Field types and constraints in FastAPI

Introduction

Field types and constraints help you control what kind of data your app accepts. They make sure the data fits rules you set, like length or value limits.

When you want to accept user input but need to check it is the right type, like a number or text.

When you want to limit values, for example, a password must be at least 8 characters.

When you want to make some fields required and others optional.

When you want to add descriptions or examples for your API documentation.

When you want to validate data automatically before using it in your app.

Syntax

FastAPI

from pydantic import BaseModel, Field

class Item(BaseModel):
    name: str = Field(..., min_length=3, max_length=50, description="Name of the item")
    price: float = Field(..., gt=0, description="Price must be greater than zero")
    tags: list[str] = Field(default_factory=list, description="List of tags")

Use Field() to add rules like minimum length or greater than checks.

The ... means the field is required.

Examples

This example requires a username between 4 and 20 characters and an age between 18 and 100.

FastAPI

from pydantic import BaseModel, Field

class User(BaseModel):
    username: str = Field(..., min_length=4, max_length=20)
    age: int = Field(..., ge=18, le=100)

Here, price must be greater than zero, and description is optional but limited to 300 characters.

FastAPI

from pydantic import BaseModel, Field

class Product(BaseModel):
    name: str
    price: float = Field(gt=0)
    description: str | None = Field(default=None, max_length=300)

Title must be at least 5 characters. Published defaults to false if not given.

FastAPI

from pydantic import BaseModel, Field

class BlogPost(BaseModel):
    title: str = Field(..., min_length=5)
    content: str
    published: bool = Field(default=False)

Sample Program

This FastAPI app defines an Item model with rules on name length and price value. The POST endpoint accepts an item and returns its data.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, Field

app = FastAPI()

class Item(BaseModel):
    name: str = Field(..., min_length=3, max_length=50, description="Name of the item")
    price: float = Field(..., gt=0, description="Price must be greater than zero")
    tags: list[str] = Field(default_factory=list, description="List of tags")

@app.post("/items/")
async def create_item(item: Item):
    return {"item_name": item.name, "item_price": item.price, "item_tags": item.tags}

Important Notes

Constraints like min_length, max_length, gt (greater than), and ge (greater or equal) help validate data easily.

Use default_factory=list to set an empty list as default to avoid mutable default arguments issues.

Descriptions in Field() help generate clear API docs automatically.

Summary

Field types and constraints ensure your app gets the right kind of data.

You can require fields, limit their size or value, and provide helpful descriptions.

FastAPI uses Pydantic models with Field() to make validation simple and automatic.

Practice

1. In FastAPI, what is the main purpose of using Field() in a Pydantic model?

easy

A. To define API routes

B. To create database tables automatically

C. To add validation rules and metadata to model fields

D. To handle HTTP requests directly

Solution

1. Step 1: Understand the role of Pydantic models in FastAPI

   Pydantic models define the data structure and validation for request and response data.

2. Step 2: Identify what Field() adds

   Field() allows adding constraints like min/max length, default values, and descriptions to model fields.

3. Final Answer:

   To add validation rules and metadata to model fields -> Option C

4. Quick Check:

   Field() = validation and metadata [OK]

Hint: Field() is for validation and extra info on model fields [OK]

Common Mistakes:

• Thinking Field() creates routes or handles requests
• Confusing Field() with database table creation
• Assuming Field() is for HTTP methods

2. Which of the following is the correct way to require a string field with a minimum length of 3 in a Pydantic model using Field()?

easy

A. name: str = Field(required=True, length=3)

B. name: str = Field(min=3)

C. name: str = Field(length=3)

D. name: str = Field(min_length=3)

Solution

1. Step 1: Recall the correct parameter for minimum length in Field()

   The correct parameter to set minimum string length is min_length.

2. Step 2: Check each option for correct syntax

   name: str = Field(min_length=3) uses min_length=3, which is correct. Others use invalid or incorrect parameters.

3. Final Answer:

   name: str = Field(min_length=3) -> Option D

4. Quick Check:

   Minimum string length uses min_length [OK]

Hint: Use min_length for string length constraints in Field() [OK]

Common Mistakes:

• Using min instead of min_length for strings
• Using length instead of min_length
• Adding required=True which is not a Field() parameter

3. Given this Pydantic model snippet:

from pydantic import BaseModel, Field

class Item(BaseModel):
    price: float = Field(gt=0)
    name: str = Field(min_length=2, max_length=10)

What happens if you create Item(price=-5, name='A')?

medium

A. Validation error because price is not greater than 0 and name is too short

B. Item is created successfully with given values

C. Validation error only for price, name is accepted

D. Validation error only for name, price is accepted

Solution

1. Step 1: Check the constraints on price

   Price must be greater than 0 (gt=0). Given price is -5, which fails this.

2. Step 2: Check the constraints on name

   Name must have length between 2 and 10. Given name is 'A' (length 1), which fails min_length=2.

3. Final Answer:

   Validation error because price is not greater than 0 and name is too short -> Option A

4. Quick Check:

   Both price and name fail constraints [OK]

Hint: Check all Field constraints; both price and name fail here [OK]

Common Mistakes:

• Assuming only one field causes error
• Ignoring min_length constraint on strings
• Thinking negative price passes gt=0

4. What is wrong with this FastAPI Pydantic model field definition?

from pydantic import BaseModel, Field

class User(BaseModel):
    age: int = Field(min_length=18)

medium

A. Field cannot be used with integers

B. min_length is invalid for integer fields

C. Missing default value for age

D. Age should be a string, not int

Solution

1. Step 1: Understand Field constraints for int type

   Integer fields use gt or ge for minimum value, not min_length.

2. Step 2: Identify the error in the code

   Using min_length on an int field causes a validation error because it applies only to strings or collections.

3. Final Answer:

   min_length is invalid for integer fields -> Option B

4. Quick Check:

   min_length applies to strings, not ints [OK]

Hint: Use gt/ge for numbers, min_length for strings only [OK]

Common Mistakes:

• Using string constraints on numeric fields
• Thinking Field() can't be used with int
• Confusing default value requirement

5. You want to create a FastAPI model for a product with a tags field that must be a list of strings, each string at least 2 characters long. Which is the best way to enforce this using Pydantic Field()?

hard

A. from typing import List
from pydantic import BaseModel, Field, conlist, constr

class Product(BaseModel):
tags: conlist(constr(min_length=2))

B. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_length=2)

C. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_items=2)

D. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_length=2, max_length=10)

Solution

1. Step 1: Understand the requirement for list of strings with string length constraints

   We need to enforce each string in the list to have at least 2 characters.

2. Step 2: Identify how to apply constraints on list items

   Pydantic's constr can constrain strings, and conlist can constrain lists of constrained types.

3. Step 3: Evaluate options

   from typing import List from pydantic import BaseModel, Field, conlist, constr class Product(BaseModel): tags: conlist(constr(min_length=2)) uses conlist(constr(min_length=2)) which correctly enforces each string's min length inside the list. Others either apply min_length to the list itself or incorrectly.

4. Final Answer:

   Use conlist with constr to enforce string length inside list -> Option A

5. Quick Check:

   conlist(constr(min_length=2)) = correct nested constraints [OK]

Hint: Use conlist with constr for list of strings with length limits [OK]

Common Mistakes:

• Applying min_length to the list instead of strings inside
• Not using constr for string constraints
• Confusing min_items with min_length