Input validation in FastAPI

Deploying AI into Production with FastAPI

Matt Eckerle

Software and Data Engineering Leader

Validating input data

Flowchart showing ML prediction with FastAPI

Why validate the input?

Validation for data integrity
Prevent errors in the application
Integrates with Pydantic
Provided powerful tools for data validation

A logo of Pydantic

Pydantic for pre-defined function

Field validation using pydantic

Custom validation with pydantic

Custom validation using pydantic

Graceful error reporting

Error reporting during validation

Pydantic field validators

User registration endpoint
Validating the username entered by users:

from pydantic import BaseModel, Field

class User(BaseModel):
    username: str = Field(..., min_length=3, max_length=50)

Adding custom validators

class User(BaseModel):
    username: str = Field(..., 
                          min_length=3, 
                          max_length=50)
    age: int

    @validator('age')
    def age_criteria(cls, age):
        if age < 13:
            raise ValueError('User must be at least 13')
        return age

Custom validators in action

Valid request:

{"username": "john_doe", "age": 25}

Valid user: username='john_doe' age=25

Invalid request:

{"username": "too_young", "age": 10}

Validation error for {'username': 'too_young', 'age': 10}: User must be at least 13

Putting it all together

Error reporting during validation

Field validator for username
Custom validator for age
Error message if failing validation

Putting it all together

@app.post("/users")
def create_user(user: User):
    return {"message": "User created",
            "user": user.dict()}

Output:

{
  "message": "User created successfully",
    "user": {
          "username": "john_doe", 
           "age": 25
    }
}

Let's practice!

Deploying AI into Production with FastAPI