```

!!! note
    A path parameter is always required as it has to be part of the path.

    So, you should declare it with `...` to mark it as required.

    Nevertheless, even if you declared it with `None` or set a default value, it would not affect anything, it would still be always required.

## Order the parameters as you need

!!! tip

Even if you declared those relationships in your Pydantic models.

But with ORM mode, as Pydantic itself will try to access the data it needs from attributes (instead of assuming a `dict`), you can declare the specific data you want to return and it will be able to go and get it, even from ORMs.

## CRUD utils

Now let's see the file `sql_app/crud.py`.

# Separate OpenAPI Schemas for Input and Output or Not

When using **Pydantic v2**, the generated OpenAPI is a bit more exact and **correct** than before. 😎

In fact, in some cases, it will even have **two JSON Schemas** in OpenAPI for the same Pydantic model, for input and output, depending on if they have **default values**.

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output

## Self-hosting JavaScript and CSS for docs

Self-hosting the JavaScript and CSS could be useful if, for example, you need your app to keep working even while offline, without open Internet access, or in a local network.

Here you'll see how to serve those files yourself, in the same FastAPI app, and configure the docs to use them.

### Project file structure

### Types and validation

These environment variables can only handle text strings, as they are external to Python and have to be compatible with other programs and the rest of the system (and even with different operating systems, as Linux, Windows, macOS).

That means that any value read in Python from an environment variable will be a `str`, and any conversion to a different type or validation has to be done in code.

You could load it at the top level of the module/file, but that would also mean that it would **load the model** even if you are just running a simple automated test, then that test would be **slow** because it would have to wait for the model to load before being able to run an independent part of the code.

    contents = f.read()
    print(contents)
```

Underneath, the `open("./somefile.txt")` creates an object that is called a "Context Manager".

When the `with` block finishes, it makes sure to close the file, even if there were exceptions.

When you create a dependency with `yield`, **FastAPI** will internally create a context manager for it, and combine it with some other related tools.

...and with that single declaration you get:

* Editor support, including:
    * Completion.
    * Type checks.
* Validation of data:
    * Automatic and clear errors when the data is invalid.
    * Validation even for deeply nested JSON objects.
* <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from:
    * JSON.
    * Path parameters.

For "synchronous" (contrary to "asynchronous") they commonly also use the term "sequential", because the computer / program follows all the steps in sequence before switching to a different task, even if those steps involve waiting.

### Concurrency and Burgers

This idea of **asynchronous** code described above is also sometimes called **"concurrency"**. It is different from **"parallelism"**.

If we want to call asynchronous functions in our tests, our test functions have to be asynchronous. AnyIO provides a neat plugin for this, that allows us to specify that some test functions are to be called asynchronously.

## HTTPX

Even if your **FastAPI** application uses normal `def` functions instead of `async def`, it is still an `async` application underneath.