## Pydantic `Settings` { #pydantic-settings }

Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with <a href="https://docs.pydantic.dev/latest/concepts/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>.

            startAnotherThread()
          }

          return readyTask
        }

        // Notify the coordinator of a task that's coming up soon.
        coordinatorWaiting -> {
          if (minDelayNanos < coordinatorWakeUpAt - now) {
            backend.coordinatorNotify(this@TaskRunner)
          }
          return null
        }

    * 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.
    * Query parameters.
    * Cookies.
    * Headers.
    * Forms.
    * Files.

          state = STATE_READ_RESPONSE_HEADERS
          responseBuilder
        }
        statusLine.code in (102 until 200) -> {
          // Processing and Early Hints will mean a second headers are coming.
          // Treat others the same for now
          state = STATE_READ_RESPONSE_HEADERS
          responseBuilder
        }
        else -> {
          state = STATE_OPEN_RESPONSE_BODY

3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function (`iterfile`).

    So, it is a generator function that transfers the "generating" work to something else internally.

    * 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.
    * Query parameters.
    * Cookies.
    * Headers.
    * Forms.
    * Files.

    return burgers
```

With `async def`, Python knows that, inside that function, it has to be aware of `await` expressions, and that it can "pause" ⏸ the execution of that function and go do something else 🔀 before coming back.

When you want to call an `async def` function, you have to "await" it. So, this won't work:

```Python
# This won't work, because get_burgers was defined with: async def
burgers = get_burgers(2)
```

You will get completion in code you might even consider impossible before. As for example, the `price` key inside a JSON body (that could have been nested) that comes from a request.

No more typing the wrong key names, coming back and forth between docs, or scrolling up and down to find if you finally used `username` or `user_name`.

### Short { #short }

And be compatible with all of them at the same time.

///

Create a utility function to hash a password coming from the user.

And another utility to verify if a received password matches the hash stored.

And another one to authenticate and return a user.

* **Define requirements**: from request path parameters, query parameters, headers, bodies, dependencies, etc.
* **Convert data**: from the request to the required type.
* **Validate data**: coming from each request:
    * Generating **automatic errors** returned to the client when the data is invalid.
* **Document** the API using OpenAPI:
    * which is then used by the automatic interactive documentation user interfaces.