Lightweight Vector Database with Embedded Machine Learning Models
Overview
A lightweight vector database for text retrieval in R with embedded machine learning models and no external API (Application Programming Interface) keys. Supports dense and hybrid search, optional HNSW (Hierarchical Navigable Small World) approximate nearest-neighbor indexing, faceted filters with ACL (Access Control List) metadata, command-line tools, and a local dashboard built with 'shiny'. The HNSW method is described by Malkov and Yashunin (2018) doi:10.1109/TPAMI.2018.2889473.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 130 (22 internal)includes a package overview page
- Examples that run
- 0%23 never run
- Exported functions with examples
- 22%
- Documented parameters
- 100%
- Return-value docs
- 38%
- Help pages citing references
- 0%
Downloads
Repository
7 development-tooling and community-health practices detected across 3 families in the upstream repository
Checks run against github.com/knowusuboaky/vectrixdb-r on 2026-10-18.
The R CMD check workflow runs on Linux
GitHub Pages site last deployed 2026-02-18 · The repository has a site deploy workflow
pkgdown site last built 2026-02-18, before release 1.1.2 (2026-02-20)
Dependencies
Installing VectrixDB adds 31 packages (20 with compiled code, 34 MB of source tarballs). Heaviest: stringi 11 MB, data.table 5.7 MB, RSQLite 4.2 MB.
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- 1.1.2Latest2026-02-20 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-02-20
- Total releases
- 1 / 1 yrs
- License
- Apache License (>= 2)
- Download size
- 148 KB
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