I will show you some of the main concepts you should probably keep in mind when deploying a **FastAPI** application (although most of it applies to any other type of web application).

As it is very simple, it's relatively intuitive to learn, although the documentation gets somewhat technical at some points.

It is also commonly used for other applications that don't necessarily need a database, user management, or any of the many features that come pre-built in Django. Although many of these features can be added with plug-ins.

It would also mean that if you get data from the `Request` object directly (for example, read the body) it won't be validated, converted or documented (with OpenAPI, for the automatic API user interface) by FastAPI.

Although any other parameter declared normally (for example, the body with a Pydantic model) would still be validated, converted, annotated, etc.

But there are specific cases where it's useful to get the `Request` object.

The same way you use `Body`, `Query`, etc. with your *path operation function* parameters, use `Depends` with a new parameter:

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[13,18] *}

Although you use `Depends` in the parameters of your function the same way you use `Body`, `Query`, etc, `Depends` works a bit differently.

You only give `Depends` a single parameter.

This parameter must be something like a function.

9. This *path operation function* is not returning dataclasses (although it could), but a list of dictionaries with internal data.

    FastAPI will use the `response_model` parameter (that includes dataclasses) to convert the response.

## List fields { #list-fields }

You can define an attribute to be a subtype. For example, a Python `list`:

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

This will make `tags` be a list, although it doesn't declare the type of the elements of the list.

## List fields with type parameter { #list-fields-with-type-parameter }

But Python has a specific way to declare lists with internal types, or "type parameters":

We can do better...

## What makes a dependency { #what-makes-a-dependency }

Up to now you have seen dependencies declared as functions.

But that's not the only way to declare dependencies (although it would probably be the more common).

The key factor is that a dependency should be a "callable".

A "**callable**" in Python is anything that Python can "call" like a function.

////

/// info

In Pydantic version 1 the method to get the JSON Schema for a model was called `Item.schema()`, in Pydantic version 2, the method is called `Item.model_json_schema()`.

///

Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.

/// note

Keep in mind that different tools might have different levels of OpenAPI support.

Some of them might not show all the extra information declared yet, although in most of the cases, the missing feature is already planned for development.

///

You can add a `title`:

{* ../../docs_src/query_params_str_validations/tutorial007_an_py310.py hl[10] *}

Nevertheless, you can still do it in **FastAPI**, using one of the internal tools from Starlette.

And the docs would still work, although not adding any documentation telling that the parameter should contain a path.

### Path convertor { #path-convertor }