pymodeller 0.5.2

A package that reads models from YAML and creates Pydantic models

PyModeller

PyModeller is a DevOps-oriented CLI tool designed to synchronize infrastructure requirements with Python application code. By utilizing a single YAML "Source of Truth," PyModeller automates the generation of Pydantic models, Peewee ORM classes, and .env templates, ensuring your configuration and database schemas never drift from your documentation.

Core Features

• Code Generation: Instant creation of typed Pydantic models or Peewee schemas from YAML.
• Traceable Settings: Automatically generates a BaseTraceableSettings class. All settings inherit from this, enabling data source tracking and native YAML loading capabilities.
• Environment Templates: Auto-generate .env.example files for seamless developer onboarding.
• Validation & Safety: Verify local .env files against specifications to catch errors before runtime.
• Drift Detection: Identify discrepancies between your YAML definitions and existing Python code.

Project Initialization & Core Models

By default, PyModeller looks for a py_modeller.yaml file in the root of your project. This file must contain two main sections: config and sections.

The 'General' Section

For Pydantic model generation, it is required to define a section named General. This section is used to generate the general_setting class, which serves as the primary configuration entry point.

• Centralized Access: This allows you to import the global configuration state from anywhere in your application.
• Project Metadata: It typically holds flags like LOCAL_DEV, N_THREADS, or global API keys.

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Installation

Install the package using uv or pip:

 uv add pymodeller

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Usage

The CLI provides four main commands to manage your development workflow:

1. Generate models

Generate typed Pydantic models or Peewee code for your project.

pymodeller codegen

2. Example Environment Generation

Generate a template .env.example file based on your YAML specification to help collaborators set up their environment.

pymodeller example

3. Environment Check

Validate your current .env file against the YAML specification to ensure all required variables are present and correctly formatted.

pymodeller check

4. Drift Detection

Check for "drift" between your YAML specification and the code already generated. This ensures that your Python models haven't fallen out of date.

pymodeller drift

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CLI Command Reference

Data Loading & Specification

The project uses a structured YAML-to-Object mapping to manage environment variables and database schemas. The py_modeller.yaml acts as the Single Source of Truth, which is parsed into an EnvSpec instance.

Environment Data Model Specification (YAML)

This document defines the schema for the py_modeller.yaml file. This file is used by the loader.py to generate typed Python dataclasses and manage environment variables, settings, and database schemas.

Validation

The loading process includes a mandatory validate_no_duplicates() call which ensures:

1. No two environment variables share the same env_name.
2. No two Python attributes (aliases) collide within the same section.

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Technical Specification

BaseTraceableSettings

Every generated settings class inherits from BaseTraceableSettings. This core class provides:

• Source Tracking: Maintains a record of where each setting value originated (Environment, YAML, or Default).
• YAML Loader: Built-in methods to populate settings directly from structured YAML files.

1. Global Config (config)

Defines the output paths for generated files.

Key	Description
PYDANTIC_OUT	Main file path for Pydantic models.
PYDANTIC_FOLDER	Directory for individual Pydantic model files.
PEEWEE_OUT	Main file path for the Peewee ORM models.
PEEWEE_FOLDER	Directory for individual ORM model files.

2. Root Structure

The YAML must contain a top-level sections key which is a list of environment groups.

sections:
- name: "ExampleSection"
  variables: []

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3. Section Schema (EnvSection)

Key	Type	Description
name	string	Required. The display name of the section.
description	string	Brief explanation of the section's purpose.
env_prefix	string	Prefix added to all variables in this section (e.g., APP_).
type	string	Section category: settings, model, or peewee (Default: model).
include_general	boolean	Whether to include general configurations (Default: true).
include_literal	boolean	Used for FastAPI/literal exports (Default: true).
database	object	Optional. Metadata for Database tables (See DBSpec).
variables	list	A list of variable definitions (See EnvVarSpec).

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4. Variable Specification (EnvVarSpec)

Key	Type	Description
name	string	Required. The variable name (automatically converted to snake_case in Python).
type	string	Data type mapping (See Supported Types).
description	string	Documentation for the variable.
default	any	Default value if the environment variable is not set.
required	boolean	If true, the loader validates its existence.
secret	boolean	If true, masks the value in logs (Type secret is a shortcut).
alias	string	Custom Python attribute name (Defaults to camelCase of name).
db_spec	object	Optional. ORM field configuration (See DBField).

Supported Types

The loader normalizes the following types:

• Primitives: string, integer, number (float), boolean, datetime.
• Special: secret (shortcut for str + secret: true), path, list.
• Numpy Arrays: pnd.NpNDArrayUint8, pnd.NpNDArrayInt8, pnd.NpNDArrayFp32.

Note: We are actively working on expanding this list to support additional data types and specialized structures in future releases.

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5. Database Specification (DBSpec)

Defined at the section level for Peewee/ORM metadata.

Key	Type	Description
table_name	string	Explicit name for the database table.
schema	string	Database schema (e.g., public).
primary_key	list	List of field names that form a composite primary key.
indexes	list	Custom database index definitions.
constraints	list	Table-level constraints.

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6. Database Field Specification (DBField)

Defined under the db_spec key within a variable.

Key	Type	Description
primary_key	boolean	Marks the field as the primary key.
allow_null	boolean	Allows NULL values in DB (Default: false).
unique	boolean	Adds a UNIQUE constraint.
max_length	integer	Max characters for string fields.
foreign_key	string	Reference to another model/table.
choices	list	Enforces a list of allowed string values.
max_digits	integer	Precision for decimal numbers.
decimal_places	integer	Scale for decimal numbers.

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Example Usage

sections:
- name: "Network"
  env_prefix: "NET"
  type: "settings"
  variables:
    - name: "host_address"
      type: "string"
      default: "0.0.0.0"
      alias: "serverHost"

- name: "UsersTable"
  type: "peewee"
  database:
    table_name: "app_users"
  variables:
    - name: "id"
      type: "integer"
      db_spec:
        primary_key: true
    - name: "api_key"
      type: "secret"
      required: true

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Contributing

Contributions are welcome! We value your help in making PyModeller better.

Before you get started, please refer to our Contributing Guide for details on our development workflow, coding standards, and how to submit pull requests.

If you encounter a bug or have a feature request, feel free to open an issue.

License

This project is licensed under the MIT License.