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Lab Solution: Environment Setup

This guide walks you through verifying and troubleshooting your environment setup.

Step 1: Verify Prerequisites​

First, ensure you have the right tools installed.

IDE (Code Editor)​

Make sure you have an IDE installed and open.

  • VS Code: Recommended for local development.
  • Project IDX: Excellent cloud-based alternative.
  • Cloud Shell Editor: Built into Google Cloud.

Check Python Version​

python --version
# OR (on some systems)
python3 --version

Requirement: You must see Python 3.10.x or higher. If you have an older version, please upgrade Python before continuing.

Check pip​

pip --version
# OR
pip3 --version

Requirement: pip must be installed to manage Python packages.

Step 2: Create the Project Structure​

  1. Create Directory:

    mkdir adk-training
    cd adk-training

  2. Create Virtual Environment: Using a virtual environment is mandatory to avoid conflicts.

    # macOS/Linux
    python3 -m venv venv

    # Windows
    python -m venv venv

  3. Activate Virtual Environment:

    # macOS/Linux
    source venv/bin/activate

    # Windows
    .\venv\Scripts\activate

    Verification: Your terminal prompt should now show (venv).

Step 3: Install Packages​

Install the Google ADK and the dotenv library:

pip install google-adk python-dotenv

Step 4: Configure Authentication​

Create a file named .env in the adk-training folder.

Option A: Google AI Studio (API Key)

GOOGLE_API_KEY="AIzaSy..."

Option B: Vertex AI (Service Account/ADC)

GOOGLE_GENAI_USE_VERTEXAI="1"
GOOGLE_CLOUD_PROJECT="your-project-id"
GOOGLE_CLOUD_LOCATION="us-central1"

Note: If using Vertex AI, ensure you have run gcloud auth application-default login.

Step 5: Run Verification​

Create verify_setup.py and run it:

python verify_setup.py

Expected Output: ✅ Google ADK is installed correctly. ✅ Authentication successful: Connected to the LLM service via ADK agent.

Self-Reflection Answers​

  • Why is Python 3.10+ required? The ADK uses modern Python features like advanced type hints and improved asynchronous handling that are only available in version 3.10 and later.
  • Why use a virtual environment? It isolates your project. If you have another project that needs an older version of a library, the virtual environment prevents them from breaking each other.
  • Why use an IDE instead of a simple text editor? IDEs like VS Code provide syntax highlighting, error detection, and debugging tools that are essential when building complex AI agents.