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5 min readIn FastAPI, you can update request parameters by modifying the data in the request object or by creating a new request object with updated parameters. To modify the data in the request object, you can access the parameters using the request object's attributes and update them as needed.
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4 min readTo create a complex schema in FastAPI, you can use Pydantic models to define the structure of your data. Pydantic allows you to create complex nested schemas with support for data validation and serialization.To create a complex schema, start by defining a Pydantic model that represents the structure of your data. You can define nested fields, lists, and dictionaries within the model to represent complex data structures.
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10 min readTo deploy a scalable API using FastAPI, you can follow these steps:Create your API using FastAPI by defining your routes, endpoints, and data models. Make sure to use asynchronous functions whenever possible to take advantage of FastAPI's performance benefits. Once your API is ready, you can deploy it using a hosting service such as Heroku, AWS, or Google Cloud Platform. Use Docker containers to containerize your API, making it easier to deploy and scale.
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4 min readTo add custom validation in FastAPI, you can create a custom Field class that inherits from the Depends class provided by FastAPI. Inside this custom Field class, you can define your custom validation logic by overriding the __call__ method.Within the __call__ method, you can perform any validation checks you need and raise a HTTPException with an appropriate status code and error message if the data does not pass the validation.
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7 min readIn FastAPI, you can set up and tear down a database between tests by leveraging fixtures in a testing framework like pytest.To set up a database connection for testing, you can create a fixture function that establishes a connection to the database and returns it to the test functions that need it. This fixture function can be scoped to the module or to a specific test session, depending on your testing requirements.
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4 min readTo download a file using FastAPI, you can create an endpoint that returns a FileResponse object from the Starlette library. First, you need to import the necessary modules: from fastapi import FastAPI from starlette.responses import FileResponse Then, create a FastAPI app instance: app = FastAPI() Next, define a route that returns a file: @app.get("/download-file") async def download_file(): file_path = "path/to/your/file.
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5 min readWhen using FastAPI with Sentry for error monitoring, you can handle errors by setting up custom error handlers. We can create custom error handlers by defining an exception handler decorator in the FastAPI application. Inside the exception handler, we can log the error message to Sentry using the capture_exception method provided by the Sentry SDK. This way, any uncaught exceptions in our FastAPI application will be sent to Sentry for tracking and monitoring.
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3 min readIn FastAPI, you can define route paths by using the @app.get, @app.post, @app.put, @app.delete, or @app.patch decorators. These decorators allow you to define the HTTP method used to interact with that particular route path. You can also define route parameters by using curly braces {} in the route path. These parameters will be automatically passed to the corresponding function as arguments. Additionally, you can use the Request parameter to access the request data and query parameters.
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6 min readIn FastAPI, you can count the total hours by utilizing the datetime module. First, you need to import the necessary modules such as datetime. Then, you can create a start_time and end_time variable to store the start and end times respectively. Calculate the difference between the end and start times to get the total hours. Finally, return the total hours as a response. This can be done by creating a route using the @app.get() decorator.
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3 min readIn FastAPI, you consume query parameters from a POST request by adding parameters to the function definition using Pydantic models. These parameters will be automatically extracted from the request and validated based on the model definition. You can define these parameters as query parameters by including the parameter type and default values in the model. When you call the function with a POST request, FastAPI will validate and extract the query parameters for you to use within the function.
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3 min readTo get variable output in PowerShell, you can use the Write-Output cmdlet followed by the variable you want to display. This will output the value of the variable to the console. Another option is to use the Write-Host cmdlet to display the variable value along with any additional text or formatting. You can also use the format operator (-f) to format the output of variables in a specific way.