#4141st of 49 in Desktop AI apps
digup
Desktop app that searches local files by meaning across text, images, audio and video
- Stars
- 6.4k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Swift
8 on 6 Oct 20266,373 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
DigUp indexes folders you choose (images, PDFs, documents, spreadsheets, audio, video, optionally code) using EmbeddingGemma 2 run through llama.cpp, and stores vectors plus a keyword index in one SQLite file. A global shortcut opens a search panel that ranks by meaning and exact words, then opens PDFs at the page and media at the moment. Indexing and search run locally; the network is used only for the model download and update checks.
Who it is for: People who want private semantic search of their own files
Strengths
- One shared embedding space lets a text query match images, PDF pages, audio and video frames
- Exact-string boost finds invoice numbers and error codes alongside semantic matches
- Opens results at the PDF page, media timestamp or code line in your editor
- No account or telemetry; files are only read, never modified or uploaded
Weaknesses
- Mac build needs Apple Silicon and macOS 14+; Windows and Linux builds are beta
- Screenshot text recognition works only on macOS; legacy .doc, RAW and SVG are Mac-only
- CPU-only first index is slow: about 5 seconds per picture on a 2017 Core i5
- Initial 865 MB model download; Windows installer is unsigned
What it needs
- GPU optional
- Needs llama.cpp, SQLite, EmbeddingGemma 2 (865 MB download)
- Models: EmbeddingGemma 2 (8-bit GGUF)
Quick answers
Is digup open source?
Yes. Its code is on GitHub under the MIT license.
Does digup need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
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