# Pydantic Function Models
[](https://github.com/astral-sh/uv)
[](https://pdm.fming.dev)
[](https://pypi.org/project/pydantic-function-models)
[](https://pypi.org/project/pydantic-function-models)
[](https://pypi.python.org/pypi/pydantic-function-models)
[](https://results.pre-commit.ci/latest/github/lmmx/pydantic-function-models/master)
A library to model Python function signatures using Pydantic, inspired by the now-deprecated `ValidatedFunction` from Pydantic v1. This package helps validate function arguments according to type hints, bridging the gap between Pydantic v1 and v2.
## Installation
```bash
pip install pydantic-function-models
```
## Features
- **Signature modelling**: Wrap any Python function to model its signature (positional, keyword, etc.) using Pydantic. Note that if you are after just **validation** (whether the arguments are valid) then you want [validate call](https://docs.pydantic.dev/latest/api/validate_call/)
- **Painless migration from Pydantic v1**: Provides a `ValidatedFunction` class ported to v2, for those who relied on `ValidatedFunction` in v1.
- **Clear error messages**: Raises Pydantic `ValidationError` with helpful details if input arguments do not match the declared types.
- **Reserved name checks**: Prevents usage of special parameter names (e.g., `v__args`, `v__kwargs`) that could conflict with internal validation logic.
## Usage
Below is a general overview of how to use **pydantic-function-models** in your own code. There is no built-in command-line interface; usage is purely through Python imports.
### Command-Line Interface
This library does **not** provide a CLI. All functionality is accessed by importing and using its classes in Python code. The remaining “Examples” section demonstrates typical usage patterns.
#### Example: Basic Validated Function
```python
from pydantic_function_models import ValidatedFunction
def add(a: int, b: int) -> int:
return a + b
vf = ValidatedFunction(add)
# Build arguments for validation
args_to_validate = (1,)
kwargs_to_validate = {"b": 2}
validated = vf.model.model_validate({
"a": args_to_validate[0],
"b": kwargs_to_validate["b"]
})
result = add(**validated.model_dump(exclude_unset=True))
print(result) # 3
```
Here, `ValidatedFunction` creates an internal Pydantic model that enforces integer values for `a` and `b`. If you passed a string for `a` or `b`, it would raise a `ValidationError`.
### Library Usage
Internally, each `ValidatedFunction` builds a Pydantic model representing the function’s signature. When you call `.model.model_validate(...)` or `.build_values(...)`, it validates the provided arguments (whether positional or keyword-based) against the signature.
---
## Project Structure
Take a look at the internals here:
- `validated_function.py`: Core logic for `ValidatedFunction`, which encapsulates argument validation and Pydantic model generation.
- `parameters.py`: Model definitions for function parameters/signatures, with checks for reserved argument names.
## Contributing
Maintained by [lmmx](https://github.com/lmmx). Contributions are welcome!
1. **Issues & Discussions**: Please open a GitHub issue or discussion for bugs, feature requests, or questions.
2. **Pull Requests**: PRs are welcome!
- Install the dev extra (e.g. with [uv](https://docs.astral.sh/uv/): `uv pip install -e .[dev]`)
- Run tests (e.g. `pytest`) and include updates to docs or examples if relevant.
- If reporting a bug, please include the version and error message/traceback if available.
## License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
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"description": "# Pydantic Function Models\n\n[](https://github.com/astral-sh/uv)\n[](https://pdm.fming.dev)\n[](https://pypi.org/project/pydantic-function-models)\n[](https://pypi.org/project/pydantic-function-models)\n[](https://pypi.python.org/pypi/pydantic-function-models)\n[](https://results.pre-commit.ci/latest/github/lmmx/pydantic-function-models/master)\n\nA library to model Python function signatures using Pydantic, inspired by the now-deprecated `ValidatedFunction` from Pydantic v1. This package helps validate function arguments according to type hints, bridging the gap between Pydantic v1 and v2.\n\n## Installation\n\n```bash\npip install pydantic-function-models\n```\n\n## Features\n\n- **Signature modelling**: Wrap any Python function to model its signature (positional, keyword, etc.) using Pydantic. Note that if you are after just **validation** (whether the arguments are valid) then you want [validate call](https://docs.pydantic.dev/latest/api/validate_call/)\n- **Painless migration from Pydantic v1**: Provides a `ValidatedFunction` class ported to v2, for those who relied on `ValidatedFunction` in v1.\n- **Clear error messages**: Raises Pydantic `ValidationError` with helpful details if input arguments do not match the declared types.\n- **Reserved name checks**: Prevents usage of special parameter names (e.g., `v__args`, `v__kwargs`) that could conflict with internal validation logic.\n\n## Usage\n\nBelow is a general overview of how to use **pydantic-function-models** in your own code. There is no built-in command-line interface; usage is purely through Python imports.\n\n### Command-Line Interface\n\nThis library does **not** provide a CLI. All functionality is accessed by importing and using its classes in Python code. The remaining \u201cExamples\u201d section demonstrates typical usage patterns.\n\n#### Example: Basic Validated Function\n\n```python\nfrom pydantic_function_models import ValidatedFunction\n\ndef add(a: int, b: int) -> int:\n return a + b\n\nvf = ValidatedFunction(add)\n\n# Build arguments for validation\nargs_to_validate = (1,)\nkwargs_to_validate = {\"b\": 2}\n\nvalidated = vf.model.model_validate({\n \"a\": args_to_validate[0],\n \"b\": kwargs_to_validate[\"b\"]\n})\nresult = add(**validated.model_dump(exclude_unset=True))\n\nprint(result) # 3\n```\n\nHere, `ValidatedFunction` creates an internal Pydantic model that enforces integer values for `a` and `b`. If you passed a string for `a` or `b`, it would raise a `ValidationError`.\n\n### Library Usage\n\nInternally, each `ValidatedFunction` builds a Pydantic model representing the function\u2019s signature. When you call `.model.model_validate(...)` or `.build_values(...)`, it validates the provided arguments (whether positional or keyword-based) against the signature.\n\n---\n\n## Project Structure\n\nTake a look at the internals here:\n\n- `validated_function.py`: Core logic for `ValidatedFunction`, which encapsulates argument validation and Pydantic model generation.\n- `parameters.py`: Model definitions for function parameters/signatures, with checks for reserved argument names.\n\n## Contributing\n\nMaintained by [lmmx](https://github.com/lmmx). Contributions are welcome!\n\n1. **Issues & Discussions**: Please open a GitHub issue or discussion for bugs, feature requests, or questions.\n2. **Pull Requests**: PRs are welcome!\n - Install the dev extra (e.g. with [uv](https://docs.astral.sh/uv/): `uv pip install -e .[dev]`)\n - Run tests (e.g. `pytest`) and include updates to docs or examples if relevant.\n - If reporting a bug, please include the version and error message/traceback if available.\n\n## License\n\nThis project is licensed under the [MIT License](https://opensource.org/licenses/MIT).\n",
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