{"name":"DigUp: Search Local Files with Google DeepMind EmbeddingGemma 2","description":"DigUp searches photos, PDFs, documents, audio, video and optionally code by meaning, across macOS, Windows and Linux. Built on Google DeepMind’s EmbeddingGemma 2 and llama.cpp, it indexes and searches files on-device.","github":"https://github.com/ARahim3/DigUp","url":"https://osrepos.com/repo/arahim3-digup","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/arahim3-digup","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/arahim3-digup.md","json":"https://osrepos.com/repo/arahim3-digup.json","topics":["desktop-app","local-first","privacy","cross-platform","semantic-search","multimodal-search","file-search"],"keywords":["desktop-app","local-first","privacy","cross-platform","semantic-search","multimodal-search","file-search"],"stars":null,"summary":"DigUp searches photos, PDFs, documents, audio, video and optionally code by meaning, across macOS, Windows and Linux. Built on Google DeepMind’s EmbeddingGemma 2 and llama.cpp, it indexes and searches files on-device.","content":"## Overview\n\nDigUp is a cross-platform desktop file search app built on Google DeepMind’s EmbeddingGemma 2 and llama.cpp. It lets you describe what you remember from a file, rather than relying only on its name or exact text, and searches the selected folders on macOS, Windows and Linux.\n\nIts appeal is finding related content across file types: a query can match a photo, a passage in a PDF, a point in a recording or video, or code when code search is enabled. Indexing and search run on your computer, which suits people who want semantic search without sending their files to a hosted service.\n\n## Key Features\n\n- Searches images, PDFs, documents, audio and video by their content, with results that can open at a relevant page or moment.\n- Uses EmbeddingGemma 2’s shared text, image, audio and video embedding space to search across different kinds of files.\n- Combines semantic matches with exact-word matching for terms such as names, invoice numbers and error codes.\n- Supports cross-language search across the model’s supported languages.\n- Offers optional, separate code search for selected project folders, with results that can open in an editor at the matching line.\n- Keeps its index and model on-device; the README says files are not uploaded and there is no telemetry.\n- Provides a global shortcut and menu-bar or system-tray access on macOS, Windows and Linux.\n\n## Use Cases\n\n- Find a photo or screenshot by describing what it shows, even when its filename is unhelpful.\n- Locate a statement in a lease or other long PDF and open the result at the relevant page.\n- Jump to a particular topic or event in a podcast or video without manually searching through the recording.\n- Search a local codebase by describing a function’s behavior, or by entering its name, when code search is enabled.\n- Find files across languages or formats without uploading a work or personal archive to a search service.\n\n## Our Take\n\nDigUp is a promising, actively changing local search app, but its newness and dependence on a single contributor make it worth evaluating before relying on it for essential workflows.\n\n**Good fit if:**\n- You want private, on-device semantic search across media and documents on macOS, Windows or Linux.\n- You are comfortable trying a beta on Windows or Linux, or a fast-moving new project on any supported platform.\n- You want optional code search alongside ordinary file search, with an MIT-licensed project you can inspect or build.\n\n**Look elsewhere if:**\n- You need a mature project with a broader contributor base; the measured activity is concentrated entirely in one contributor.\n- You need text recognition in screenshots on Windows or Linux, or need older Word documents, RAW photos or SVG files indexed on those platforms.\n- You need a hosted, centrally managed search service rather than an index maintained on each computer.\n\n## Project Health\n\n| Signal | Value |\n|---|---|\n| Status | **New**: less than six months old and changing fast |\n| Last commit | 2026-10-14 (today) |\n| Commits, last 90 days | 100+ |\n| Releases, last 12 months | 3 (latest v0.7.0-beta.1, 2026-10-14, pre-release) |\n| Contributors | 1 |\n| Issues closed, last 90 days | 8 (typically closed in 2 days) |\n| Pull requests merged, last 90 days | 0 |\n| Project age | 5 days |\n\nChecked on 2026-10-14 with the GitHub API.\n\n## Project Facts\n\n- Language: Swift\n- License: MIT\n- Stars: 3.5k\n- Forks: 235\n- Topics: embeddinggemma, embeddings, file-search, linux, llama-cpp, local-first, macos, menubar-app\n- Archived: no\n\n## What You Need\n\nDetected in the repository:\n\n- Swift (main language on GitHub)\n- A test suite and automated checks on GitHub Actions\n\n## Getting Started\n\nOn a Mac, install with Homebrew:\n\n```sh\nbrew install --cask arahim3/tap/digup\n```\n\nWindows and Linux builds are marked beta in the README. See the [README](https://github.com/ARahim3/DigUp#readme) for downloads, platform-specific setup and build instructions.\n\n## License in Plain Words\n\n**MIT** (permissive).\n\n- Commercial use: yes\n- Modify and redistribute: yes\n- You must keep: the copyright and license notice\n- Share your changes: no\n\nA summary, not legal advice: the LICENSE file is what applies.\n\n## Alternatives\n\n- [lilbee](https://osrepos.com/repo/tobocop2-lilbee): lilbee searches files, code, and websites to provide cited answers through several interfaces, while DigUp focuses on semantic search across local files.\n\n| Project | Language | License | Stars | Status |\n|---|---|---|---|---|\n| **DigUp** | Swift | MIT | 3.5k | New |\n| [lilbee](https://osrepos.com/repo/tobocop2-lilbee) | Python | MIT | 62 | Active |\n\n## Considerations\n\n- The project is very new and changing quickly. Recent activity includes frequent commits and three releases in the past year, with the latest tagged release marked as a beta. Measured issue handling shows issues being closed, but no merged pull requests in the last 90 days.\n- Development is concentrated in one contributor. That is a maintenance and continuity risk for teams depending on long-term support.\n- The first setup downloads an 865 MB model, and initial indexing can take substantial time for large collections. The README notes that CPU-only indexing on Windows and Linux can be slow for large photo libraries; a GPU is used when available and found to be faster, but is not presented as a requirement.\n- Platform support is not identical. Screenshot text recognition is currently limited to macOS, as are some older document and image formats. Windows and Linux are described as beta.\n- The README says indexing is limited to selected folders and excludes cloud-only files, hidden or sensitive-looking folders, and several generated or dataset-like files. Check its file-handling rules against your needs before indexing a work collection.\n- DigUp is MIT-licensed. The README also identifies third-party components with their own licenses, including EmbeddingGemma 2 under Apache 2.0 and FFmpeg under LGPL; review bundled license notices when redistributing packages.","metrics":{"detailViews":5,"githubClicks":3},"dates":{"published":null,"modified":"2026-10-14T20:28:12.000Z"}}