This code won't be executed in your app, we only need it to *document* how that *external API* should look like.

But, you already know how to easily create automatic documentation for an API with **FastAPI**.

So we are going to use that same knowledge to document how the *external API* should look like... by creating the *path operation(s)* that the external API should implement (the ones your API will call).

/// tip

* `datetime.date`:
    * Python `datetime.date`.
    * In requests and responses will be represented as a `str` in ISO 8601 format, like: `2008-09-15`.
* `datetime.time`:
    * A Python `datetime.time`.
    * In requests and responses will be represented as a `str` in ISO 8601 format, like: `14:23:55.003`.
* `datetime.timedelta`:

When we don't need to declare more validations or metadata, we can make the `q` query parameter required just by not declaring a default value, like:

```Python
q: str
```

instead of:

```Python
q: str | None = None
```

But we are now declaring it with `Query`, for example like:

//// tab | Annotated

```Python
q: Annotated[str | None, Query(min_length=3)] = None
```

////

* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.
* It has a <a href="https://docs.python.org/3/glossary.html#term-file-like-object" class="external-link" target="_blank">file-like</a> `async` interface.

For example, you could use it to read and verify passwords generated by another system (like Django) but hash any new passwords with a different algorithm like Bcrypt.

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

///

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

Notice that `response_model` is a parameter of the "decorator" method (`get`, `post`, etc). Not of your *path operation function*, like all the parameters and body.

///

`response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself....

///

## Multiple body parameters { #multiple-body-parameters }

In the previous example, the *path operations* would expect a JSON body with the attributes of an `Item`, like:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

But you can also declare multiple body parameters, e.g. `item` and `user`:

It's relatively tightly coupled with relational databases (like MySQL or PostgreSQL), so, having a NoSQL database (like Couchbase, MongoDB, Cassandra, etc) as the main store engine is not very easy.

# JSON Compatible Encoder { #json-compatible-encoder }

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder` { #using-the-jsonable-encoder }