Sekejap-DB

A graph-first, embedded multi-model database engine for Rust and Python.

1: Overview

Sekejap-DB is a graph-native database designed for high-performance, relationship-heavy workloads like Root Cause Analysis (RCA), RAG, and Agentic AI.

It unifies Graph, Vector, Spatial, and Full-Text search into a single, cohesive engine where the Graph acts as the primary structure and other models serve as attributes or filters.

1.1 Features

• HNSW Engine: Custom, SIMD-accelerated HNSW implementation for high-concurrency vector search.
• Graph-First: Relationships are first-class citizens. Queries traverse edges to prune the search space.
• Hybrid Querying: Native Index Intersection allows combining Graph, Vector, Spatial, and Text conditions via a pipeline API.
• Embedded: Runs directly in your application process (Rust/Python). Zero network overhead.
• Zero-Copy Storage: Memory-mapped arenas for ultra-fast data access.

2: Main Usage (Rust & Python)

2.1 Basic CRUD Operations

Write Data

Rust:

use sekejap::SekejapDB;

// Initialize with path and capacity
let db = SekejapDB::new(std::path::Path::new("./data"), 1_000_000)?;

// Simple write
db.nodes().put("event/001", r#"{"title": "Theft", "severity": "high"}"#)?;

// JSON with Vector & Geo
db.nodes().put("news/flood-2026", r#"{
    "title": "Flood in Jakarta",
    "vectors": { "dense": [0.1, 0.2, 0.3] },
    "geo": { "loc": { "lat": -6.2, "lon": 106.8 } }
}"#)?;

Python:

import sekejap

# Initialize with path and capacity
db = sekejap.SekejapDB("./data", capacity=1000000)

# Simple write
db.put("event/001", '{"title": "Theft", "severity": "high"}')

# JSON with Vector & Geo
db.put("news/flood-2026", """
{
    "title": "Flood in Jakarta",
    "vectors": { "dense": [0.1, 0.2, 0.3] },
    "geo": { "loc": { "lat": -6.2, "lon": 106.8 } }
}
""")

Read Data

Rust:

if let Some(event) = db.nodes().get("news/flood-2026") {
    println!("Found: {}", event);
}

Python:

event = db.get("news/flood-2026")
if event:
    print(f"Found: {event}")

Delete Data

Rust:

db.nodes().remove("event/001")?;

Python:

db.remove("event/001")

2.2 Edges & Traversal

Link Nodes

Rust:

db.edges().link("poverty", "crime/001", "causal", 0.8)?;

Python:

db.link("poverty", "crime/001", "causal", 0.8)

Hybrid Query Pipeline

Find events starting from "cuisine/italian", traversing backward via "related", and filtering by rating.

Rust:

let results = db.nodes().one("cuisine/italian")
    .backward("related")
    .where_gt("rating", 4.5)
    .collect()?;

Python:

results = db.backward("cuisine/italian", "related")
# Filtered results currently return as PyHit list
for hit in results:
    print(hit.payload)

3: Building and Testing

# Run tests
cargo test --all-features

# Run benchmarks
cargo run --example graduation_benchmark --all-features

# Check for errors
cargo check --features all

License

MIT