![]() model_validate(): a utility for loading any object into a model.model_rebuild(): rebuild the model schema, which also supports building recursive generic models.model_post_init(): perform additional initialization after the model is initialized.model_parametrized_name(): compute the class name for parametrizations of generic classes.model_json_schema(): returns a jsonable dictionary representing the model as JSON Schema.model_fields_set: set of fields which were set when the model instance was initialized.model_extra: get extra fields set during validation.model_dump_json(): returns a JSON string representation of model_dump().model_dump(): returns a dictionary of the model's fields and values.model_copy(): returns a copy (by default, shallow copy) of the model.model_construct(): a class method for creating models without running validation.model_computed_fields: a dictionary of the computed fields of this model instance.Models possess the following methods and attributes: The example above only shows the tip of the iceberg of what models can do. Basic model usage ¶īy default, models are mutable and field values can be changed through attribute assignment. With "parse" reserved specifically for discussions related to JSON parsing. While the terms "parse" and "validation" were previously used interchangeably, moving forward, we aim to exclusively employ "validate", Given the widespread adoption of "validation" as the colloquial termįor this process, we will consistently use it in our documentation. Precisely conforms to the applied type hints. In essence, Pydantic's primary goal is to assure that the resulting structure post-processing (termed "validation") Refer to the Data Conversion and Attribute Copies sections below. For a more in-depth understanding of the implications for your usage, Without mutating the original input data. This can involve copying arguments passed to the constructor in order to perform coercion to a new type In some cases, "validation" goes beyond just model creation, and can include the copying and coercion of data. While this distinction may initially seem subtle, it holds practical significance. When data cannot be successfully parsed into a model instance. This distinction becomes apparent when considering that Pydantic's ValidationError is raised Pydantic guarantees the types and constraints of the output, not the input data. In Pydantic, the term "validation" refers to the process of instantiating a model (or other type) that adheres to specified The action of checking or proving the validity or accuracy of something. Primary focus doesn't align precisely with the dictionary definition of "validation": validation ¶ The potential confusion around the term "validation" arises from the fact that, strictly speaking, Pydantic's This task, which Pydantic is well known for, is most widely recognized as "validation" in colloquial terms,Įven though in other contexts the term "validation" may be more restrictive. We use the term "validation" to refer to the process of instantiating a model (or other type) that adheres to specified types andĬonstraints. Validation - a deliberate misnomer TL DR ¶
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