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Development Environment Setup

This guide helps you set up your development environment for the Agentic AI Nano-Degree course modules. Corporate users have access to pre-configured cloud environments, while public users can set up their own local development environment.

Local Development Environment Setup

For public access, you'll need to set up your local development environment. While this requires more initial setup than a cloud environment, it provides full control over your development setup.

Requirements

Before beginning the course modules, ensure you have:

  1. Python 3.11+ installed with pip and virtual environment support
  2. Git for version control and cloning repositories
  3. Code editor (VS Code recommended with Python extension)
  4. LLM API access (OpenAI, Anthropic Claude, or local models like Ollama)

Setup Steps

# Create virtual environment
python -m venv agentic-ai
source agentic-ai/bin/activate  # On Windows: agentic-ai\Scripts\activate

# Clone repository
git clone https://github.com/fwornle/agentic-ai-nano.git
cd agentic-ai-nano

# Install dependencies
pip install -r requirements.txt

API Configuration

Create a .env file in the project root:

# OpenAI (required for most examples)
OPENAI_API_KEY=your_openai_api_key_here

# Optional: Other LLM providers
ANTHROPIC_API_KEY=your_anthropic_key_here

IDE Setup

VS Code (Recommended): 1. Install the Python extension 2. Install the Jupyter extension for notebook support 3. Open the project folder 4. Select your virtual environment as the Python interpreter

Testing Your Setup

# Test your API configuration
from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello! Test message."}]
)
print(response.choices[0].message.content)

Local LLM Options

For development without API costs:

  • Ollama: Run models locally (ollama pull llama2)
  • LM Studio: User-friendly local LLM interface
  • GPT4All: Open-source local models

Course Module Structure

Each module includes:

  • Jupyter notebooks for interactive learning
  • Python scripts for standalone examples
  • Requirements files for module-specific dependencies
  • README files with module-specific setup instructions

Ready to start? With your local environment configured, you can begin working through the course modules and exercises!