Pydantic model definition in FastAPI - Step-by-Step Execution

Concept Flow - Pydantic model definition

Define class inheriting BaseModel

↓

Add fields with types

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Create instance with data

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Pydantic validates data

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Valid data

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Instance created

You define a class with fields and types, then create an instance. Pydantic checks the data and either creates the object or raises an error.

Execution Sample

FastAPI

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str

user = User(id=1, name="Alice")

Defines a User model with id and name, then creates a valid User instance.

Execution Table

Step	Action	Input Data	Validation Result	Output
1	Define User model	N/A	N/A	User class ready with id:int, name:str
2	Create User instance	{id: 1, name: 'Alice'}	Valid	User(id=1, name='Alice')
3	Create User instance	{id: 'one', name: 'Bob'}	Invalid	ValidationError raised: id must be int

💡 Execution stops when instance is created or validation error is raised.

Variable Tracker

Variable	Start	After Step 2	After Step 3
user	undefined	User(id=1, name='Alice')	ValidationError (no instance)

Key Moments - 2 Insights

Why does Pydantic raise an error when id is a string instead of int?

Because the model defines id as int, Pydantic checks the type at instance creation (see execution_table step 3) and raises a ValidationError if the type does not match.

Can I create a User without providing all fields?

No, unless you provide default values in the model. Otherwise, Pydantic requires all fields defined without defaults to be present (see execution_table step 2).

Visual Quiz - 3 Questions

Look at the execution table, what is the output when creating User with id=1 and name='Alice'?

A User(id=1, name='Alice')

B ValidationError

C None

D User(id='1', name='Alice')

💡 Hint

Check execution_table row 2 Output column

At which step does Pydantic raise a ValidationError?

A Step 1

B Step 2

C Step 3

D No error raised

💡 Hint

Look at execution_table rows and Validation Result column

If you remove the type annotation for 'id', what happens when creating User with id='one'?

A ValidationError still raised

B User instance created with id='one'

C Syntax error

D Runtime error unrelated to validation

💡 Hint

Pydantic validates only annotated fields; see how type affects validation in execution_table

Concept Snapshot

Pydantic model definition:
- Create a class inheriting BaseModel
- Define fields with Python types
- Instantiate with data dict
- Pydantic validates types automatically
- Raises ValidationError if data invalid
- Used in FastAPI for request/response models

Full Transcript

Pydantic model definition means creating a Python class that inherits from BaseModel. You add fields with types like int or str. When you create an instance of this class with data, Pydantic checks if the data matches the types. If it does, the instance is created. If not, Pydantic raises a ValidationError. For example, if you define a User model with id as int and name as str, and you try to create a User with id='one' (a string), Pydantic will raise an error because it expects an int. This helps catch mistakes early and ensures your data is correct. This process is key in FastAPI to handle data validation automatically.