This is a code generation package that converts YML definitions to Pydantic models (either python code or python objects).
Pydantic is a python library for data validation and settings management using python type annotations.
Here's an official example from the docs
Normally you just program the schemas within your program, but there are several use cases when code generation makes a lot of sense:
- You're programming several apps that use the same schema (think an API server and client library for it)
- You're programming in more than one programming language
pip install pydantic-gen
First you need to create a YAML file with your desired class schema. See example.yml file.
from pydantic_gen import SchemaGen
generated = SchemaGen('example.yml')
The code is now generated and stored in generated.code
attribute. There are
two ways to use the code:
1. Save it to a file, and use the file in your program.
generated.to_file('example_output.py')
You can inspect the resulting example_output.py
2. Import the generated classed directly without saving
generated.to_sys(module_name='generated_schemas')
After running .to_sys()
module 'generated_schemas'
will be added to
sys.modules
and become importable like a normal module:
from generated_schemas import GeneratedSchema1
schema = GeneratedSchema1(id=1)
Recommended usage pattern is creating the yaml files needed for your projects and storing them in a separate repository, to achieve maximum consistency across all projects.
schemas
- list of all schemas described
name
- name of the generated class
props
- list of properties of the class using python type
annotation. Fields: name
- field name, type
- field type,
optional
- bool, if True the type will be wrapped in Optional
,
default
- default value for the field.
config
- list of config settings from Model Config
of pydantic.
Project is fully covered by tests.
This project uses the excellent poetry for packaging. Please read about it and let's all start using
pyproject.toml
files as a standard. Read more: