json_string= pathlib.Path('person.json').read_text()

person= Person.model_validate_json(json_string)

print(repr(person))

print(person)

If the data in the file is not valid,`pydantic` will raise a[`ValidationError`][pydantic_core.ValidationError].

data= tomllib.load(f)

person= Person.model_validate(data)

print(repr(person))

print(person)

YAML (YAML Ain't Markup Language) is a human-readable data serialization format that is often used for configuration files.

Consider the following YAML file:

name:John Doe

age:30

email:john@example.com

class Person(BaseModel):

name:str

age:PositiveInt

email:EmailStr

with open('person.yaml') as f:

XML (eXtensible Markup Language) is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable.

Consider the following XML file:

<?xml version="1.0"?>

<person>

<name>John Doe</name>

<age>30</age>

<email>john@example.com</email>

</person>

Here's how we validate that data:

```python {test="skip"}

import xml.etree.ElementTreeasET

from pydanticimport BaseModel, EmailStr, PositiveInt

classPerson(BaseModel):

name:str

age: PositiveInt

email: EmailStr

tree=ET.parse('person.xml').getroot()

data= {child.tag: child.textfor childin tree}

person= Person.model_validate(data)

print(person)

INI files are a simple configuration file format that uses sections and key-value pairs. They are commonly used in Windows applications and older software.

Consider the following INI file:

name = John Doe

age = 30

email = john@example.com

Here's how we validate that data:

```python {test="skip"}

import configparser

from pydanticimport BaseModel, EmailStr, PositiveInt

classPerson(BaseModel):

name:str

age: PositiveInt

email: EmailStr

config= configparser.ConfigParser()

config.read('person.ini')

person= Person.model_validate(config['PERSON'])

print(person)

RabbitMQ is a popular message broker that implements the AMQP protocol.

In order to run this example locally, you'll first need to[install RabbitMQ](https://www.rabbitmq.com/download.html) and start your server.

Here's a simple example of how you can use Pydantic to:

1. Serialize data to push to the queue

2. Deserialize and validate data when it's popped from the queue

First, let's create a sender script.

```python {test="skip"}

import pika

from pydanticimport BaseModel, EmailStr

classUser(BaseModel):

id:int

name:str

email: EmailStr

connection= pika.BlockingConnection(pika.ConnectionParameters('localhost'))

channel= connection.channel()

channel.queue_declare(queue=QUEUE_NAME)

defpush_to_queue(user_data: User) ->None:

serialized_data= user_data.model_dump_json()

channel.basic_publish(

exchange='',

routing_key=QUEUE_NAME,

body=serialized_data,

)

print(f'Added to queue:{serialized_data}')

user1= User(id=1,name='John Doe',email='john@example.com')

user2= User(id=2,name='Jane Doe',email='jane@example.com')

push_to_queue(user1)

push_to_queue(user2)

connection.close()

And here's the receiver script.

```python {test="skip"}

import pika

from pydanticimport BaseModel, EmailStr

classUser(BaseModel):

id:int

name:str

email: EmailStr

defmain():

connection= pika.BlockingConnection(pika.ConnectionParameters('localhost'))

channel= connection.channel()

channel.queue_declare(queue=QUEUE_NAME)

defprocess_message(

ch: pika.channel.Channel,

method: pika.spec.Basic.Deliver,

properties: pika.spec.BasicProperties,

body:bytes,

):

user= User.model_validate_json(body)

print(f'Validated user:{repr(user)}')

ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_consume(queue=QUEUE_NAME,on_message_callback=process_message)

channel.start_consuming()

if__name__=='__main__':

try:

main()

exceptKeyboardInterrupt:

To test this example:

1. Run the receiver script in one terminal to start the consumer.

2. Run the sender script in another terminal to send messages.

ARQ is a fast Redis-based job queue for Python.

It's built on top of Redis and provides a simple way to handle background tasks.

In order to run this example locally, you’ll need to[Install Redis](https://redis.io/docs/latest/operate/oss_and_stack/install/install-redis/) and start your server.

Here's a simple example of how you can use Pydantic with ARQ to:

1. Define a model for your job data

2. Serialize data when enqueueing jobs

3. Validate and deserialize data when processing jobs

```python {test="skip"}

import asyncio

from typingimport Any

from arqimport create_pool

from arq.connectionsimport RedisSettings

from pydanticimport BaseModel, EmailStr

classUser(BaseModel):

id:int

name:str

email: EmailStr

REDIS_SETTINGS= RedisSettings()

asyncdefprocess_user(ctx: dict[str, Any],user_data: dict[str, Any]) ->None:

user= User.model_validate(user_data)

print(f'Processing user:{repr(user)}')

asyncdefenqueue_jobs(redis):

user1= User(id=1,name='John Doe',email='john@example.com')

user2= User(id=2,name='Jane Doe',email='jane@example.com')

await redis.enqueue_job('process_user', user1.model_dump())

print(f'Enqueued user:{repr(user1)}')

await redis.enqueue_job('process_user', user2.model_dump())

print(f'Enqueued user:{repr(user2)}')

classWorkerSettings:

functions= [process_user]

redis_settings=REDIS_SETTINGS

asyncdefmain():

redis=await create_pool(REDIS_SETTINGS)

await enqueue_jobs(redis)

if__name__=='__main__':

asyncio.run(main())

This script is complete.

It should run "as is" both to enqueue jobs and to process them.