DevOps CLI Tool (myapp)

A context-aware Command Line Interface (CLI) tool designed for streamlined infrastructure and deployment operations. Built with a robust, production-grade architecture featuring unified state management, decoupled environments, and automated testing capabilities.

Architecture & Concept

This tool is designed around a Context-Aware architecture pattern. Instead of every isolated command re-initializing loggers, rebuilding network configurations, or reading environment variables independently, a single, global Context object is initialized at runtime. This bundle manages shared dependencies across all terminal execution flows.

graph TD
%% Define Styles
classDef fileStyle fill:#f9f9f9,stroke:#333,stroke-width:2px;
classDef factoryStyle fill:#e1f5fe,stroke:#0288d1,stroke-width:2px;
classDef contextStyle fill:#e8f5e9,stroke:#388e3c,stroke-width:2px;
classDef commandStyle fill:#fff3e0,stroke:#f57c00,stroke-width:2px;
%% Nodes
Env[".env File <br> (APP_ENV, LOG_LEVEL, API_BASE_URL, TIMEOUT_SECS)"]:::fileStyle
Factory["Context Factory <br><br> <b>Initializes:</b><br>• Settings (Pydantic)<br>• Logger (Standard Logging)<br>• HTTP Client (HTTPX)"]:::factoryStyle
Context["Shared Context <br><br> Passed globally to all commands down the line"]:::contextStyle
StatusCommand["status Command"]:::commandStyle
DeployCommand["deploy Command"]:::commandStyle
%% Relationships
Env --> Factory
Factory --> Context
Context --> StatusCommand
Context --> DeployCommand

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Core Architecture Components

• Configuration (Settings): Managed via pydantic-settings to guarantee strict type-safety, default fallbacks, and painless loading from .env files.
• State Carrier (Context): A lightweight Python dataclass encapsulating configuration settings, a structured logger instance, and an isolated stateful synchronous HTTP client (httpx.Client).
• Command Router (cli.py): Built on top of Typer, translating declarative Python functions directly into beautiful, production-ready Unix-style terminal utilities.

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Features & Capabilities

1. Unified Environment Lifecycle Configuration

The tool decouples execution parameters completely from code execution. Environment overrides can be swapped seamlessly by updating an external runtime state or a .env file structure:

Parameter	Environment Key	Default	Purpose
Environment	APP_ENV	"dev"	Targets runtime level (dev, staging, prod)
Log Level	LOG_LEVEL	"INFO"	Standard streams verbosity filtering
API Base Address	API_BASE_URL	None	The orchestration controller URL to hit
Network Timeout	TIMEOUT_SECS	30	Request timeout ceiling before failure

2. Live Health Checking (status)

Polls target core cloud components to evaluate operational integrity.

• Action: Performs an HTTP GET request to your configured {API_BASE_URL}/health.
• Debugging Flag: Appending --verbose or -v dynamically forces the logging runtime into DEBUG mode on the fly to print granular connection internals.

3. Orchestration & Continuous Deployment (deploy)

Safely pipes deployment instructions down to your infrastructure controller pipeline.

• Action: Ships a structured JSON payload via an HTTP POST to {API_BASE_URL}/deploy.
• Payload Schema: Passes an explicit immutable string variable (tag) identifying the docker image/revision target.

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Technical Stack

• CLI Framework: Typer (FastAPI's sister framework for rapid terminal composition)
• Networking Engine: HTTPX (Next-generation, highly performant HTTP transport engine)
• Validation Engine: Pydantic (BaseSettings) (Strict type enforcement for environment profiles)
• Testing Harness: Pytest & typer.testing.CliRunner

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Quickstart & Local Installation

Prerequisites

• Python 3.10 or greater
• Poetry, Hatch, or standard Pip tools

1. Setup Environment Configuration

Create a .env file in the root execution workspace of the project:

# .env Configuration Example
APP_ENV=staging
LOG_LEVEL=INFO
API_BASE_URL=[https://api.internal.devops.company.com](https://api.internal.devops.company.com)
TIMEOUT_SECS=15

2. Local Editable Installation

Install the project dependencies and bind the console entrypoint:

pip install -e .

Usage Guide

Once installed, use the globally mapped command binary myapp.

Execute System Status Diagnostics

$ myapp status
2026-05-20 00:24:11,542 INFO myapp - Checking status
OK: healthy

Toggle Verbose Debug Output

$ myapp status --verbose
2026-05-20 00:24:15,102 DEBUG myapp - Context created
2026-05-20 00:24:15,103 DEBUG myapp - Checking status
OK: healthy

Trigger a System Deployment

$ myapp deploy v2.4.1-rc1
2026-05-20 00:24:20,311 INFO myapp - Triggering deploy {"tag": "v2.4.1-rc1"}
Deploy started

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Automated Test Harness

The engine separates production side-effects by utilizing a full dependency injection architecture via Pytest fixtures.

Tests run against an isolated DummyClient that mocks remote HTTP payloads, ensuring your local codebase won't accidentally trigger a production release when running unit testing suites.

To run the local testing execution profile, execute:

> pytest tests/