# pydantic-core
[](https://github.com/pydantic/pydantic-core/actions?query=event%3Apush+branch%3Amain+workflow%3Aci)
[](https://codecov.io/gh/pydantic/pydantic-core)
[](https://pypi.python.org/pypi/pydantic-core)
[](https://github.com/pydantic/pydantic-core)
[](https://github.com/pydantic/pydantic-core/blob/main/LICENSE)
This package provides the core functionality for [pydantic](https://docs.pydantic.dev) validation and serialization.
Pydantic-core is currently around 17x faster than pydantic V1.
See [`tests/benchmarks/`](./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._
```py
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
You'll need rust stable [installed](https://rustup.rs/), 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:
```bash
# clone this repo or your fork
git clone git@github.com:pydantic/pydantic-core.git
cd pydantic-core
# create a new virtual env
python3 -m venv env
source env/bin/activate
# install dependencies and install pydantic-core
make install
```
That should be it, the example shown above should now run.
You might find it useful to look at [`python/pydantic_core/_pydantic_core.pyi`](./python/pydantic_core/_pydantic_core.pyi) and
[`python/pydantic_core/core_schema.py`](./python/pydantic_core/core_schema.py) for more information on the python API,
beyond that, [`tests/`](./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 run `format build-dev lint test`
## Profiling
It's possible to profile the code using the [`flamegraph` utility from `flamegraph-rs`](https://github.com/flamegraph-rs/flamegraph). (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.):
```bash
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
1. Bump package version locally. Do not just edit `Cargo.toml` on Github, you need both `Cargo.toml` and `Cargo.lock` to be updated.
2. Make a PR for the version bump and merge it.
3. Go to https://github.com/pydantic/pydantic-core/releases and click "Draft a new release"
4. In the "Choose a tag" dropdown enter the new tag `v<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> <YYYY-MM-DD>"
6. Click Generate release notes button
7. Click Publish release
8. Go to https://github.com/pydantic/pydantic-core/actions and ensure that all build for release are done successfully.
9. Go to https://pypi.org/project/pydantic-core/ and ensure that the latest release is published.
10. Done 🎉
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"author_email": "Samuel Colvin <s@muelcolvin.com>, Adrian Garcia Badaracco <1755071+adriangb@users.noreply.github.com>, David Montague <david@pydantic.dev>, David Hewitt <mail@davidhewitt.dev>, Sydney Runkle <sydneymarierunkle@gmail.com>, Victorien Plot <contact@vctrn.dev>",
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"description": "# pydantic-core\n\n[](https://github.com/pydantic/pydantic-core/actions?query=event%3Apush+branch%3Amain+workflow%3Aci)\n[](https://codecov.io/gh/pydantic/pydantic-core)\n[](https://pypi.python.org/pypi/pydantic-core)\n[](https://github.com/pydantic/pydantic-core)\n[](https://github.com/pydantic/pydantic-core/blob/main/LICENSE)\n\nThis package provides the core functionality for [pydantic](https://docs.pydantic.dev) validation and serialization.\n\nPydantic-core is currently around 17x faster than pydantic V1.\nSee [`tests/benchmarks/`](./tests/benchmarks/) for details.\n\n## Example of direct usage\n\n_NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core._\n\n```py\nfrom pydantic_core import SchemaValidator, ValidationError\n\n\nv = SchemaValidator(\n {\n 'type': 'typed-dict',\n 'fields': {\n 'name': {\n 'type': 'typed-dict-field',\n 'schema': {\n 'type': 'str',\n },\n },\n 'age': {\n 'type': 'typed-dict-field',\n 'schema': {\n 'type': 'int',\n 'ge': 18,\n },\n },\n 'is_developer': {\n 'type': 'typed-dict-field',\n 'schema': {\n 'type': 'default',\n 'schema': {'type': 'bool'},\n 'default': True,\n },\n },\n },\n }\n)\n\nr1 = v.validate_python({'name': 'Samuel', 'age': 35})\nassert r1 == {'name': 'Samuel', 'age': 35, 'is_developer': True}\n\n# pydantic-core can also validate JSON directly\nr2 = v.validate_json('{\"name\": \"Samuel\", \"age\": 35}')\nassert r1 == r2\n\ntry:\n v.validate_python({'name': 'Samuel', 'age': 11})\nexcept ValidationError as e:\n print(e)\n \"\"\"\n 1 validation error for model\n age\n Input should be greater than or equal to 18\n [type=greater_than_equal, context={ge: 18}, input_value=11, input_type=int]\n \"\"\"\n```\n\n## Getting Started\n\nYou'll need rust stable [installed](https://rustup.rs/), or rust nightly if you want to generate accurate coverage.\n\nWith rust and python 3.9+ installed, compiling pydantic-core should be possible with roughly the following:\n\n```bash\n# clone this repo or your fork\ngit clone git@github.com:pydantic/pydantic-core.git\ncd pydantic-core\n# create a new virtual env\npython3 -m venv env\nsource env/bin/activate\n# install dependencies and install pydantic-core\nmake install\n```\n\nThat should be it, the example shown above should now run.\n\nYou might find it useful to look at [`python/pydantic_core/_pydantic_core.pyi`](./python/pydantic_core/_pydantic_core.pyi) and\n[`python/pydantic_core/core_schema.py`](./python/pydantic_core/core_schema.py) for more information on the python API,\nbeyond that, [`tests/`](./tests) provide a large number of examples of usage.\n\nIf you want to contribute to pydantic-core, you'll want to use some other make commands:\n* `make build-dev` to build the package during development\n* `make build-prod` to perform an optimised build for benchmarking\n* `make test` to run the tests\n* `make testcov` to run the tests and generate a coverage report\n* `make lint` to run the linter\n* `make format` to format python and rust code\n* `make` to run `format build-dev lint test`\n\n## Profiling\n\nIt's possible to profile the code using the [`flamegraph` utility from `flamegraph-rs`](https://github.com/flamegraph-rs/flamegraph). (Tested on Linux.) You can install this with `cargo install flamegraph`.\n\nRun `make build-profiling` to install a release build with debugging symbols included (needed for profiling).\n\nOnce that is built, you can profile pytest benchmarks with (e.g.):\n\n```bash\nflamegraph -- pytest tests/benchmarks/test_micro_benchmarks.py -k test_list_of_ints_core_py --benchmark-enable\n```\nThe `flamegraph` command will produce an interactive SVG at `flamegraph.svg`.\n\n## Releasing\n\n1. Bump package version locally. Do not just edit `Cargo.toml` on Github, you need both `Cargo.toml` and `Cargo.lock` to be updated.\n2. Make a PR for the version bump and merge it.\n3. Go to https://github.com/pydantic/pydantic-core/releases and click \"Draft a new release\"\n4. In the \"Choose a tag\" dropdown enter the new tag `v<the.new.version>` and select \"Create new tag on publish\" when the option appears.\n5. Enter the release title in the form \"v<the.new.version> <YYYY-MM-DD>\"\n6. Click Generate release notes button\n7. Click Publish release\n8. Go to https://github.com/pydantic/pydantic-core/actions and ensure that all build for release are done successfully.\n9. Go to https://pypi.org/project/pydantic-core/ and ensure that the latest release is published.\n10. Done \ud83c\udf89\n\n",
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