rryam / VecturaKit
Swift-based vector database for on-device RAG using MLTensor and MLX Embedders
Why this job: says: vector database; topics: rag, rag
What it does
- Pluggable Embeddings. Bring any provider that conforms to VecturaEmbedder.
- Auto-Dimension Detection. Automatically detects embedding dimensions from the configured embedder.
- On-Device Storage. Stores and manages vector embeddings locally.
- Hybrid Search. Combines vector similarity with BM25 text search for relevant search results (VecturaKit).
- Pluggable Search Engines. Implement custom search algorithms by conforming to the VecturaSearchEngine protocol.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 2 months ago
- Stars
- 328
- +2 this week
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- 6.3.0
- Released 3 months ago
- Built with
- Swift
- 100% of the code
Is it looked after?
- Contributors
- 1
- Last 90 days
- Issues
- 100% closed
- 0 still open
- Pull requests
- 59 merged
- 2 waiting
- Releases
- 30
- 16 in the last year
- No contributing guide
- No security policy
- No code of conduct
More: languages, topics and project record
Languages
- Swift 100%
Topics
- mlx-swift
- rag
- swift
Project record
- JobRAG and search
- StatusListed
- Commits324
- Forks30
- Watching2
- Stars in 28 days+4
- CreatedJan 17, 2025
- First seen by usNov 7, 2026
- CheckedNov 7, 2026