Lexy BE

Welcome to the Interview Backend Challenge! This project is built with FastAPI for serving the API and Pydantic for robust data validation, following a clean architecture approach to ensure maintainability and scalability.

🎯 Objectives

The objectives are described in the PDF you received.

⚙️ Requirements

• 🐍 Python 3.13 or higher
• ⚡ UV (Python package manager and virtual environment tool)
• 🐳 Docker (for containerized development)

🚀 Getting Started

1️⃣ Install Python

Make sure you have Python 3.13 or higher installed. You can download it from the official Python website.

2️⃣ Install UV

UV is a fast Python package manager that also allows you to create and manage virtual environments.

For installation details, see the UV documentation.

3️⃣ Create a Virtual Environment

UV automatically creates a virtual environment for the project by installing the required dependencies. At this point it is necessary to have Python version 3.13 or higher installed. To create a virtual environment and install the dependencies, run the following command in the project directory:

uv sync --extra dev

4️⃣ Run the Project

To run the project, use the following command:

docker-compose build
docker-compose up

5️⃣ Access the Application

Once the application is running, you can access it at http://localhost:3001/docs.

6️⃣ Run Tests

To run the tests, use the following command:

uv run pytest ./tests

🏗️ Project Architecture Overview

The codebase is organized into three main layers:

1. api/ – Presentation Layer 🌐

• Exposes the HTTP API using FastAPI.
• Contains route definitions for users and news (routes/).
• Handles authentication and security logic (auth/).
• Manages dependency injection and wiring via container.py, which connects protocol interfaces to their concrete implementations for loose coupling and easy testing.

2. core/ – Domain Layer 🧠

• Defines the business logic and domain entities.
• Organized by feature (e.g., news/, users/), each with:
  • entities/: Pydantic models and DTOs for data validation and transfer.
  • protocols/: Abstract repository interfaces (Python Protocols) that describe required data operations, independent of the database or framework.
  • services/: Business logic and use cases, operating only on protocols and entities, never on infrastructure details.

3. infraestructure/ – Infrastructure Layer 🗄️

• Implements the actual data access logic.
• Contains database models (Beanie ODM for MongoDB) and repository implementations that fulfill the contracts defined in core/protocols/.
• Includes database connection setup and seeding utilities.

🔗 Dependency Injection

• The api/container.py file is responsible for dependency injection.

• It binds protocol interfaces from core/protocols/ to their concrete implementations in infraestructure/database/repositories/.

• This design allows you to swap implementations (e.g., for testing) without changing business logic.

  📝 Notes

• Ensure you have Docker installed and running to use docker-compose commands.

• The project is designed to be run in a virtual environment for better dependency management.

• The uv command is used to manage the virtual environment and dependencies.

• If you want to add a new dependency with uv you can use the command:

uv add <package_name>

💻 Recommended IDEs

• Visual Studio Code
  • If you are using Visual Studio Code, you can install the Python extension for better support.
  • And the black formatter for code formatting.