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.
Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"

GINC small-scale in-context learning dataset GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context

P-Lambda 29 Dec 19, 2022
Text to image synthesis using thought vectors

Text To Image Synthesis Using Thought Vectors This is an experimental tensorflow implementation of synthesizing images from captions using Skip Though

Paarth Neekhara 2.1k Jan 05, 2023
This repo is a C++ version of yolov5_deepsort_tensorrt. Packing all C++ programs into .so files, using Python script to call C++ programs further.

yolov5_deepsort_tensorrt_cpp Introduction This repo is a C++ version of yolov5_deepsort_tensorrt. And packing all C++ programs into .so files, using P

41 Dec 27, 2022
《Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching》(CVPR 2020)

This contains the codes for cross-view geo-localization method described in: Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching, CVPR2020.

41 Oct 27, 2022
Fast algorithms to compute an approximation of the minimal volume oriented bounding box of a point cloud in 3D.

ApproxMVBB Status Build UnitTests Homepage Fast algorithms to compute an approximation of the minimal volume oriented bounding box of a point cloud in

Gabriel Nützi 390 Dec 31, 2022
Implementation of Barlow Twins paper

barlowtwins PyTorch Implementation of Barlow Twins paper: Barlow Twins: Self-Supervised Learning via Redundancy Reduction This is currently a work in

IgorSusmelj 86 Dec 20, 2022
Bayesian Meta-Learning Through Variational Gaussian Processes

vmgp This is the repository of Vivek Myers and Nikhil Sardana for our CS 330 final project, Bayesian Meta-Learning Through Variational Gaussian Proces

Vivek Myers 2 Nov 17, 2022
This repository provides an efficient PyTorch-based library for training deep models.

s3sec Test AWS S3 buckets for read/write/delete access This tool was developed to quickly test a list of s3 buckets for public read, write and delete

Bytedance Inc. 123 Jan 05, 2023
SparseInst: Sparse Instance Activation for Real-Time Instance Segmentation, CVPR 2022

SparseInst 🚀 A simple framework for real-time instance segmentation, CVPR 2022 by Tianheng Cheng, Xinggang Wang†, Shaoyu Chen, Wenqiang Zhang, Qian Z

Hust Visual Learning Team 458 Jan 05, 2023
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

MIRA Lab 33 Dec 07, 2022
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

Introduction PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch. Key features include: Data structure for

Facebook Research 6.8k Jan 01, 2023
Trained on Simulated Data, Tested in the Real World

Trained on Simulated Data, Tested in the Real World

livox 43 Nov 18, 2022
Neural Architecture Search Powered by Swarm Intelligence 🐜

Neural Architecture Search Powered by Swarm Intelligence 🐜 DeepSwarm DeepSwarm is an open-source library which uses Ant Colony Optimization to tackle

288 Oct 28, 2022
Cross View SLAM

Cross View SLAM This is the associated code and dataset repository for our paper I. D. Miller et al., "Any Way You Look at It: Semantic Crossview Loca

Ian D. Miller 99 Dec 09, 2022
Official Pytorch Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images.

IAug_CDNet Official Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images. Overview We propose a

53 Dec 02, 2022
A tight inclusion function for continuous collision detection

Tight-Inclusion Continuous Collision Detection A conservative Continuous Collision Detection (CCD) method with support for minimum separation. You can

Continuous Collision Detection 89 Jan 01, 2023
SwinTrack: A Simple and Strong Baseline for Transformer Tracking

SwinTrack This is the official repo for SwinTrack. A Simple and Strong Baseline Prerequisites Environment conda (recommended) conda create -y -n SwinT

LitingLin 196 Jan 04, 2023
High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models arXiv | BibTeX High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach*, Andreas Blattmann*, Dominik Lorenz

CompVis Heidelberg 5.6k Dec 30, 2022
Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation

VT-UNet This repo contains the supported pytorch code and configuration files to reproduce 3D medical image segmentaion results of VT-UNet. Environmen

Himashi Amanda Peiris 114 Dec 20, 2022
Pyeventbus: a publish/subscribe event bus

pyeventbus pyeventbus is a publish/subscribe event bus for Python 2.7. simplifies the communication between python classes decouples event senders and

15 Apr 21, 2022