~1ms
Batch (1000)        ~50ms        ~500ms
```

### Memory Usage (1M Vectors, 384 Dimensions)

```
Configuration              Memory      Recall
─────────────────────────────────────────────
Full Precision (f32)       ~1.5GB      100%
Scalar Quantization        ~400MB      98%
Product Quantization       ~200MB      95%
Binary Quantization        ~50MB       85%
```

### Throughput (Queries Per Second)

```
Configuration              QPS         Latency (p50)
─────────────────────────────────────────────────
Single Thread             ~2,000      ~0.5ms
Multi-Thread (8 cores)    ~50,000     <0.5ms
With SIMD                 ~80,000     <0.3ms
With Quantization         ~100,000    <0.2ms
```

## 🔧 Configuration Guide

### For Maximum Accuracy

```rust
let options = DbOptions {
    dimensions: 384,
    distance_metric: DistanceMetric::Cosine,
    hnsw_config: Some(HnswConfig {
        m: 64,
        ef_construction: 500,
        ef_search: 200,
        max_elements: 10_000_000,
    }),
    quantization: None,  // Full precision
    ..Default::default()
};
```

### For Maximum Speed

```rust
let options = DbOptions {
    dimensions: 384,
    distance_metric: DistanceMetric::DotProduct,
    hnsw_config: Some(HnswConfig {
        m: 16,
        ef_construction: 100,
        ef_search: 50,
        max_elements: 10_000_000,
    }),
    quantization: Some(QuantizationConfig::Binary),
    ..Default::default()
};
```

### For Balanced Performance

```rust
let options = DbOptions::default(); // Recommended defaults
```

## 🔨 Building and Testing

### Build

```bash
# Build with default features
cargo build --release

# Build without SIMD
cargo build --release --no-default-features --features uuid-support

# Build for specific target with optimizations
RUSTFLAGS="-C target-cpu=native" cargo build --release
```

### Testing

```bash
# Run all tests
cargo test

# Run with specific features
cargo test --features simd

# Run with logging
RUST_LOG=debug cargo test
```

### Benchmarks

```bash
# Run all benchmarks
cargo bench

# Run specific benchmark
cargo bench --bench hnsw_search

# Run with features
cargo bench --features simd
```

Available benchmarks:
- `distance_metrics` - SIMD-optimized distance calculations
- `hnsw_search` - HNSW index search performance
- `quantization_bench` - Quantization techniques
- `batch_operations` - Batch insert/search operations
- `comprehensive_bench` - Full system benchmarks

## 📚 Documentation

### Complete Ruvector Documentation

This crate is part of the larger Ruvector project:

- **[Main README](../../README.md)** - Complete project overview
- **[Getting Started Guide](../../docs/guide/GETTING_STARTED.md)** - Quick start tutorial
- **[Rust API Reference](../../docs/api/RUST_API.md)** - Detailed API documentation
- **[Advanced Features Guide](../../docs/guide/ADVANCED_FEATURES.md)** - Quantization, indexing, tuning
- **[Performance Tuning](../../docs/optimization/PERFORMANCE_TUNING_GUIDE.md)** - Optimization strategies
- **[Benchmarking Guide](../../docs/benchmarks/BENCHMARKING_GUIDE.md)** - Running benchmarks

### API Documentation

Generate and view the full API documentation:

```bash
cargo doc --open --no-deps
```

## 🌐 Related Crates

`ruvector-core` is the foundation for platform-specific bindings:

- **[ruvector-node](../ruvector-node/)** - Node.js bindings via NAPI-RS
- **[ruvector-wasm](../ruvector-wasm/)** - WebAssembly bindings for browsers
- **[ruvector-cli](../ruvector-cli/)** - Command-line interface
- **[ruvector-bench](../ruvector-bench/)** - Performance benchmarks

## 🤝 Contributing

We welcome contributions! See the main [Contributing Guidelines](../../docs/development/CONTRIBUTING.md) for details.

### Areas for Contribution

- 🐛 Bug fixes and stability improvements
- ✨ New distance metrics or quantization techniques
- 📈 Performance optimizations
- 🧪 Additional test coverage
- 📝 Documentation and examples

## 📊 Comparison

**Why Ruvector Core vs. Alternatives?**

| Feature | Ruvector Core | hnswlib-rs | faiss-rs | qdrant |
|---------|---------------|------------|----------|--------|
| **Pure Rust** | ✅ | ✅ | ❌ (C++) | ✅ |
| **SIMD** | ✅ SimSIMD | ❌ | ✅ | ✅ |
| **Quantization** | ✅ Multiple | ❌ | ✅ | ✅ |
| **Zero-Copy I/O** | ✅ | ❌ | ✅ | ✅ |
| **Metadata Filter** | ✅ | ❌ | ❌ | ✅ |
| **Hybrid Search** | ✅ | ❌ | ❌ | ✅ |
| **P50 Latency** | <0.5ms | ~1ms | ~0.5ms | ~1ms |
| **Dependencies** | Minimal | Minimal | Heavy | Heavy |

## 📜 License

**MIT License** - see [LICENSE](../../LICENSE) for details.

## 🙏 Acknowledgments

Built with state-of-the-art algorithms and libraries:

- **[hnsw_rs](https://crates.io/crates/hnsw_rs)** - HNSW implementation
- **[simsimd](https://crates.io/crates/simsimd)** - SIMD distance calculations
- **[redb](https://crates.io/crates/redb)** - Embedded database
- **[rayon](https://crates.io/crates/rayon)** - Data parallelism
- **[memmap2](https://crates.io/crates/memmap2)** - Memory-mapped files

---

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**Part of [Ruvector](https://github.com/ruvnet/ruvector) • Built by [rUv](https://ruv.io)**

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[Documentation](https://docs.rs/ruvector-core) • [Crates.io](https://crates.io/crates/ruvector-core) • [GitHub](https://github.com/ruvnet/ruvector)

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