Adding custom validators

Mode360 Solutions is the organization behind the comment moderation system, and you're given a task to create a validation service for all employees. The system should be able to validate input details (username, email and age) such that only employees with an official email address can register.

You need to define a Pydantic User model using @field_validator decorator on email to check if the entered email ends with @mode360.com

These validators enhance security and system integration.

This exercise is part of the course

Deploying AI into Production with FastAPI

Exercise instructions

Add the Pydantic decorator to create a custom validator on the email field.
Use the string endswith method to check if the email address ends with the @mode360.com domain.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from pydantic import BaseModel, field_validator, Field

class User(BaseModel):
    username: str = Field(..., min_length=5, max_length=20)  
    email: str
    age: int

    # Add the Pydantic decorator to validate
    ____('email')  
    def email_must_be_example_domain(cls, user_email):
        # Use the endswith method to validate the email ends with @mode360.com
        if not user_email.endswith("____"):
            raise ValueError('Email must be from the mode360.com domain')
        return user_email

Edit and Run Code

This exercise is part of the course

Deploying AI into Production with FastAPI

AdvancedSkill Level

4.9+

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Start serving your ML model's predictions via FastAPI endpoints. You'll learn to load pre-trained ML models and create API endpoints to serve predictions as serialized responses over HTTP requests. You'll leverage Pydantic data models to validate requests and responses.

Exercise 1: GET and POST requests for AI Exercise 2: GET endpoint for model information Exercise 3: POST endpoint for model registration Exercise 4: FastAPI prediction with a pre-trained model Exercise 5: Load the pre-trained model Exercise 6: Create the prediction endpoint Exercise 7: Running the FastAPI app Exercise 8: Request and response models Exercise 9: Create a Pydantic model for ML input Exercise 10: Validate request and response for ML prediction

Learn how to serve machine learning models through FastAPI endpoints. This chapter covers creating endpoints that return predictions, handling different types of input data, and implementing robust input validation. You'll build production-ready APIs that can validate different types of input data while having ML models loaded at server startup with zero downtime.

Exercise 1: Handling different input types in FastAPI Exercise 2: Handle textual request data Exercise 3: Handle numerical request data Exercise 4: Input validation in FastAPI Exercise 5: Field validation Exercise 6: Adding custom validators

Current Exercise

Exercise 7: Testing custom validators Exercise 8: Loading a pre-trained model Exercise 9: Loading AI model at server startup Exercise 10: Health-check API for model loading Exercise 11: Returning structured prediction response Exercise 12: Returning structured output from API Exercise 13: Testing the endpoint for structured response

This chapter covers securing APIs with key-based authentication, managing request rates with custom rate limiting, and improving performance through asynchronous processing. You'll learn to protect endpoints, prevent abuse, and handle time-consuming tasks efficiently, preparing your API for production.

Exercise 1: API Key Authentication Exercise 2: Securing APIs with key authentication Exercise 3: Secure the API endpoint Exercise 4: Rate Limiting Exercise 5: Implementing rate limiter Exercise 6: Add rate limiting to endpoint Exercise 7: Asynchronous processing Exercise 8: Create an async sentiment analysis endpoint Exercise 9: Implementing background tasks Exercise 10: Handling timeout errors

This chapter covers advanced topics that will enable you to support FastAPI apps long term in production. Topics include versioning and documenting API endpoints, advanced input validation to support more complex input and output, and monitoring and logging to ensure apps are running correctly and troubleshoot live when they are not.

Exercise 1: API versioning and documentation Exercise 2: Classify code changes for new endpoint versions Exercise 3: Adding a new endpoint version Exercise 4: Adding an API description header Exercise 5: Advanced input validation and error handling Exercise 6: Nesting models Exercise 7: Custom model validator Exercise 8: Global exception handler Exercise 9: Monitoring and logging Exercise 10: Logging time to load a model Exercise 11: Adding a simple health check endpoint Exercise 12: Sharing model parameters with monitoring Exercise 13: Wrap-up