This repository provides an in-depth overview of the pydantic library, version 2.3.0. Pydantic is a crucial data validation and settings management library for Python, enabling the early identification and rectification of errors.

1. Models
2. Nested Models
3. Fields
4. Field Validators
5. Model Validators
6. Computed Fields
7. Integration with Dataclasses
8. Settings for Environment Variables

Models in Pydantic are classes that inherit from BaseModel. They define the data structure using type annotations and offer avenues for data validation and conversion.

Nested Models allow you to represent complex, nested data structures by defining models within models.

Fields are the individual variables defined within a model. They can have additional configurations and validations beyond the basic type annotations.

Field Validators are functions used to apply additional validations or transformations to fields before they are set in the model.

Model Validators allow for validation at the model level, occurring after all the Field Validators have been executed. They can be performed both pre and post-field validation.

Computed Fields are fields whose values are dynamically computed from the values of other fields.

Pydantic offers integrations with Python Dataclasses to simplify type safety and data validation.

Pydantic enables the loading and validation of settings from environment variables, simplifying the configuration of your applications.