Implementing rate limiter

You're building a sentiment analysis API where users can analyze texts for sentiments. To prevent abuse, you need to implement rate limiting that allows only 5 requests per minute per API key. The RateLimiter class is already created and you have to add the is_rate_limited method within the RateLimiter class that checks the number of requests that have been made within the 1 minute time window.

The datetime and timedelta classes from the datetime library have been pre-imported.

Deze oefening maakt deel uit van de cursus

Deploying AI into Production with FastAPI

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Oefeninstructies

Get the current time and calculate the timestamp for one minute ago.
Filter the request list to keep only timestamps within the last minute.
Check if the number of recent requests exceeds the limit.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

def is_rate_limited(self, api_key: str) -> bool:
    # Get current time and the timestamp for one minute ago
    now = _____
    minute_ago = now - _____(minutes=1)
    
    # Remove requests older than 1 minute
    self.requests[api_key] = [
        req_time for req_time in self.requests[api_key]
        if req_time > _____]
    
    # Check if no. of requests exceeded the set limit
    if len(self.requests[api_key]) ____ self.requests_per_minute:
        return True
    self.requests[api_key].append(now)
    return False

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Deploying AI into Production with FastAPI

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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 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

Huidige oefening

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