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    fastapi-mock

A utility for FastAPI that allows you to create mock endpoints quickly and easily.


Maintainers
1

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FastAPI Mock

A utility for FastAPI that allows you to create mock endpoints quickly and easily.

Overview

Installation

Install the package using pip:

pip install fastapi-mock

NOTE: FastAPI Mock requires Python 3.11+ and FastAPI working with Pydantic 2.

Usage

Return example instead of NotImplementedError

Here, we'll explore how to use FastAPI Mock by creating a FastAPI application, adding middleware, and raising NotImplementedError. Note that we'll be using the MockMiddleware class from the FastAPI Mock.

Let's define our FastAPI application:

from fastapi import FastAPI
from fastapi_mock import MockUtilities
from pydantic import BaseModel

app = FastAPI()

# just create an instance of MockUtilities and pass FastAPI app as argument to it. It will add exception handlers to
# the app automatically.
MockUtilities(app, return_example_instead_of_500=True)


class ResponseModel(BaseModel):
    message: str


@app.get("/mock-endpoint", status_code=200)
def mock() -> ResponseModel:
    # instead of ResponseModel, you can use any type annotation that is supported by FastAPI Mock.
    raise NotImplementedError()

In the above code, we define a FastAPI application, add the MockMiddleware to handle the exception, and define a GET endpoint at /mock-endpoint. When the endpoint function is called, it raises a NotImplementedError with ResponseModel set as the response model and 200 as the status code.

If you hit the endpoint /mock-endpoint, you'll see the mock data: just

{
  "message": "Hello, World ❤️"
}

NOTE: FastAPI Mock can process not only basic types, but list, tuple, set, dict, enum.Enum generic types and UnionTypo too. Also, it will resolve response models recursively, so you can define nested models.

Return example Instead of HTTP 500 Error

It also can replace HTTP 500 error with the example. To enable this feature, just pass return_example_instead_of_500=True to the MockUtilities constructor.

from fastapi import FastAPI
from fastapi_mock import MockUtilities
from pydantic import BaseModel

app = FastAPI()

MockUtilities(app, return_example_instead_of_500=True)


class ResponseModel(BaseModel):
    message: str


@app.get("/mock-endpoint")
def mock() -> ResponseModel:
    my_infinity = (
            1 / 0
    )  # raise ZeroDivisionError, then will be converted it to HTTP 500 error
    # in FastAPI ExceptionMiddleware and handled by FastAPI Mock
    return ResponseModel(message=f"UFO is real! and infinity is {my_infinity}")

Advanced Usage

Now we'll look at a more advanced usage of FastAPI Mock, including defining examples in the response model's JSON schema, utilizing field examples and defaults, configuring middleware with the custom provider for int and str types.

Examples in JSON Schema

FastAPI Mock will choose a random example from the examples list in the response model's JSON schema.

Let's try it out:

from fastapi import FastAPI
from fastapi_mock import MockUtilities
from pydantic import BaseModel, ConfigDict

app = FastAPI()

MockUtilities(app)


class ResponseModel(BaseModel):
    message: str

    model_config = ConfigDict(
        json_schema_extra={
            "examples": [{"message": "My name is (chka-chka, Slim Shady) - Eminem"}]
        }
    )


@app.get("/mock-endpoint")
def mock() -> ResponseModel:
    raise NotImplementedError()

The default status code is 200, so we don't need to specify it. Now, if you hit the endpoint /mock-endpoint, you'll see the mock data:

{
  "message": "My name is (chka-chka, Slim Shady) - Eminem"
}

Or you can define examples in route decorator:

from fastapi import FastAPI
from fastapi_mock import MockUtilities
from pydantic import BaseModel

app = FastAPI()

MockUtilities(app)


class ResponseModel(BaseModel):
    message: str


@app.get(
    "/mock-endpoint",
    openapi_extra={
        "examples": [{"message": "My name is (chka-chka, Slim Shady) - Eminem"}]
    },
)
def mock() -> ResponseModel:
    raise NotImplementedError()

PRIORITY: The examples from the route decorator have higher priority than the examples from the response model.

Field Examples and Defaults

FastAPI Mock will iterate through the fields in the response model and choose a random example (or default) from the field info.

Here's an example:

from fastapi import FastAPI
from fastapi_mock import MockUtilities
from pydantic import BaseModel, Field

app = FastAPI()

MockUtilities(app)


class ResponseModel(BaseModel):
    field_with_examples: str = Field(examples=["I", "Love", "Python"])
    field_with_default: str = Field(default="I ❤️ Python")
    field_with_default_factory: str = Field(default_factory=lambda: "I ❤️ Python\n" * 3)


@app.get("/mock-endpoint")
def mock() -> ResponseModel:
    raise NotImplementedError()

Now, if you hit the endpoint /mock-endpoint, you'll see the mock data:

{
  "field_with_examples": "Love",
  "field_with_default": "I ❤️ Python",
  "field_with_default_factory": "I ❤️ Python\nI ❤️ Python\nI ❤️ Python\n"
}

PRIORITY: The examples from the JSON schema have higher priority than the field examples. Moreover, the field examples have higher priority than the field defaults.

Custom Provider

FastAPI Mock uses the constant examples for str, random examples for int and float, bool by default. However, you can configure the middleware to use your own provider for any of basic types.

For example, let's configure the middleware to use the faker library for str type:

from fastapi import FastAPI
from fastapi_mock import MockUtilities, ExampleProvider
from pydantic import BaseModel
from faker import Faker  # pip install faker

app = FastAPI()
fake = Faker()

MockUtilities(
    app,
    example_provider=ExampleProvider(
        providers={
            str: lambda: fake.sentence()
        }
    )
)


class ResponseModel(BaseModel):
    message: str


@app.get("/mock-endpoint")
def mock() -> ResponseModel:
    raise NotImplementedError()

Now, if you hit the endpoint /mock-endpoint, you'll see the random mock data:

{
  "message": "Some random sentence from faker."
}

Contributing

Publishing a new version

  1. Update the version in pyproject.toml
  2. Commit the changes
  3. Create a new tag with the version number (e.g. git tag -a 0.1.0 -m "0.1.0")
  4. Push the tag to the repository (e.g. git push origin 0.1.0)
  5. Draft a new release on GitHub with the same version number and the release notes
  6. Setup poetry PyPi configuration ( see tutorial)
  7. Run poetry build
  8. Run poetry publish
  9. Attach the built wheel to the release
  10. Publish the release
  11. Done!

FAQs


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