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sekejap-db

Execute unified graph, vector, spatial, and full-text queries in-process.

6|Updated Feb 7, 2026
Search Tags:#database#vector search#embedded#full-text search#graph#spatial#sekejapql
One-click install
npx skills add https://github.com/insanalamin/sekejap --skill sekejap-db
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sekejap-db
Source: https://github.com/insanalamin/sekejap/tree/main/docs
Command: npx skills add https://github.com/insanalamin/sekejap --skill sekejap-db

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a powerful, embedded database solution that unifies graph, vector, spatial, and full-text search capabilities within a single, efficient query pipeline, eliminating the need for complex joins across disparate systems.

Core Features & Use Cases

  • Unified Querying: Execute complex queries that traverse graph relationships and filter by vector similarity, spatial proximity, or full-text relevance in one go.
  • Embedded Performance: Runs in-process with zero network overhead, ideal for applications requiring high-speed data access.
  • Multi-Model Data: Supports nodes with rich JSON payloads, vectors, GeoJSON geometries, and full-text indexed fields.
  • Use Case: Analyze crime data by finding suspects connected to a specific crime (graph), who are also located near a certain point (spatial), and whose descriptions match certain keywords (full-text), all within a single query.

Quick Start

Use the sekejap-db skill to query for all open robberies near a specific latitude and longitude.

Frequently Asked Questions about sekejap-db

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query graph relationships and spatial proximity in a single database operation?▼

An embedded, graph-first multi-model database engine enables unified queries that traverse graph relationships and filter by spatial proximity simultaneously, eliminating the need for complex joins across disparate systems with zero network overhead.

What is an embedded multi-model database and when do I need one?▼

An embedded multi-model database runs in-process with zero network overhead, unifying graph, vector, spatial, and full-text search within a single query pipeline for Rust and Python applications requiring high-speed data access.

Can I use a graph database for vector similarity search and full-text filtering in Rust?▼

Yes, this embedded database engine supports Rust and Python, allowing you to perform vector similarity search and full-text filtering directly alongside graph traversals on nodes with rich JSON payloads.

What is the best way to combine spatial and full-text search with graph traversals?▼

Using a graph-first multi-model database engine is the best way to combine spatial and full-text search with graph traversals. It supports GeoJSON geometries and full-text indexed fields, enabling unified filtering by location and keywords alongside graph relationship analysis.

Does this embedded database engine support complex JSON payloads and GeoJSON geometries?▼

Yes, the embedded database engine supports multi-model data by handling nodes with rich JSON payloads, vectors, and GeoJSON geometries, enabling efficient storage and querying of complex spatial and structured data in-process.

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