Field validation rules in FastAPI - Step-by-Step Execution

Concept Flow - Field validation rules

Define Pydantic Model with Fields

↓

Add Validation Rules (e.g., min_length, gt)

↓

Receive Input Data

↓

Pydantic Validates Input

↓

Process Data

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Client Fixes Input

Input data is checked against rules defined in Pydantic models. If valid, data is processed; if not, errors are returned.

Execution Sample

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, Field

class User(BaseModel):
    name: str = Field(min_length=3)
    age: int = Field(gt=0)

app = FastAPI()

Defines a User model with validation rules: name must be at least 3 characters, age must be greater than 0.

Execution Table

Step	Input Data	Validation Check	Result	Action
1	{"name": "Al", "age": 25}	name min_length=3	Fail	Return error: name too short
2	{"name": "Alice", "age": -1}	age gt=0	Fail	Return error: age must be > 0
3	{"name": "Bob", "age": 30}	name min_length=3 and age gt=0	Pass	Process data successfully
4	{"name": "Eve", "age": 0}	age gt=0	Fail	Return error: age must be > 0

💡 Pydantic validates all fields, collects all errors if any fail, and returns them; passes if all rules are met.

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4
name	undefined	"Al"	"Alice"	"Bob"	"Eve"
age	undefined	25	-1	30	0
validation_result	none	fail	fail	pass	fail

Key Moments - 3 Insights

Why does the validation fail when name is "Al"?

Because the name field requires at least 3 characters (see execution_table step 1), "Al" has only 2 characters.

What happens if age is 0?

Validation fails since age must be greater than 0 (execution_table step 4), so 0 is not accepted.

Does validation check all fields even if one fails?

Yes, Pydantic validates all fields and collects all errors even if some fail (see exit_note).

Visual Quiz - 3 Questions

Look at the execution table, what is the validation result at step 3?

A Pass

B Error

C Fail

D Skipped

💡 Hint

Check the 'Validation Check' and 'Result' columns at step 3 in the execution_table.

At which step does the age field fail validation?

A Step 2

B Step 1

C Step 3

D Step 4

💡 Hint

Look for 'age gt=0' failure in the execution_table rows.

If the name field had min_length=2 instead of 3, which step would pass validation?

A Step 2

B Step 3

C Step 1

D Step 4

💡 Hint

Consider the name length in step 1 and the min_length rule in the execution_table.

Concept Snapshot

Field Validation Rules in FastAPI:
- Use Pydantic models to define fields.
- Add rules like min_length, gt (greater than).
- Input data is checked automatically.
- Errors returned if rules fail.
- Valid data proceeds to processing.

Full Transcript

In FastAPI, field validation rules are set using Pydantic models. Each field can have constraints like minimum length for strings or greater-than for numbers. When input data arrives, FastAPI uses these rules to check if the data is valid. If any rule fails, FastAPI returns an error immediately, telling the client what is wrong. If all rules pass, the data is accepted and processed. This helps keep data clean and safe without extra code.

Practice

1. What is the purpose of using Field with parameters like min_length or ge in FastAPI models?

easy

A. To validate and restrict input data before processing

B. To format the output data after processing

C. To create database tables automatically

D. To handle HTTP request routing

Solution

1. Step 1: Understand Field usage in FastAPI

   Field is used to define extra validation rules on model fields.

2. Step 2: Recognize validation purpose

   Parameters like min_length or ge ensure input data meets conditions before API logic runs.

3. Final Answer:

   To validate and restrict input data before processing -> Option A

4. Quick Check:

   Field validation = Input data check [OK]

Hint: Field with min_length or ge always validates input data [OK]

Common Mistakes:

• Confusing validation with output formatting
• Thinking Field creates database tables
• Assuming Field controls routing

2. Which of the following is the correct way to enforce a string field to have at least 3 characters using FastAPI's Field?

easy

A. name: str = Field(length_min=3)

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

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

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

Solution

1. Step 1: Recall correct parameter name for minimum length

   FastAPI uses min_length to set minimum string length.

2. Step 2: Match option with correct syntax

   name: str = Field(min_length=3) uses min_length=3, which is correct syntax.

3. Final Answer:

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

4. Quick Check:

   min_length = min_length [OK]

Hint: Use min_length for strings, not min or length_min [OK]

Common Mistakes:

• Using incorrect parameter names like min or length_min
• Confusing min_length with min for numbers
• Misspelling parameter names

3. Given this FastAPI model snippet:

from pydantic import BaseModel, Field

class User(BaseModel):
    age: int = Field(ge=18, le=65)

user = User(age=70)

What will happen when creating user?

medium

A. An error is raised because age is greater than 65

B. User instance is created with age 70 without error

C. Age is automatically set to 65

D. Age is ignored and set to default 0

Solution

1. Step 1: Understand ge and le constraints

   ge=18 means age must be >=18, le=65 means age must be <=65.

2. Step 2: Check provided age value

   Age 70 is greater than 65, violating the le constraint.

3. Step 3: FastAPI/Pydantic behavior on violation

   It raises a validation error when constraints are not met.

4. Final Answer:

   An error is raised because age is greater than 65 -> Option A

5. Quick Check:

   ge/le constraints cause error if violated [OK]

Hint: ge and le set min/max values; violation raises error [OK]

Common Mistakes:

• Assuming values auto-correct to limits
• Thinking invalid values are ignored
• Confusing ge/le with min_length/max_length

4. Identify the error in this FastAPI model field validation:

from pydantic import BaseModel, Field

class Product(BaseModel):
    code: str = Field(regex="[A-Z]{3}[0-9]{4}")

product = Product(code="abC1234")

medium

A. The code field should be an integer, not string

B. Field does not support regex parameter

C. The regex pattern is case-sensitive and input does not match

D. Missing default value for code field

Solution

1. Step 1: Analyze regex pattern

   Pattern requires exactly 3 uppercase letters followed by 4 digits.

2. Step 2: Check input against pattern

   Input "abC1234" has lowercase letters and does not fully match pattern.

3. Step 3: Understand validation result

   Regex is case-sensitive by default, so input fails validation causing error.

4. Final Answer:

   The regex pattern is case-sensitive and input does not match -> Option C

5. Quick Check:

   Regex is case-sensitive unless flagged [OK]

Hint: Regex in Field is case-sensitive by default [OK]

Common Mistakes:

• Assuming regex ignores case without flags
• Thinking Field does not accept regex
• Confusing data types for fields

5. You want to create a FastAPI model for a username that must be 5 to 10 characters long, only letters and digits allowed, and must start with a letter. Which Field validation is correct?

hard

A. username: str = Field(min_length=5, max_length=10, regex='^[0-9][a-zA-Z0-9]*$')

B. username: str = Field(min_length=5, max_length=10, regex='^[a-zA-Z]{5,10}$')

C. username: str = Field(min_length=5, max_length=10, regex='^[a-zA-Z0-9]{5,10}$')

D. username: str = Field(min_length=5, max_length=10, regex='^[a-zA-Z][a-zA-Z0-9]*$')

Solution

1. Step 1: Check length constraints

   min_length=5 and max_length=10 ensure username length between 5 and 10.

2. Step 2: Analyze regex pattern

   Regex '^[a-zA-Z][a-zA-Z0-9]*$' means username starts with a letter, followed by letters or digits.

3. Step 3: Verify other options

   Options starting with a digit are incorrect. Options using regex '^[a-zA-Z0-9]{5,10}$' allow starting with digits. Options using '^[a-zA-Z]{5,10}$' exclude digits.

4. Final Answer:

   username: str = Field(min_length=5, max_length=10, regex='^[a-zA-Z][a-zA-Z0-9]*$') -> Option D

5. Quick Check:

   Start letter + length + allowed chars = username: str = Field(min_length=5, max_length=10, regex='^[a-zA-Z][a-zA-Z0-9]*$') [OK]

Hint: Start letter regex: ^[a-zA-Z], then allowed chars [OK]

Common Mistakes:

• Allowing username to start with digit
• Not enforcing length limits
• Using regex that excludes digits or letters incorrectly