FastAPI SQLAlchemy Models Setup

Setup

In this lesson, we’ll start by working with a pre-existing application that demonstrates full CRUD (Create, Read, Update, Delete) functionality on a mock data model. This will provide a solid foundation for integrating SQLAlchemy into the project.

If you have a complete, working application from the Python FastAPI MVC CRUD Build Lesson, you may choose to use that codebase as starter code instead of the repo provided below.

1. Clone the starter code

We’ve provided a starter repository with the base application code. Clone the repository to your local machine and rename the folder for this lesson:

git clone https://git.generalassemb.ly/modular-curriculum-all-courses/python-fastapi-mvc-crud-build-solution python-fastapi-sqlalchemy-models
cd python-fastapi-sqlalchemy-models

2. Install dependencies

This project uses pipenv for managing dependencies. To install everything the project needs, run:

pipenv install

Installing SQLAlchemy

SQLAlchemy is a powerful and flexible library for working with databases in Python. While it is not part of FastAPI itself, it is commonly used in FastAPI applications for interacting with relational databases.

1. Install required database packages

For this lesson, we’ll be using PostgreSQL as our database engine. To work with PostgreSQL in Python, we need to install two new packages:

• SQLAlchemy – A popular Object Relational Mapper (ORM) that allows us to interact with databases using Python objects instead of raw SQL queries.
• psycopg2-binary – A PostgreSQL database adapter that enables SQLAlchemy to connect to and interact with PostgreSQL databases.

To install both dependencies in your virtual environment, run:

pipenv install sqlalchemy psycopg2-binary

2. Activate the virtual environment:

 pipenv shell

3. Start the development server:

pipenv run uvicorn main:app --reload

You should now have the app running. Visit http://127.0.0.1:8000 in your browser to confirm it’s working.

Open the application in Visual Studio Code:

code .

4. Integrating SQLAlchemy

In this module we will be:

• Defining models using SQLAlchemy’s ORM (Object Relational Mapper).
• Configuring a PostgreSQL database connection.
• Migrating from mock data to a real database.