Are you doing some ETL project, you don't want to manage your database schema with raw SQL, and maybe validate data from external tools using those schemas e.g. when using LLM output? Then this tool might help you.

shed is a cli tool that acts like an application for your database schemas management using SQLModel orm and manages them for you using alembic. You get free json-schema export for all your pydantic.BaselModel (v2).

• You can create your database models git repo that only manages db models, using shed to manage db and schemas
• You can add shed as tool into an existing python project and add migration files to it, following the folder structure proposed below.

Using uv:

uv tool install https://github.com/gh-PonyM/shed.git

Using pipx:

pipx install git+https://github.com/gh-PonyM/shed.git#main

Create a new project inside the projects folder:

shed init news_agg -o projects -c postgres://user:pw@localhost:5432/db_name --env lab

This will create the following folder structure:

projects
└── news_agg
    ├── migrations
    │   └── versions
    └── models.py

This will create a config file for local and prod databases using sqlite for local and postgres for prod.

development:
  db:
    news_agg:
      connection:
        db_path: news_agg.sqlite
      type: sqlite
projects:
  news_agg:
    db:
      lab:
        connection:
          database: db_name
          host: localhost
          password: pw
          port: 5432
          username: user
        type: postgres
    module: ./projects/news_agg/models.py

Then just define your SQLModel files as in models.py.

• Clone databases from prod to dev
• Running short-commands for alembic like revision or migrate
• Alembic command wrapper that takes your project and passes the db information to alembic
• Exporting jsonschemas for your models (see schemas)

Alembic uses different config files and folders. The first entrypoint is the alembic.ini file and the top of the config template we use is this:

[alembic]
script_location = {script_dir}
prepend_sys_path = .
version_path_separator = os
sqlalchemy.url = sqlite:///:memory:
version_locations = {versions_dir}

As you can see, the folder containing the version python files can be specified as well as the script location where env.py and script.py.mako is expected to be found. Using a generated alembic.ini, env.py and script.py.mako, we have hassle creating those files for every project we need to manage data as developer / data engineer.