{ "cells": [ { "cell_type": "markdown", "metadata": { }, "source": [ "# Module 0 Self-Driven Lab\n", "\n", "Use this notebook as your workspace for **Dev Environment Setup**. The answer-key notebook sits next to this file. Work through each checkpoint before comparing against the completed version.\n" ] }, { "cell_type": "markdown", "metadata": { }, "source": "# Module 0 — Dev Environment Setup\n\nBefore writing any code, you need three things working:\n\n1. **VS Code** as your editor\n2. **Jupyter notebooks** for interactive development\n3. The **Anthropic Python SDK** connected to a valid API key\n\nThis module gets all three running and ends with a verified \"Hello Claude\" test." }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 1. What You\u0027ll Need\n", "\n", "- **Python 3.9 or later** — check with `python --version` or `python3 --version`\n", "- **VS Code** — download from [code.visualstudio.com](https://code.visualstudio.com)\n", "- **An Anthropic API key** — from [console.anthropic.com](https://console.anthropic.com)\n", "- **~10 minutes** and a terminal\n", "\n", "Run the cell below to confirm your Python version is 3.9+:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { }, "outputs": [ ], "source": [ "# Setup cell. Run this before the exercise cells below.\n", "import sys\n", "\n", "print(f\"Python version: {sys.version}\")\n", "print(f\"Executable: {sys.executable}\")\n", "\n", "assert sys.version_info \u003e= (3, 9), \"Python 3.9 or later is required\"\n", "print(\"\\nPython version check passed.\")" ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 2. Install VS Code Extensions\n", "\n", "Open VS Code and install these two extensions from the Extensions panel (`Ctrl+Shift+X` / `Cmd+Shift+X`):\n", "\n", "| Extension | Publisher | Why |\n", "|---|---|---|\n", "| **Python** | `ms-python` | Python language support, linting, virtual environment detection |\n", "| **Jupyter** | `ms-toolsai` | Run `.ipynb` notebooks directly inside VS Code without launching a browser |" ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 3. Create Your Project Folder\n", "\n", "Pick a home for your lab files. All 10 modules build on the same notebook, so one folder is all you need.\n", "\n", "```bash\n", "mkdir claude-architect-lab\n", "cd claude-architect-lab\n", "```" ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 4. Set Up a Virtual Environment\n", "\n", "A virtual environment keeps the SDK and its dependencies isolated from the rest of your system — important when you\u0027re working with multiple Python projects.\n", "\n", "```bash\n", "# Create the environment\n", "python -m venv .venv\n", "\n", "# Activate it\n", "# macOS / Linux:\n", "source .venv/bin/activate\n", "\n", "# Windows (PowerShell):\n", ".venv\\Scripts\\Activate.ps1\n", "\n", "# Windows (Command Prompt):\n", ".venv\\Scripts\\activate.bat\n", "```\n", "\n", "Your terminal prompt should now show `(.venv)` to confirm it\u0027s active." ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 5. Install Dependencies\n", "\n", "```bash\n", "pip install anthropic python-dotenv ipykernel\n", "```\n", "\n", "- **`anthropic`** — official Python SDK for the Claude API\n", "- **`python-dotenv`** — loads your `.env` file so the API key is never hardcoded\n", "- **`ipykernel`** — registers your virtual environment as a Jupyter kernel so VS Code can find it\n", "\n", "Verify the install by running the cell below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { }, "outputs": [ ], "source": [ "# Setup cell. Run this before the exercise cells below.\n", "import importlib.metadata\n", "\n", "for pkg in (\"anthropic\", \"python-dotenv\", \"ipykernel\"):\n", " try:\n", " version = importlib.metadata.version(pkg)\n", " print(f\"{pkg:\u003c15} {version}\")\n", " except importlib.metadata.PackageNotFoundError:\n", " print(f\"{pkg:\u003c15} NOT INSTALLED — run: pip install {pkg}\")" ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 6. Get Your API Key\n", "\n", "1. Go to [console.anthropic.com](https://console.anthropic.com) and sign in (or create a free account).\n", "2. Navigate to **API Keys** in the left sidebar.\n", "3. Click **Create Key**, give it a name like `architect-lab`, and copy the key — it starts with `sk-ant-`.\n", "\n", "\u003e **You only see the full key once**, so copy it immediately." ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 7. Store Your Key Safely\n", "\n", "Create two files in your project folder:\n", "\n", "**`.env`**\n", "```\n", "ANTHROPIC_API_KEY=sk-ant-api03-YOUR-KEY-HERE\n", "```\n", "\n", "**`.gitignore`**\n", "```\n", ".env\n", ".venv/\n", "__pycache__/\n", "*.pyc\n", ".ipynb_checkpoints/\n", "```\n", "\n", "\u003e **Never commit your API key.** The `.gitignore` above ensures `.env` stays local. If you accidentally push a key, **rotate it immediately** in the Anthropic console — old keys can be used by anyone who finds them in your git history." ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 8. Create Your Lab Notebook\n", "\n", "1. Open your project folder in VS Code: **File → Open Folder**\n", "2. Create a new file named `lab.ipynb` (**File → New File**, then save with the `.ipynb` extension).\n", "3. VS Code will open it as a notebook. In the top-right corner, click **Select Kernel**.\n", "4. Choose **Python Environments** → select the `.venv` you just created. If it doesn\u0027t appear, run **Python: Select Interpreter** from the command palette first." ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## 9. Hello Claude — Your First Cell\n", "\n", "Run the cell below with `Shift+Enter`. If everything is configured correctly, you should see a one-sentence response from Claude. That\u0027s your environment confirmed." ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## Checkpoint 1\n", "\n", "Implement this step yourself. Use the preceding explanation and the module page as your guide, then compare your approach with the answer key after you have a working version.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { }, "outputs": [ ], "source": [ "# TODO: Implement checkpoint 1 for Module 0.\n", "# Keep the cell focused, run it, inspect the output, then move to the next checkpoint.\n", "\n", "raise NotImplementedError(\u0027Complete checkpoint 1\u0027)\n" ] }, { "cell_type": "markdown", "metadata": { }, "source": "## 10. Understanding the Response Object\n\nBefore moving to Module 1, it\u0027s worth knowing what the API returned.\n\n- **`stop_reason`** — `\"end_turn\"` means Claude finished naturally. The exam tests this heavily.\n- **`usage`** — input and output token counts used to calculate cost.\n- **`model`** — confirms which model version actually ran your request." }, { "cell_type": "markdown", "metadata": { }, "source": [ "## Checkpoint 2\n", "\n", "Implement this step yourself. Use the preceding explanation and the module page as your guide, then compare your approach with the answer key after you have a working version.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { }, "outputs": [ ], "source": [ "# TODO: Implement checkpoint 2 for Module 0.\n", "# Keep the cell focused, run it, inspect the output, then move to the next checkpoint.\n", "\n", "raise NotImplementedError(\u0027Complete checkpoint 2\u0027)\n" ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "### Other stop_reason values to know\n", "\n", "| Value | Meaning |\n", "|---|---|\n", "| `end_turn` | Claude finished its response naturally |\n", "| `max_tokens` | Hit the `max_tokens` cap — response was truncated |\n", "| `stop_sequence` | A custom stop sequence you supplied was matched |\n", "| `tool_use` | Claude wants to call a tool (covered in later modules) |\n", "| `pause_turn` | Long-running turn paused — resume by sending it back |\n", "| `refusal` | Claude declined for safety reasons |\n", "\n", "\u003e Keep your notebook cells small and purposeful. Each lab module adds new cells to this same `lab.ipynb`. A well-structured notebook with clear headings and focused cells is also good practice for the structured-output modules later in the lab." ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## Troubleshooting\n", "\n", "| Problem | Most likely cause | Fix |\n", "|---|---|---|\n", "| `AuthenticationError` | `.env` not found or key is wrong | Check the file is in the project root, not a subfolder. Confirm no extra spaces around the `=`. |\n", "| Kernel not listed | `.venv` not registered with Jupyter | Run `python -m ipykernel install --user --name .venv` inside your activated environment. |\n", "| `ModuleNotFoundError: anthropic` | Wrong Python environment active | Confirm `(.venv)` is in your terminal prompt, then re-run `pip install anthropic`. |\n", "| VS Code shows \"No kernel\" | Jupyter extension not installed | Install the Jupyter extension from the Extensions panel and reload VS Code. |\n", "\n", "The cell below runs a quick diagnostic if anything above fails:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { }, "outputs": [ ], "source": [ "# Setup cell. Run this before the exercise cells below.\n", "import os\n", "import sys\n", "from pathlib import Path\n", "\n", "from dotenv import load_dotenv\n", "\n", "load_dotenv()\n", "\n", "checks = []\n", "\n", "# Python version\n", "checks.append((\"Python \u003e= 3.9\", sys.version_info \u003e= (3, 9), sys.version.split()[0]))\n", "\n", "# Inside a virtual environment\n", "in_venv = sys.prefix != getattr(sys, \"base_prefix\", sys.prefix)\n", "checks.append((\"Virtual environment active\", in_venv, sys.prefix))\n", "\n", "# .env file present in CWD\n", "env_path = Path.cwd() / \".env\"\n", "checks.append((\".env file found\", env_path.exists(), str(env_path)))\n", "\n", "# API key loaded and well-formed\n", "key = os.getenv(\"ANTHROPIC_API_KEY\", \"\")\n", "key_ok = key.startswith(\"sk-ant-\")\n", "checks.append((\"ANTHROPIC_API_KEY loaded\", key_ok, f\"{key[:10]}...\" if key else \"(missing)\"))\n", "\n", "# anthropic SDK importable\n", "try:\n", " import anthropic\n", " checks.append((\"anthropic SDK importable\", True, anthropic.__version__))\n", "except ImportError as e:\n", " checks.append((\"anthropic SDK importable\", False, str(e)))\n", "\n", "for label, passed, detail in checks:\n", " mark = \"PASS\" if passed else \"FAIL\"\n", " print(f\"[{mark}] {label:\u003c30} {detail}\")" ] }, { "cell_type": "markdown", "metadata": { }, "source": [ "## Reflection\n", "\n", "Before opening the answer key, write down what worked, what failed, and which API behavior or agent-design rule you would rely on in production.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.9" } }, "nbformat": 4, "nbformat_minor": 5 }