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This package provides the core functionality forpydantic validation and serialization.

Pydantic-core is currently around 17x faster than pydantic V1.Seetests/benchmarks/ for details.

NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core.

frompydantic_coreimportSchemaValidator,ValidationErrorv=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})assertr1== {'name':'Samuel','age':35,'is_developer':True}# pydantic-core can also validate JSON directlyr2=v.validate_json('{"name": "Samuel", "age": 35}')assertr1==r2try:v.validate_python({'name':'Samuel','age':11})exceptValidationErrorase: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]    """

You'll need rust stableinstalled, or rust nightly if you want to generate accurate coverage.

With rust and python 3.9+ installed, compiling pydantic-core should be possible with roughly the following:

# clone this repo or your forkgit clone git@github.com:pydantic/pydantic-core.gitcd pydantic-core# create a new virtual envpython3 -m venv envsource env/bin/activate# install dependencies and install pydantic-coremake install

That should be it, the example shown above should now run.

You might find it useful to look atpython/pydantic_core/_pydantic_core.pyi andpython/pydantic_core/core_schema.py for more information on the python API,beyond that,tests/ provide a large number of examples of usage.

If you want to contribute to pydantic-core, you'll want to use some other make commands:

• 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 to runformat build-dev lint test

It's possible to profile the code using theflamegraph utility fromflamegraph-rs. (Tested on Linux.) You can install this withcargo install flamegraph.

Runmake 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

Theflamegraph command will produce an interactive SVG atflamegraph.svg.

1. Bump package version locally. Do not just editCargo.toml on Github, you need bothCargo.toml andCargo.lock to be updated.
2. Make a PR for the version bump and merge it.
3. Go tohttps://github.com/pydantic/pydantic-core/releases and click "Draft a new release"
4. In the "Choose a tag" dropdown enter the new tagv<the.new.version> and select "Create new tag on publish" when the option appears.
5. Enter the release title in the form "v<the.new.version> "
6. Click Generate release notes button
7. Click Publish release
8. Go tohttps://github.com/pydantic/pydantic-core/actions and ensure that all build for release are done successfully.
9. Go tohttps://pypi.org/project/pydantic-core/ and ensure that the latest release is published.
10. Done 🎉