pydantic_core

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pydantic_core public API members

The pydantic_core module exports the following public API members: SchemaValidator, SchemaSerializer, ValidationError, ErrorDetails, InitErrorDetails, SchemaError, PydanticCustomError, PydanticKnownError, PydanticOmit, PydanticUseDefault, PydanticSerializationError, PydanticSerializationUnexpectedValue, Url, MultiHostUrl, MultiHostHost, ArgsKwargs, Some, TzInfo, to_json, from_json, to_jsonable_python, list_all_errors, ErrorTypeInfo, and __version__.

pydantic_core.core_schema module

The pydantic_core.core_schema module contains the core schema definitions used by Pydantic for validation and serialization. This module is part of pydantic_core, which is the underlying validation engine for Pydantic.

pydantic-core provides core validation and serialization

pydantic-core is a package that provides the core functionality for pydantic validation and serialization. Users should not use pydantic-core directly; instead they should use pydantic, which in turn uses pydantic-core.

pydantic-core is 17x faster than pydantic V1

pydantic-core is currently around 17x faster than pydantic V1.

SchemaValidator direct usage example

The following example demonstrates direct usage of pydantic-core's SchemaValidator class: 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)

SchemaValidator methods validate_python and validate_json

SchemaValidator has two validation methods: validate_python() for validating Python objects and validate_json() for validating JSON directly.

pydantic_core.to_json function

The pydantic_core.to_json function is available for JSON serialization.

pydantic_core.from_json supports partial JSON parsing

The pydantic_core.from_json function accepts an allow_partial parameter. When allow_partial=True, it can deserialize incomplete JSON data, returning the portion that was successfully parsed. When allow_partial=False (the default), parsing errors occur on incomplete JSON.

Partial JSON parsing with from_json example

from_json can parse incomplete JSON when allow_partial=True. Example: from_json('["aa", "bb", "c', allow_partial=True) returns ['aa', 'bb']. This works with both arrays and dictionaries.

String caching details in JSON parser

String caching uses a fully associative cache with size 16,384. Only strings where len(string) < 64 are cached. String caching improves performance but increases memory usage slightly. There is overhead to cache lookup, so it may be worth disabling with cache_strings=False if very few repeated strings are in the data.

Pydantic v2.5.0+ uses jiter JSON parser

Starting in v2.5.0, Pydantic uses jiter, a fast and iterable JSON parser, instead of serde. The jiter parser is almost entirely compatible with serde and provides modest performance improvements. It supports deserialization of inf and NaN values.

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