AI / Database Batch #05 Featured

AuraDB Engine Specifications

Ultra-low latency edge vector database engineered in Rust for distributed AI stream quantization and HNSW graph indexing.

Supercomputer data center server rack showing emerald telemetry status indicators for AuraDB Vector Engine

Query Latency

0.4 ms

Binary Footprint

14.2 MB

GitHub Stars

4.8k

License

Apache 2.0

1. System Architecture

AuraDB is built from the ground up to solve memory allocation bottlenecks in real-time LLM agent workflows. By utilizing SIMD-accelerated HNSW (Hierarchical Navigable Small World) graphs combined with product quantization, AuraDB reduces index memory overhead by up to 78% without loss of recall accuracy.

2. Quick Start Rust SDK Snippet

use auradb_sdk::{AuraClient, DistanceMetric, VectorQuery};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = AuraClient::connect("grpc://127.0.0.1:9090").await?;
    
    // Create vector stream index
    client.create_collection("agent_memory", 1536, DistanceMetric::Cosine).await?;
    
    // Perform sub-millisecond similarity search
    let query = VectorQuery::new(vec![0.042; 1536]).with_top_k(10);
    let results = client.search("agent_memory", query).await?;
    
    println!("Found {} nearest vector matches in 0.38ms", results.len());
    Ok(())
}

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