Knowledge Management for Humans using Machine Learning & Tags

Overview

HyperTag

HyperTag helps humans intuitively express how they think about their files using tags and machine learning. Represent how you think using tags. Find what you look for using semantic search for your text documents (yes, even PDF's) and images. Instead of introducing proprietary file formats like other existing file organization tools, HyperTag just smoothly layers on top of your existing files without any fuss.

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

Accompanying blog post: https://blog.neotree.uber.space/posts/hypertag-file-organization-made-for-humans

Table of Contents

Install

Available on PyPI

$ pip install hypertag (supports both CPU only & CUDA accelerated execution!)

Community

Join the HyperTag matrix chat room to stay up to date on the latest developments or to ask for help.

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 (warning: daemon process is a RAM hog with ~2GB usage).

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.

HyperTag Graph Example

CLI Functions

Import existing directory recursively

Import files with tags inferred from the existing directory hierarchy.

$ hypertag import path/to/directory

Add file/s or URL/s manually

$ hypertag add path/to/file https://github.com/SeanPedersen/HyperTag

Tag file/s (with values)

Manually tag files. Shortcut: $ hypertag t

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

Add a value to a file's tag:

$ hypertag tag sean.txt with name="Sean Pedersen"

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 (with values) and operands. Tags are fuzzy matched for convenience. Nesting is currently not supported, queries are evaluated from left to right.
Shortcut: $ hypertag q

Query with a value using a wildcard: $ hypertag query name="Sean*"
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 name="Sean*" is equivalent to $ hypertag query human and name="Sean*"

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

A custom search algorithm combining semantic with token matching search. 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 and auto-indexes it (if supported file format).
  • 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)
  • Added URLs are saved in ~/.config/hypertag/web_pages for websites, others in ~/.config/hypertag/downloads
  • Symbolic links are used to create the HyperTagFS directory structure
  • Semantic Search: boosted using hnswlib
    • Text to text search is powered by the awesome DistilBERT
    • Text to image & image to image search is powered by OpenAI's impressive CLIP model

Development

  • Find prioritized issues here: TODO List
  • Pick an issue and comment how you plan to tackle it before starting out, to make sure no dev time is wasted.
  • 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 .
  • Codacy: Dashboard
  • 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, graphing and fuzzy matching functions that make it very convenient to use. All while HyperTag's code base staying relatively tiny at <2000 LOC compared to similar projects like TMSU (>10,000 LOC in Go) and SuperTag (>25,000 LOC in Rust), making it easy to hack on.

Owner
Ravn Tech, Inc.
Rapidly Emerging & Adapting Flock
Ravn Tech, Inc.
COD-Rank-Localize-and-Segment (CVPR2021)

COD-Rank-Localize-and-Segment (CVPR2021) Simultaneously Localize, Segment and Rank the Camouflaged Objects Full camouflage fixation training dataset i

JingZhang 52 Dec 20, 2022
Framework that uses artificial intelligence applied to mathematical models to make predictions

LiconIA Framework that uses artificial intelligence applied to mathematical models to make predictions Interface Overview Table of contents [TOC] 1 Ar

4 Jun 20, 2021
The official re-implementation of the Neurips 2021 paper, "Targeted Neural Dynamical Modeling".

Targeted Neural Dynamical Modeling Note: This is a re-implementation (in Tensorflow2) of the original TNDM model. We do not plan to further update the

6 Oct 05, 2022
Code for ECIR'20 paper Diagnosing BERT with Retrieval Heuristics

Bert Axioms This is the repository with the code for the Paper Diagnosing BERT with Retrieval Heuristics Required Data In order to run this code, you

Arthur Câmara 5 Jan 21, 2022
This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-grained Classification".

HA-in-Fine-Grained-Classification This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-g

16 Oct 29, 2022
GAN encoders in PyTorch that could match PGGAN, StyleGAN v1/v2, and BigGAN. Code also integrates the implementation of these GANs.

MTV-TSA: Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions. This is the official code release fo

owl 37 Dec 24, 2022
Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP

Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP Abstract: We introduce a method that allows to automatically se

Daniil Pakhomov 134 Dec 19, 2022
A PyTorch Implementation of "Neural Arithmetic Logic Units"

Neural Arithmetic Logic Units [WIP] This is a PyTorch implementation of Neural Arithmetic Logic Units by Andrew Trask, Felix Hill, Scott Reed, Jack Ra

Kevin Zakka 181 Nov 18, 2022
AI grand challenge 2020 Repo (Speech Recognition Track)

KorBERT를 활용한 한국어 텍스트 기반 위협 상황인지(2020 인공지능 그랜드 챌린지) 본 프로젝트는 ETRI에서 제공된 한국어 korBERT 모델을 활용하여 폭력 기반 한국어 텍스트를 분류하는 다양한 분류 모델들을 제공합니다. 본 개발자들이 참여한 2020 인공지

Young-Seok Choi 23 Jan 25, 2022
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method (NeurIPS 2021)

Skyformer This repository is the official implementation of Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr"om Method (NeurIPS 2021).

Qi Zeng 46 Sep 20, 2022
TensorFlow for Raspberry Pi

TensorFlow on Raspberry Pi It's officially supported! As of TensorFlow 1.9, Python wheels for TensorFlow are being officially supported. As such, this

Sam Abrahams 2.2k Dec 16, 2022
LLVIP: A Visible-infrared Paired Dataset for Low-light Vision

LLVIP: A Visible-infrared Paired Dataset for Low-light Vision Project | Arxiv | Abstract It is very challenging for various visual tasks such as image

CVSM Group - email: <a href=[email protected]"> 377 Jan 07, 2023
The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

PRIMER The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. PRIMER is a pre-trained model for mu

AI2 111 Dec 18, 2022
"Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion"(WWW 2021)

STAR_KGC This repo contains the source code of the paper accepted by WWW'2021. "Structure-Augmented Text Representation Learning for Efficient Knowled

Bo Wang 60 Dec 26, 2022
A repository built on the Flow software package to explore cyber-security attacks on intelligent transportation systems.

A repository built on the Flow software package to explore cyber-security attacks on intelligent transportation systems.

George Gunter 4 Nov 14, 2022
Network Compression via Central Filter

Network Compression via Central Filter Environments The code has been tested in the following environments: Python 3.8 PyTorch 1.8.1 cuda 10.2 torchsu

2 May 12, 2022
Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

Weakly_detector Tensorflow implementation of "Learning Deep Features for Discriminative Localization" B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and

Taeksoo Kim 363 Jun 29, 2022
PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation

PyGRANSO PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation Please check https://ncvx.org/PyGRANSO for detailed instructions (introd

SUN Group @ UMN 26 Nov 16, 2022
CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary.

CUP-DNN CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary. The model was trained on the expre

1 Oct 27, 2021
Location-Sensitive Visual Recognition with Cross-IOU Loss

The trained models are temporarily unavailable, but you can train the code using reasonable computational resource. Location-Sensitive Visual Recognit

Kaiwen Duan 146 Dec 25, 2022