Praxshell

Praxshell is a lightweight Python-powered CLI for learning machine learning concepts.
It provides explanations, searchable notes, study roadmaps, and interactive notebooks in a shell-like interface.

---

What is Praxshell?

Praxshell is designed as a learning shell.
It works by combining:

• A vault of notes, study roadmaps, and linked notebooks.
• A set of commands for explanations, search, and roadmap navigation.
• A simple compiler that renders Markdown notes into styled HTML with code and plots.

The shell is dependency-free and runs with the Python standard library.
Rendering notebooks requires only two assets:

• A vault (knowledge base), available here: PraxsVault
• A local copy of plotly.min.js for plots

Both of these are handled automatically by Praxshell on first run.
No additional dependencies need to be installed.

---

Installation

Praxshell is published as a Python package. Install it directly:

pip install praxshell

After installation, launch it with:

praxshell

Or clone the repository:

git clone https://github.com/yourname/praxshell.git
cd praxshell

Run the shell:

python -m praxshell.cli.cli 

---

Commands

General

• help – List commands or get help on a specific one
• history – Show past commands, or clear them
• exit / quit – Leave Praxshell
• version – Show the current version

Study

• explain <concept> – Explain an AI concept (from vault notes)
• notebook <concept> – Open a linked notebook in the browser
• search <keywords> – Search notes by keyword or sentence
• roadmap [list|show|next] – View or step through study roadmaps

Vault Management

• update vault – Replace your local vault with the latest
• update vault merge – Merge new content without overwriting existing files

---

Showcase

Here’s what working with Praxshell looks like:

$ praxshell
    ____                       __         ____
   / __ \_________ __  _______/ /_  ___  / / /
  / /_/ / ___/ __ `/ |/_/ ___/ __ \/ _ \/ / /
 / ____/ /  / /_/ />  <(__  ) / / /  __/ / /
/_/   /_/   \__,_/_/|_/____/_/ /_/\___/_/_/

+=== Welcome to Praxshell v0.1.0 ===+
Type 'help' to see available commands.

prax > help Transformer
[*] Transformer (concept)
    Category: models
    Description: Transformers are neural architectures based on self-attention, widely used in NLP and beyond.

prax > explain adam 
[*] Explanation for Adam
------------------------
Category: optimizer
Description: Adam (Adaptive Moment Estimation) combines momentum and RMSProp, adjusting learning rates adaptively for each parameter.

Related Concepts
----------------
  - SGD
  - Momentum
Notebook: vault/notes/optimizer/Adam.md

prax > roadmap list
[*] Available Roadmaps:
- ML_Starter → A path through the most essential machine learning fundamentals.
- Optimization_and_Training → Understand how models are trained efficiently.
- Classification → Learn the core models and losses for classification tasks.

prax > roadmap show ML_Starter
[*] Roadmap: ML Starter
Description: A path through the most essential machine learning fundamentals.


Steps
-----
  - LinearAlgebra → Start with the math foundations for machine learning.
  - Calculus → Understand derivatives and gradients, key for optimization.
  - Probability → Learn probability basics for uncertainty in ML.
  - GradientDescent → Study how parameters are optimized using gradients.
  - LinearRegression → Train your first regression model.
  - MSE → Understand Mean Squared Error for regression evaluation.

prax > roadmap next 
[*] Next Step in ML_Starter:
- LinearAlgebra → Start with the math foundations for machine learning.

[*] Explanation for LinearAlgebra
---------------------------------
Category: theory
Description: Linear Algebra provides the building blocks of ML, with vectors, matrices, and operations like dot products and matrix multiplication.

Related Concepts
----------------
  - Calculus
  - GradientDescent
Notebook: vault/notes/theory/LinearAlgebra.md

prax > notebook LinearAlgebra 
[*] Opening notebook for LinearAlgebra...

prax > history all
[*] Command History:
1. help Transformer
2. explain Adam
3. roadmap list
4. roadmap show ML_Starter
5. roadmap next 
6. notebook LinearAlgebra

prax > help explain
[*] explain - Explain an AI concept. Usage: explain <concept>

prax > help Transformer
[*] Transformer (concept)
    Category: models
    Description: Transformers are neural architectures based on self-attention, widely used in NLP and beyond.

prax > search why does gradient descent sometimes get stuck in local minima
[*] Search results for 'why does gradient descent sometimes get stuck in local minima':
- GradientDescent → Gradient Descent is a fundamental optimization algorithm that updates parameters by moving in the direction of the negative gradient of the loss.
- SGD → Stochastic Gradient Descent (SGD) updates parameters using small random batches of data, making it scalable for large datasets.
- Calculus → Calculus underlies optimization with derivatives, gradients, and the chain rule for backpropagation.
- MSE → Mean Squared Error is a loss function used in regression tasks, measuring the squared difference between predictions and targets.
- Momentum → Momentum is an extension of SGD that accumulates past gradients to speed up convergence and smooth oscillations.

...and many more commands available.

---

Requirements

Praxshell is designed to be dependency-free. It only requires:

• Python 3.9+
• PraxVault – contains all study notes, roadmaps, and examples
  • Repository: https://github.com/diputs-sudo/praxvault
  • Praxshell will download and set this up automatically on first run
• plotly.min.js – used for rendering interactive charts in notebooks
  • This is also automatically downloaded into the html/ folder

No additional Python dependencies are required.

---

Contributing

Contributions are welcome! Please open an issue or pull request on GitHub.

---

License

Praxshell is licensed under the Apache License 2.0.

SPDX-License-Identifier: Apache-2.0