Jaseci Installation Guide

Welcome to the Jaseci! We are excited that you have decided to explore the world of Jaseci, and we are here to assist you in the installation process.

Jaseci offers a range of packages that can enhance the production of bleeding edge AI, including the Jaseci NLP library, Jaseci Speech library, Jaseci Miscellaneous library, and JAC Vision. Additionally, the Jaseci VS Code Extension provides syntax highlighting and auto-complete features, while Jaseci Studio allows Jaseci hackers to develop programs and visualize the graph output seamlessly. Furthermore, Jaseci Studio includes features that enable you to test and develop JAC programs, which we will cover in later sections.

Let's get started with the installation process and unlock the full potential of Jaseci!

Jaseci is tested and supported on the following systems:

• Ubuntu 18.04 or later
• Windows WSL2
• macOS* (see note below for compatibility with Apple silicon)

Windows Setup

To run commands for Jaseci we need a terminal that accepts bash arguments. We recommend using the Ubuntu terminal that comes as the default with WSL.

Step 1:

Check if WSL is installed by running the following the Windows powershell terminal :

wsl -l -v

This will return the flavour of the distribution used for WSL. The version column will show the version of WSL.

Step 2:

If no version is specified open windows powershell in administrator mode and install WSL by running :

wsl --install

Step 3:

Restart your Computer

Step 4:

Open the Ubuntu terminal. For more information on installation see here.

Install Jaseci in Debian or WSL2

Step 1:

Install Jaseci Dependencies

Jaseci requires the following dependencies:

• python3.10-dev
• g++
• build-essential
• pkg-config
• cmake

To install in Debian (or WSL2),

apt-get install python3.10-dev python3-pip git g++ build-essential pkg-config cmake

Step 2:

Install pip python package manager

# Upgrade pip to the latest
pip install --upgrade pip

Step 3:

Once all the dependencies are installed. Now lets install Jaseci.

pip install jaseci

Step 4:

To ensure our installation is working run;

jsctl --help

Once it shows a list of options and commands as follows, you're installation is complete.

  The Jaseci Command Line Interface

Options:
  -f, --filename TEXT  Specify filename for session state.
  -m, --mem-only       Set true to not save file for session.
  --help               Show this message and exit.

Commands:
  actions     Group of `actions` commands
  alias       Group of `alias` commands
  apply       Group of `apply` commands
  architype   Group of `architype` commands
  booktool    Internal book generation tools
  clear       Clear terminal
  config      Group of `config` commands
  edit        Edit a file
  .
  .
  .
  .

Install Jaseci on MacOS

Step 1:

For macOS, install the following dependencies using one of macOS package manager such as Homebrew and MacPorts.

• python3.10-dev
• g++
• build-essential
• pkg-config
• cmake

Step 2:

Install Jaseci

pip install jaseci

Step 3: To ensure our installation is working run;

jsctl --help

Once it shows a list of options and commands as follows, you're installation is complete.

  The Jaseci Command Line Interface

Options:
  -f, --filename TEXT  Specify filename for session state.
  -m, --mem-only       Set true to not save file for session.
  --help               Show this message and exit.

Commands:
  actions     Group of `actions` commands
  alias       Group of `alias` commands
  apply       Group of `apply` commands
  architype   Group of `architype` commands
  booktool    Internal book generation tools
  clear       Clear terminal
  config      Group of `config` commands
  edit        Edit a file
  .
  .
  .
  .

Note

For macOS, there is currently a known compatibility issue between tensorflow-text and Apple custom ARM-based silicon (M1, M2, etc.). If you are on a Mac machine with an Apple chip, you can still use jaseci and jaseci-serv and majority of the AI modules come with Jaseci, with the exception of those depending on tensorflow-text, which includes use_enc and use_qa in the jac-nlp package.

Alternatively, you can build tensorflow-text from source following solutions provided by the community.