Vidyut provides reliable infrastructure for Sanskrit software. Our main focus is on building libraries for natural language processing.
Vidyut compiles to fast, safe, and memory-efficient native code, and it can be bound to other programming languages with minimal work. We commit to providing first-class support for Python bindings through vidyut-py, and we are eager to help you create bindings for your language of choice.
Vidyut is an ambitious and transformative project, and you can help us make it a success. If you simply want to join our community of Sanskrit enthusiasts, see the Community section -- we are very friendly and welcome members of all backgrounds. For specific details on how you can contribute, see the Contributing section instead.
Vidyut is under active development as part of the Ambuda project and is published under the MIT license.
Vidyut is meant for programmers who are building Sanskrit software. If you are not comfortable writing software or using tools like a command line interface, we recommend that you use the tools on Ambuda instead.
We currently offer two ways to use Vidyut:
We provide first-class support for Python through the vidyut Python package, which we define in the vidyut-py repo. If you have Python installed on your machine, you can install Vidyut as follows.
$ pip install vidyutVidyut is implemented in Rust, which provides low-level control with high-level ergonomics. You can install Rust on your computer by following the instructions here.
Once you've installed Rust, you can try cloning the Vidyut repo and running our tests:
$ git clone https://github.com/ambuda-org/vidyut.git
$ cd vidyut
$ make testYour first build will likely take a few minutes, but future builds will be much faster.
To learn how to navigate this repo, see the Components section. For details on how to get involved, see the Contributing section.
Vidyut contains several standard components for common Sanskrit processing tasks. These components work together well, but you can also use them independently depending on your use case.
In Rust, components of this kind are called crates.
vidyut-cheda segments Sanskrit expressions into words then annotates those
words with their morphological data. Our segmenter is optimized for real-time
and interactive usage: it is fast, low-memory, and capably handles pathological
input.
For details, see the vidyut-cheda README.
vidyut-kosha defines a key-value store that can compactly map tens of
millions of Sanskrit words to their inflectional data. Depending on the
application, storage costs can be as low as 1 byte per word. This storage
efficiency comes at the cost of increased lookup time, but in practice, we have
found that this increase is negligible and well worth the efficiency gains
elsewhere.
For details, see the vidyut-kosha README.
vidyut-prakriya generates Sanskrit words with their prakriyās (derivations)
according to the rules of Paninian grammar. Our long-term goal is to provide a
complete implementation of the Ashtadhyayi.
For details, see the vidyut-prakriya README.
vidyut-sandhi contains various utilities for working with sandhi changes
between words. It is fast, simple, and appropriate for most use cases.
For details, see the vidyut-sandhi README.
To view documentation for all crates (including private modules and structs),
run make docs. This command will generate Rust's standard documentation and
open it in your default web browser.
Vidyut is an ambitious and tranformative project, and you can help us build it. Depending on your background and skills, there are different ways you can contribute.
First, we recommend joining our community so that you can follow along with progress on Ambuda and Vidyut and participate in discussions around them.
If you use a tool that depends on Vidyut, please file GitHub issues when you see errors or surprising behavior. Please also feel free to file issues for feature requests. We'll do our best to accommodate them.
If you know Sanskrit, please give us detailed feedback on any mistakes you see
and what you think the correction should be. This kind of work is especially
valuable for vidyut-prakriya.
If you can program, we encourage you to learn some Rust and get
involved with Vidyut directly. We encourage you to be bold and make pull
requests for work that you think will improve the project. Or if you would like
some pointers on where to get started, you can explore the issues in our issue
tracker. All of our open work items are listed there, and we
encourage you to create a PR for any open issue. Issues tagged with sanskrit
require some basic familiarity with Sanskrit, and issues tagged with
vyakarana require a much deeper level of Sanskrit grammatical knowledge.
If you are familiar with machine learning as well, we are always eager for
improvements to vidyut-cheda. Our current model use simple bigram statistics;
there is plenty of room to improve!
If you want to pursue an open-ended research project, here are the components we are most excited about:
- dependency parsing and anvaya generation
- search indexing that accounts for sandhi and Sanskrit's complex morphology.
- transliteration, perhaps through a port of Aksharamukha
- meter recognition
- support for Vedic Sanskrit
- implementations of non-Paninian grammars
And if there's something else you're excited about, please let us know about it -- we'll probably be excited about it too!
If you're excited about our work on Vidyut, we would love to have you join our community.
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Most of our conversation occurs on Ambuda's Discord server on the
#nlpchannel, where you can chat directly with our team and get fast answers to your questions. We also schedule time to spend together virtually, usually on a weekly frequency. -
Occasional discussion related to Vidyut might also appear on ambuda-discuss or on standard mailing lists like sanskrit-programmers.
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You can also follow along with project announcements on ambuda-announce.