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.
2021语言与智能技术竞赛:机器阅读理解任务

LICS2021 MRC 1. 项目&任务介绍 本项目基于官方给定的baseline(DuReader-Checklist-BASELINE)进行二次改造,对整个代码框架做了简单的重构,对核心网络结构添加了注释,解耦了数据读取的模块,并添加了阈值确认的功能,一些小的细节也做了改进。 本次任务为202

roar 29 Dec 05, 2022
DataCLUE: 国内首个以数据为中心的AI测评(含模型分析报告)

DataCLUE 以数据为中心的AI测评(DataCLUE) DataCLUE: A Chinese Data-centric Language Evaluation Benchmark 内容导引 章节 描述 简介 介绍以数据为中心的AI测评(DataCLUE)的背景 任务描述 任务描述 实验结果

CLUE benchmark 135 Dec 22, 2022
Toward a Visual Concept Vocabulary for GAN Latent Space, ICCV 2021

Toward a Visual Concept Vocabulary for GAN Latent Space Code and data from the ICCV 2021 paper Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Kl

Sarah Schwettmann 13 Dec 23, 2022
A linter to manage all your python exceptions and try/except blocks (limited only for those who like dinosaurs).

Manage your exceptions in Python like a PRO Currently in BETA. Inspired by this blog post. I shared the building process of this tool here. “For those

Guilherme Latrova 353 Dec 31, 2022
The official repository of the ISBI 2022 KNIGHT Challenge

KNIGHT The official repository holding the data for the ISBI 2022 KNIGHT Challenge About The KNIGHT Challenge asks teams to develop models to classify

Nicholas Heller 4 Jan 22, 2022
PyTorch implementation of Microsoft's text-to-speech system FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.

An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"

Chung-Ming Chien 1k Dec 30, 2022
NLP Text Classification

多标签文本分类任务 近年来随着深度学习的发展,模型参数的数量飞速增长。为了训练这些参数,需要更大的数据集来避免过拟合。然而,对于大部分NLP任务来说,构建大规模的标注数据集非常困难(成本过高),特别是对于句法和语义相关的任务。相比之下,大规模的未标注语料库的构建则相对容易。为了利用这些数据,我们可以

Jason 1 Nov 11, 2021
The Classical Language Toolkit

Notice: This Git branch (dev) contains the CLTK's upcoming major release (v. 1.0.0). See https://github.com/cltk/cltk/tree/master and https://docs.clt

Classical Language Toolkit 754 Jan 09, 2023
(ACL-IJCNLP 2021) Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models.

BERT Convolutions Code for the paper Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models. Contains expe

mlpc-ucsd 21 Jul 18, 2022
wxPython app for converting encodings, modifying and fixing SRT files

Subtitle Converter Program za obradu srt i txt fajlova. Requirements: Python version 3.8 wxPython version 4.1.0 or newer Libraries: srt, PyDispatcher

4 Nov 25, 2022
FactSumm: Factual Consistency Scorer for Abstractive Summarization

FactSumm: Factual Consistency Scorer for Abstractive Summarization FactSumm is a toolkit that scores Factualy Consistency for Abstract Summarization W

devfon 83 Jan 09, 2023
SASE : Self-Adaptive noise distribution network for Speech Enhancement with heterogeneous data of Cross-Silo Federated learning

SASE : Self-Adaptive noise distribution network for Speech Enhancement with heterogeneous data of Cross-Silo Federated learning We propose a SASE mode

Tower 1 Nov 20, 2021
A Python script that compares files in directories

compare-files A Python script that compares files in different directories, this is similar to the command filecmp.cmp(f1, f2). I made this script in

Colvin 1 Oct 15, 2021
SGMC: Spectral Graph Matrix Completion

SGMC: Spectral Graph Matrix Completion Code for AAAI21 paper "Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning". Data Format

Chao Chen 8 Dec 12, 2022
Making text a first-class citizen in TensorFlow.

TensorFlow Text - Text processing in Tensorflow IMPORTANT: When installing TF Text with pip install, please note the version of TensorFlow you are run

1k Dec 26, 2022
Source code for CsiNet and CRNet using Fully Connected Layer-Shared feedback architecture.

FCS-applications Source code for CsiNet and CRNet using the Fully Connected Layer-Shared feedback architecture. Introduction This repository contains

Boyuan Zhang 4 Oct 07, 2022
TEACh is a dataset of human-human interactive dialogues to complete tasks in a simulated household environment.

TEACh is a dataset of human-human interactive dialogues to complete tasks in a simulated household environment.

Alexa 98 Dec 09, 2022
超轻量级bert的pytorch版本,大量中文注释,容易修改结构,持续更新

bert4pytorch 2021年8月27更新: 感谢大家的star,最近有小伙伴反映了一些小的bug,我也注意到了,奈何这个月工作上实在太忙,更新不及时,大约会在9月中旬集中更新一个只需要pip一下就完全可用的版本,然后会新添加一些关键注释。 再增加对抗训练的内容,更新一个完整的finetune

muqiu 317 Dec 18, 2022
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

303 Dec 17, 2022
A Semi-Intelligent ChatBot filled with statistical and economical data for the Premier League.

MONEYBALL - ChatBot Module: 4006CEM, Class: B, Group: 5 Contributors: Jonas Djondo Roshan Kc Cole Samson Daniel Rodrigues Ihteshaam Naseer Kind remind

Jonas Djondo 1 Nov 18, 2021