🐍🔗 SQLAlchemy-Pydantic Codegen

A Python library for generating Pydantic models from SQLAlchemy models, providing a seamless integration between SQLAlchemy and Pydantic for data validation and serialization.

✨ Key Features

• Automatic Pydantic model generation from SQLAlchemy models.
• Relationship support: Nested models for SQLAlchemy relationships.
• Custom JSON/JSONB field mapping to your own Pydantic models.
• Auto-generated __init__.py for schema packages.

📦 Installation

uv add sqlalchemy-pydantic-codegen

🚀 Usage

We recommend sqlacodegen to generate your SQLAlchemy models automatically.

Once your SQLAlchemy models are ready, generate Pydantic models with:

sqlalchemy-pydantic-codegen --models-path my_app.db.models --output-dir src/schemas

• --models-path: Dotted path to your SQLAlchemy models (required)
• --output-dir: Output directory for generated schemas (default: src/schemas)

🛠️ Custom Configuration

To map JSON/JSONB fields to custom Pydantic models, use the --config option.

Create a config file (e.g., codegen_config.py):

# codegen_config.py

# Maps table names to a dictionary of field names and the Pydantic model to use.
CUSTOM_JSONB_MODELS = {
    "my_table": {
        "my_jsonb_field": "MyCustomPydanticModelForJsonbField",
    },
}

# Maps the Pydantic model name to its full import statement.
CUSTOM_IMPORTS = {
    "MyCustomPydanticModelForJsonbField": "from my_app.schemas import MyCustomPydanticModelForJsonbField",
}

Then, run the command with the --config flag:

sqlalchemy-pydantic-codegen --models-path my_app.db.models --output-dir src/schemas --config codegen_config.py

📤 Output

• One Pydantic schema file per SQLAlchemy model.
• init.py with all exports and forward references.
• Cleaned and ready-to-use Pydantic models.