rryam/VecturaKit
Swift-based vector database for on-device RAG using MLTensor and MLX Embedders
This is a tool for Swift developers building on-device applications for Apple platforms. It helps you integrate AI search capabilities directly into your app without relying on external servers. You provide text or data, and it generates and stores embeddings locally, allowing your app to perform fast, intelligent searches on that content. This is for app developers creating rich, offline-capable AI experiences.
263 stars.
Use this if you are a Swift developer building an iOS, macOS, watchOS, tvOS, or visionOS app and want to add offline-capable, AI-powered semantic search or retrieval-augmented generation (RAG) directly within the app.
Not ideal if you are not a Swift developer, or if your application requires large-scale, server-side vector database operations.
Stars
263
Forks
25
Language
Swift
License
MIT
Category
Last pushed
Mar 05, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/rryam/VecturaKit"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
alibaba/zvec
A lightweight, lightning-fast, in-process vector database
devflowinc/trieve
All-in-one platform for search, recommendations, RAG, and analytics offered via API
matte1782/edgevec
High-performance vector search for Browser, Node, and Edge
KyroDB/KyroDB
Autonomous Vector database for AI agents and RAG. Hybrid Semantic Cache eliminates cold-cache...
Build5Nines/SharpVector
Lightweight, In-memory, Semantic Search, Text Vector Database to embed in any .NET Application