pydantic-core
This package provides the core functionality for pydantic validation and serialization.
Pydantic-core is currently around 17x faster than pydantic V1.
See tests/benchmarks/ for details.
Example of direct usage
NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core.
from pydantic_core import SchemaValidator, ValidationError
v = SchemaValidator(
{
'type': 'typed-dict',
'fields': {
'name': {
'type': 'typed-dict-field',
'schema': {
'type': 'str',
},
},
'age': {
'type': 'typed-dict-field',
'schema': {
'type': 'int',
'ge': 18,
},
},
'is_developer': {
'type': 'typed-dict-field',
'schema': {
'type': 'default',
'schema': {'type': 'bool'},
'default': True,
},
},
},
}
)
r1 = v.validate_python({'name': 'Samuel', 'age': 35})
assert r1 == {'name': 'Samuel', 'age': 35, 'is_developer': True}
# pydantic-core can also validate JSON directly
r2 = v.validate_json('{"name": "Samuel", "age": 35}')
assert r1 == r2
try:
v.validate_python({'name': 'Samuel', 'age': 11})
except ValidationError as e:
print(e)
"""
1 validation error for model
age
Input should be greater than or equal to 18
[type=greater_than_equal, context={ge: 18}, input_value=11, input_type=int]
"""
Getting Started
Prerequisites
You'll need:
- Rust - Rust stable (or nightly for coverage)
- uv - Fast Python package manager
- git - For version control
- make - For running development commands (or use
nmakeon Windows)
Quick Start
# Clone the repository (or from your fork)
git clone git@github.com:pydantic/pydantic-core.git
cd pydantic-core
# Install all dependencies using uv, setup pre-commit hooks, and build the development version
make install
Verify your installation by running:
make
This runs a full development cycle: formatting, building, linting, and testing
Development Commands
Run make help to see all available commands, or use these common ones:
make build-dev # to build the package during development
make build-prod # to perform an optimised build for benchmarking
make test # to run the tests
make testcov # to run the tests and generate a coverage report
make lint # to run the linter
make format # to format python and rust code
make all # to run to run build-dev + format + lint + test
Useful Resources
python/pydantic_core/_pydantic_core.pyi- Python API typespython/pydantic_core/core_schema.py- Core schema definitionstests/- Comprehensive usage examples
Profiling
It's possible to profile the code using the flamegraph utility from flamegraph-rs. (Tested on Linux.) You can install this with cargo install flamegraph.
Run make build-profiling to install a release build with debugging symbols included (needed for profiling).
Once that is built, you can profile pytest benchmarks with (e.g.):
flamegraph -- pytest tests/benchmarks/test_micro_benchmarks.py -k test_list_of_ints_core_py --benchmark-enable
The flamegraph command will produce an interactive SVG at flamegraph.svg.
Releasing
TBC (needs to be integrated into pydantic repository release process).
2 maintainers
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