{* ../../docs_src/query_param_models/tutorial001_an_py310.py hl[9:13,17] *}

**FastAPI** will **extract** the data for **each field** from the **query parameters** in the request and give you the Pydantic model you defined.

## Check the Docs { #check-the-docs }

You can see the query parameters in the docs UI at `/docs`:

<div class="screenshot">
<img src="/img/tutorial/query-param-models/image01.png">
</div>

  pip install twine==3.6.0
fi

# When cross-compiling with RBE, we need to copy the macOS sysroot to be
# inside the TensorFlow root directory. We then define them as a filegroup
# target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel
# can register it as an input to compile/link actions and send it to the remote
# VMs when needed.
# TODO(b/316932689): Avoid copying and replace with a local repository rule.

- ⚡ [**FastAPI**](https://fastapi.tiangolo.com) for the Python backend API.
  - 🧰 [SQLModel](https://sqlmodel.tiangolo.com) for the Python SQL database interactions (ORM).
  - 🔍 [Pydantic](https://docs.pydantic.dev), used by FastAPI, for the data validation and settings management.
  - 💾 [PostgreSQL](https://www.postgresql.org) as the SQL database.
- 🚀 [React](https://react.dev) for the frontend.

During the development, I also contributed to <a href="https://www.starlette.dev/" class="external-link" target="_blank">**Starlette**</a>, the other key requirement.

## Development { #development }

use an **internal cache** when building the image, if a file hasn't changed since the last time building the container image, then it will **reuse the same layer** created the last time, instead of copying the file again and creating a new layer from scratch.

Just avoiding the copy of files doesn't necessarily improve things too much, but because it used the cache for that step, it can **use the cache for the next step**. For example, it could use the cache for the instruction that installs...

If the top level value of the JSON body you expect is a JSON `array` (a Python `list`), you can declare the type in the parameter of the function, the same as in Pydantic models:

```Python
images: list[Image]
```

as in:

{* ../../docs_src/body_nested_models/tutorial008_py39.py hl[13] *}

## Editor support everywhere { #editor-support-everywhere }

And you get editor support everywhere.

/// note

Keep in mind that you have to return the `JSONResponse` directly.

///

/// info

The `model` key is not part of OpenAPI.

**FastAPI** will take the Pydantic model from there, generate the JSON Schema, and put it in the correct place.

The correct place is:

* In the key `content`, that has as value another JSON object (`dict`) that contains:

# Currently, there is no way to pass multiple cookies with the same name.
# The only way to pass multiple values for cookie params is to serialize them using

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

**FastAPI** will **extract** the data for **each field** from the **headers** in the request and give you the Pydantic model you defined.

## Check the Docs { #check-the-docs }

You can see the required headers in the docs UI at `/docs`:

<div class="screenshot">
<img src="/img/tutorial/header-param-models/image01.png">
</div>

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