🧠 Bashnet – Semantic Bash Learning CLI

Bashnet is a cross-platform command-line tool that helps you explore and learn Bash commands, options, and scripting structures using a semantic network.

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🚀 Features

• Import modular Bash knowledge from structured JSON files
• Deep semantic search: finds related options, concepts, commands
• Simple search: retrieve a single concept by label
• Export complete semantic network as knowledge_net.json
• Visualize the network as interactive HTML (via pyvis)
• Fully Docker-compatible for platform-independent execution
• CLI interface with live terminal usage

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📊 JSON File Roles

Each file defines a component of the semantic network:

• commands.json: Bash commands (e.g. ls, cd)
• options.json: CLI flags and options (e.g. -l, --help) — must be described independently of any command
• scripting.json: Syntax and structures like if, for, [[ ]]
• concepts.json: Abstract/contextual terms like directory, stdout
• knowledge_net.json: The complete, generated semantic network (nodes + relations)

All files should be placed in the src/data/ folder to be imported correctly.

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📁 Minimal Example (Extendable)

To get started, here's a small valid example:

src/data/commands.json

[
    {
        "id": "cmd_cd",
        "label": "cd",
        "type": "command",
        "description": "Changes the current working directory to a specified path.",
        "category": "file management",
        "tags": [
            "filesystem",
            "navigation",
            "directory",
            "shell"
        ],
        "examples": [
            {
                "command": "cd /home/user",
                "expected_output": "",
                "explanation": "Changes to the absolute path '/home/user'."
            },
            {
                "command": "cd ..",
                "expected_output": "",
                "explanation": "Moves one level up to the parent directory."
            }
        ],
        "options": [],
        "related": [
            "cmd_pwd",
            "con_directory"
        ],
        "links": [
            "https://www.gnu.org/software/bash/manual/html_node/Bourne-Shell-Builtins.html#index-cd"
        ]
    }
]
 

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🐳 Run with Docker (Recommended)

Make sure docker is installed.

Linux / macOS / WSL (Bash-Terminal):

🔧 Step 1: Build the image

docker build -t bashnet .

▶ Step 2: Start the CLI in your Bashnet project folder

docker run --rm -it \
  -v "$PWD:/app" \
  -e RUNNING_IN_DOCKER=1 \
  bashnet

Windows 11 (PowerShell):

🔧 Step 1: Build the image

docker build -t bashnet .

▶ Step 2: Start the CLI in your Bashnet project folder

docker run --rm -it `
  -v "${PWD}:/app" `
  -e RUNNING_IN_DOCKER=1 `
  bashnet

🧾 Flags explained

Flag	Description
--rm	Automatically delete the container after exit
-it	Enables interactive input/output (CLI)
-v "$PWD:/app"	Mounts your working dir (so JSON & HTML stay persistent)
-e RUNNING_IN_DOCKER=1	Disables automatic browser open for HTML inside container

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💡 CLI Commands

Run inside the container or Python shell:

bashnet> import
    → Loads JSON files and builds the semantic net

bashnet> search <term>
    → Deep search including context like options, related concepts

bashnet> search <term> --simple
    → Exact search for a node only

bashnet> visualize
    → Generates semantic_net.html with interactive graph

bashnet> exit
    → Leave the Bashnet CLI

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🧪 Run Without Docker (Local Python)

You can also run Bashnet locally if you prefer:

1. Ensure Python 3.12+ is installed
2. Install dependencies:

pip install -r requirements.txt

3. Run the CLI:

python -m bashnet

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⚙️ Requirements

Minimal:

click
networkx
pyvis

Optionally, use Poetry for development:

poetry install
poetry run python -m bashnet

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📄 License

MIT License – free to use, modify, extend. Copyright (c) 2025