srvdb 0.1.6

Zero-Gravity Embedded Vector Database - Offline-first, RAM-efficient vector search

🚀 SvDB - Zero-Gravity Embedded Vector Database

"All the intelligence, none of the weight."

SvDB is a high-performance embedded vector database designed for offline-first, mobile-ready, and RAM-efficient applications. Built in Rust with sub-millisecond search speeds.

✨ Key Features

• 🔌 Offline-First: No network calls, no latency, no cloud bills
• 💾 RAM-Efficient: Uses mmap for zero-copy vector access, OS manages memory
• ⚡ Blazing Fast: Sub-5ms search on 100k vectors using binary quantization
• 📱 Mobile-Ready: Designed for embedded systems, iOS, Android, and IoT
• 🎯 Simple API: Clean Rust trait interface with minimal complexity

🏗️ Architecture

Dual-File System

• vectors.bin: Memory-mapped binary quantized vectors (1 bit/dimension)
• metadata.db: Embedded key-value store (redb) for metadata

Search Pipeline

1. Coarse Search: Hamming distance via XOR/popcount (parallelized with rayon)
2. Fine Rescore: Optional exact similarity for top-k candidates (future)

📦 Installation

Add to your Cargo.toml:

[dependencies]
svdb = "1.0.0"

🚀 Quick Start

use svdb::{SvDB, Vector, VectorEngine};
use anyhow::Result;

fn main() -> Result<()> {
    // Initialize database
    let mut db = SvDB::new("./my_vectors")?;

    // Add a vector (1536 dimensions for OpenAI embeddings)
    let vec = Vector::new(vec![0.1; 1536]);
    let id = db.add(&vec, r#"{"title": "example"}"#)?;

    // Search for similar vectors
    let results = db.search(&vec, 10)?;
    
    for result in results {
        println!("ID: {}, Score: {:.4}", result.id, result.score);
    }

    // Persist to disk
    db.persist()?;
    
    Ok(())
}

📊 Performance

• Latency: < 5ms for 100k vectors (standard mobile CPU)
• Memory: < 50MB baseline overhead
• Binary Size: < 10MB (stripped release build)
• Storage: 192 bytes per vector (1536 dimensions)

🛠️ Tech Stack

• Storage: memmap2 for vectors, redb for metadata
• Parallelism: rayon for multi-threaded search
• Quantization: Binary quantization (1 bit per dimension)
• Error Handling: anyhow + thiserror

📖 Examples

Run the basic example:

cargo run --example basic

🧪 Testing

# Run all tests
cargo test

# Run with output
cargo test -- --nocapture

# Run benchmarks (when implemented)
cargo bench

🎯 Use Cases

• Mobile AI: On-device semantic search without cloud dependency
• IoT Devices: Lightweight vector search on resource-constrained hardware
• Offline Apps: RAG systems that work without internet
• Edge Computing: Vector search at the edge with minimal latency

📝 API Reference

VectorEngine Trait

pub trait VectorEngine {
    fn new(path: &str) -> Result<Self>;
    fn add(&mut self, vec: &Vector, meta: &str) -> Result<u64>;
    fn search(&self, query: &Vector, k: usize) -> Result<Vec<SearchResult>>;
    fn get_metadata(&self, id: u64) -> Result<Option<String>>;
    fn persist(&mut self) -> Result<()>;
}

Types

pub struct Vector {
    pub data: Vec<f32>,  // 1536 dimensions
}

pub struct SearchResult {
    pub id: u64,
    pub score: f32,           // 0.0 to 1.0
    pub metadata: Option<String>,
}

🗺️ Roadmap

• MVP: Binary quantization + Hamming search
• Product quantization for better accuracy
• HNSW graph index for faster search
• ARM NEON SIMD optimizations
• C FFI bindings for mobile (iOS/Android)
• Incremental indexing (hot updates)
• Metadata filtering (WHERE clause)

🤝 Contributing

Contributions welcome! Please check the issues page.

📄 License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT license (LICENSE-MIT)

at your option.

🙏 Acknowledgments

Built following the principles from the PRD for constraint-driven AI development.

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Made with ⚡ and 🦀 by the SvDB team