{* ../../docs_src/first_steps/tutorial001.py hl[8] *}

You can return a `dict`, `list`, singular values as `str`, `int`, etc.

You can also return Pydantic models (you'll see more about that later).

There are many other objects and models that will be automatically converted to JSON (including ORMs, etc). Try using your favorite ones, it's highly probable that they are already supported.

## Recap

* Import `FastAPI`.

hould-try-7c0ac7eebb3e title: 5 Advanced Features of FastAPI You Should Try - author: Kaustubh Gupta author_link: https://medium.com/@kaustubhgupta1828/ link: https://www.analyticsvidhya.com/blog/2021/06/deploying-ml-models-as-api-using-fastapi-and-heroku/ title: Deploying ML Models as API Using FastAPI and Heroku - link: https://jarmos.netlify.app/posts/using-github-actions-to-deploy-a-fastapi-project-to-heroku/ title: Using GitHub Actions to Deploy a FastAPI Project to Heroku author_link: http...

@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
    return filter_query
```

Read the new docs: [Query Parameter Models](https://fastapi.tiangolo.com/tutorial/query-param-models/).

#### `Header` Parameter Models

Use Pydantic models for `Header` parameters:

```python
from typing import Annotated

from fastapi import FastAPI, Header
from pydantic import BaseModel

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

## Pydantic Models for Input and Output

Let's say you have a Pydantic model with default values, like this one:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### Model for Input

platforms/core-configuration/kotlin-dsl-provider-plugins/   @gradle/bt-kotlin-dsl-maintainers
platforms/core-configuration/kotlin-dsl-tooling-builders/   @gradle/bt-kotlin-dsl-maintainers
platforms/core-configuration/kotlin-dsl-tooling-models/     @gradle/bt-kotlin-dsl-maintainers

# Core automation platform (Declarative DSL)
platforms/core-configuration/declarative-dsl-api/                   @gradle/bt-declarative-dsl

<img src="/img/python-types/image06.png">

Notice that this means "`one_person` is an **instance** of the class `Person`".

It doesn't mean "`one_person` is the **class** called `Person`".

## Pydantic models

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

	github.com/goccy/go-json v0.10.3 // indirect
	github.com/golang/groupcache v0.0.0-20210331224755-41bb18bfe9da // indirect
	github.com/google/btree v1.1.2 // indirect
	github.com/google/gnostic-models v0.6.8 // indirect
	github.com/google/pprof v0.0.0-20240827171923-fa2c70bbbfe5 // indirect
	github.com/grafana/regexp v0.0.0-20240518133315-a468a5bfb3bc // indirect

github.com/google/cel-go v0.21.0 h1:cl6uW/gxN+Hy50tNYvI691+sXxioCnstFzLp2WO4GCI=
github.com/google/cel-go v0.21.0/go.mod h1:rHUlWCcBKgyEk+eV03RPdZUekPp6YcJwV0FxuUksYxc=
github.com/google/gnostic-models v0.6.8 h1:yo/ABAfM5IMRsS1VnXjTBvUb61tFIHozhlYvRgGre9I=
github.com/google/gnostic-models v0.6.8/go.mod h1:5n7qKqH0f5wFt+aWF8CW6pZLLNOfYuF5OpfBSENuI8U=
github.com/google/go-cmp v0.2.0/go.mod h1:oXzfMopK8JAjlY9xF4vHSVASa0yLyX7SntLO5aqRK0M=