hands-on-ai 0.1.13

Hands-on AI Toolkit for classrooms

HandsOnAI: Your AI Learning Lab

AI learning made simple for students and educators

HandsOnAI is a unified educational toolkit designed to teach students how modern AI systems work — by building and interacting with them directly.

It provides a clean, modular structure that introduces core AI concepts progressively through three tools:

🧱 Module Overview

Module	Purpose	CLI Name
chat	Simple chatbot with system prompts	chat
rag	Retrieval-Augmented Generation (RAG)	rag
agent	ReAct-style reasoning with tool use	agent
models	Model capability detection and utilities	models

Each module is:

• 🔌 Self-contained
• 🧩 Installable via one package: pip install hands-on-ai
• 🧠 Designed for progressive learning

🗂 Project Structure

hands_on_ai/
├── chat/           ← A simple prompt/response chatbot
├── rag/            ← Ask questions using your own documents
├── agent/          ← Agent reasoning + tools (ReAct-style)
├── config.py       ← Shared config (model, chunk size, paths)
├── cli.py          ← Meta CLI (list, config, version)
├── models.py       ← Centralized model utilities
├── utils/          ← Shared tools, prompts, paths, etc.
└── commands/       ← Shared CLI commands

Examples and scripts are available in the repository:

hands-on-ai/
├── examples/       ← Example scripts for all modules
└── scripts/        ← Utility scripts for package maintenance

🧑‍🏫 Why This Matters for Students

Each tool teaches a different level of modern AI interaction:

• chat – Prompt engineering, roles, and LLMs
• rag – Document search, embeddings, and grounded answers
• agent – Multi-step reasoning, tool use, and planning

🚀 Getting Started

Installation

# Install from PyPI
pip install hands-on-ai

# Or directly from GitHub
pip install git+https://github.com/teaching-repositories/hands-on-ai.git

Prerequisites

• Python 3.6 or higher
• For local LLM usage: Ollama or similar local LLM server

Quick Start

Run a local Ollama server, then import and start chatting:

from hands_on_ai.chat import pirate_bot
print(pirate_bot("What is photosynthesis?"))

For more options:

from hands_on_ai.chat import get_response, friendly_bot, pirate_bot

# Basic usage with default model
response = get_response("Tell me about planets")
print(response)

# Use a personality bot
pirate_response = pirate_bot("Tell me about sailing ships")
print(pirate_response)

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines on how to get involved.

License

This project is licensed under the MIT License - see the LICENSE file for details.

LLM Ready

This package is LLM-ready with a comprehensive guide for Large Language Models to understand its functionality. See the LLM Guide for detailed API references, usage examples, and best practices.

For best results when working with an LLM:

1. Download the LLM guide file
2. Upload it to your LLM interface/chat at the beginning of your conversation
3. The LLM will now have detailed knowledge about the package's structure and capabilities

Acknowledgments

• Built with education in mind
• Powered by open-source LLM technology
• Inspired by educators who want to bring AI into the classroom responsibly