hypertag 0.5.6.1

File organization made easy using tags and machine learning

HyperTag

File organization made easy. HyperTag let's humans intuitively express how they think about their files using tags.

Objective Function: Minimize time between a thought and access to all relevant files.

Install

Available on PyPI

$ pip install hypertag

Community

Join the HyperTag matrix chat room to stay up to date on the latest developments.

Overview

HyperTag offers a slick CLI but more importantly it creates a directory called HyperTagFS which is a file system based representation of your files and tags using symbolic links and directories.

Directory Import: Import your existing directory hierarchies using $ hypertag import path/to/directory. HyperTag converts it automatically into a tag hierarchy using metatagging.

Semantic Text & Image Search (Experimental): Search for images (jpg, png) and text documents (yes, even PDF's) content with a simple text query. Text search is powered by the awesome Sentence Transformers library. Text to image search is powered by OpenAI's CLIP model. Currently only English queries are supported.

HyperTag Daemon (Experimental): Monitors HyperTagFS and directories added to the auto import list for user changes (see section "Start HyperTag Daemon" below). Also spawns the DaemonService which speeds up semantic search significantly.

Fuzzy Matching Queries: HyperTag uses fuzzy matching to minimize friction in the unlikely case of a typo.

File Type Groups: HyperTag automatically creates folders containing common files (e.g. Images: jpg, png, etc., Documents: txt, pdf, etc., Source Code: py, js, etc.), which can be found in HyperTagFS.

HyperTag Graph: Quickly get an overview of your HyperTag Graph! HyperTag visualizes the metatag graph on every change and saves it at HyperTagFS/hypertag-graph.pdf.

CLI Functions

Import existing directory recursively

Import files with tags inferred from the existing directory hierarchy.

$ hypertag import path/to/directory

Tag file/s

Manually tag files. Shortcut: $ hypertag t

$ hypertag tag humans/*.txt with human "Homo Sapiens"

Untag file/s

Manually remove tag/s from file/s.

$ hypertag untag humans/*.txt with human "Homo Sapiens"

Tag a tag

Metatag tag/s to create tag hierarchies. Shortcut: $ hypertag tt

$ hypertag metatag human with animal

Merge tags

Merge all associations (files & tags) of tag A into tag B.

$ hypertag merge human into "Homo Sapiens"

Query using Set Theory

Print file names of the resulting set matching the query. Queries are composed of tags and operands. Tags are fuzzy matched for convenience. Nesting is currently not supported, queries are evaluated from left to right.
Shortcut: $ hypertag q

Print paths: $ hypertag query human --path
Print fuzzy matched tag: $ hypertag query man --verbose
Disable fuzzy matching: $ hypertag query human --fuzzy=0

Default operand is AND (intersection):
$ hypertag query human "Homo Sapiens" is equivalent to $ hypertag query human and "Homo Sapiens"

OR (union):
$ hypertag query human or "Homo Sapiens"

MINUS (difference):
$ hypertag query human minus "Homo Sapiens"

Index supported image and text files

Only indexed files can be searched.

$ hypertag index

To parse even unparseable PDF's, install tesseract: # pacman -S tesseract tesseract-data-eng

Index only image files: $ hypertag index --image
Index only text files: $ hypertag index --text

Semantic search for text files

Print text file names sorted by matching score. Performance benefits greatly from running the HyperTag daemon.
Shortcut: $ hypertag s

$ hypertag search "your important text query" --path --score --top_k=10

Semantic search for image files

Print image file names sorted by matching score. Performance benefits greatly from running the HyperTag daemon.
Shortcut: $ hypertag si

Text to image: $ hypertag search_image "your image content description" --path --score --top_k=10

Image to image: $ hypertag search_image "path/to/image.jpg" --path --score --top_k=10

Start HyperTag Daemon

Start daemon process with triple functionality:

• Watches HyperTagFS directory for user changes
  • Maps file (symlink) and directory deletions into tag / metatag removal/s
  • On directory creation: Interprets name as set theory tag query and automatically populates it with results
  • On directory creation in Search Images or Search Texts: Interprets name as semantic search query (add top_k=42 to limit result size) and automatically populates it with results
• Watches directories on the auto import list for user changes:
  • Maps file changes (moves & renames) to DB
  • On file creation: Adds new file/s with inferred tag/s
• Spawns DaemonService to load and expose models used for semantic search, speeding it up significantly

$ hypertag daemon

Print all tags of file/s

$ hypertag tags filename1 filename2

Print all metatags of tag/s

$ hypertag metatags tag1 tag2

Print all tags

$ hypertag show

Print all files

Print names: $ hypertag show files

Print paths: $ hypertag show files --path

Visualize HyperTag Graph

Visualize the metatag graph hierarchy (saved at HyperTagFS root).

$ hypertag graph

Specify layout algorithm (default: fruchterman_reingold):

$ hypertag graph --layout=kamada_kawai

Generate HyperTagFS

Generate file system based representation of your files and tags using symbolic links and directories.

$ hypertag mount

Add directory to auto import list

Directories added to the auto import list will be monitored by the daemon for new files or changes.

$ hypertag add_auto_import_dir path/to/directory

Set HyperTagFS directory path

Default is the user's home directory.

$ hypertag set_hypertagfs_dir path/to/directory

Architecture

• Python and it's vibrant open-source community power HyperTag
• Many other awesome open-source projects make HyperTag possible (listed in pyproject.toml)
• SQLite3 serves as the meta data storage engine (located at ~/.config/hypertag/hypertag.db)
• Symbolic links are used to create the HyperTagFS directory structure
• Semantic text search is powered by the awesome DistilBERT
• Text to image search is powered by OpenAI's impressive CLIP model

Development

• Clone repo: $ git clone https://github.com/SeanPedersen/HyperTag.git
• $ cd HyperTag/
• Install Poetry
• Install dependencies: $ poetry install
• Activate virtual environment: $ poetry shell
• Run all tests: $ pytest -v
• Run formatter: $ black hypertag/
• Run linter: $ flake8
• Run type checking: $ mypy **/*.py
• Run security checking: $ bandit --exclude tests/ -r .
• Run HyperTag: $ python -m hypertag

Inspiration

What is the point of HyperTag's existence?
HyperTag offers many unique features such as the import, semantic search for images and texts, graphing and fuzzy matching functions that make it very convenient to use. All while HyperTag's code base staying tiny at <1500 LOC in comparison to TMSU (>10,000 LOC) and SuperTag (>25,000 LOC), making it easy to hack on.

This project is partially inspired by these open-source projects:

• TMSU: Written in Go
• SuperTag: Written in Rust