velesdb-core

High-performance vector database engine written in Rust.

Features

• Blazing Fast: HNSW index with explicit SIMD (4x faster than auto-vectorized)
• Persistent Storage: Memory-mapped files for efficient disk access
• Multiple Distance Metrics: Cosine, Euclidean, Dot Product, Hamming, Jaccard
• ColumnStore Filtering: 122x faster than JSON filtering at scale
• Metadata Filtering: Filter search results by payload attributes
• VelesQL: SQL-like query language for vector operations

Installation

cargo add velesdb-core

Quick Start

use velesdb_core::{Database, DistanceMetric};
use serde_json::json;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create a new database
    let db = Database::open("./my_vectors")?;

    // Create a collection with 384-dimensional vectors
    let collection = db.create_collection("documents", 384, DistanceMetric::Cosine)?;

    // Insert vectors with metadata
    collection.upsert(vec![
        (1, vec![0.1; 384], json!({"title": "Hello World", "category": "greeting"})),
        (2, vec![0.2; 384], json!({"title": "Rust Programming", "category": "tech"})),
    ])?;

    // Search for similar vectors
    let query = vec![0.15; 384];
    let results = collection.search(&query, 5)?;

    for result in results {
        println!("ID: {}, Score: {:.4}", result.id, result.score);
    }

    Ok(())
}

Distance Metrics

Performance

• Search latency: < 1ms for 100k vectors
• ColumnStore filtering: 122x faster than JSON at 100k items
• Memory efficient with SQ8 quantization (4x reduction)

License

Business Source License 1.1 (BSL-1.1)

See LICENSE for details.