All these parameters can be different than "`static`", adjust them with the needs and specific details of your own application.

## More info

Use the `TestClient` object the same way as you do with `httpx`.

Write simple `assert` statements with the standard Python expressions that you need to check (again, standard `pytest`).

```Python hl_lines="2  12  15-18"
{!../../../docs_src/app_testing/tutorial001.py!}
```

!!! tip
    Notice that the testing functions are normal `def`, not `async def`.

```hl_lines="4"
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

That line shows the URL where your app is being served, in your local machine.

### Check it

Open your browser at <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.

You will see the JSON response as:

```JSON
{"message": "Hello World"}
```

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

Let's update the example from [SQL (Relational) Databases](../tutorial/sql-databases.md){.internal-link target=_blank} to use a testing database.

All the app code is the same, you can go back to that chapter check how it was.

The only changes here are in the new testing file.

Your normal dependency `get_db()` would return a database session.

!!! tip
    If you need GraphQL, I still would recommend you check out <a href="https://strawberry.rocks/" class="external-link" target="_blank">Strawberry</a>, as it's based on type annotations instead of custom classes and types.

## Learn More

---

"_If you're looking to learn one **modern framework** for building REST APIs, check out **FastAPI** [...] It's fast, easy to use and easy to learn [...]_"

"_We've switched over to **FastAPI** for our **APIs** [...] I think you'll like it [...]_"

```

注意，此处把 `current_user` 的类型声明为 Pydantic 的 `User` 模型。

这有助于在函数内部使用代码补全和类型检查。

!!! tip "提示"

    还记得请求体也是使用 Pydantic 模型声明的吧。

    放心，因为使用了 `Depends`，**FastAPI** 不会搞混。

!!! check "检查"

    依赖系统的这种设计方式可以支持不同的依赖项返回同一个 `User` 模型。

    而不是局限于只能有一个返回该类型数据的依赖项。


## 其它模型

接下来，直接在*路径操作函数*中获取当前用户，并用 `Depends` 在**依赖注入**系统中处理安全机制。

开发者可以使用任何模型或数据满足安全需求（本例中是 Pydantic 的 `User` 模型）。

    ```

With this, we get tooling support, from editors and mypy as this code is correct in terms of types, but we also get the data filtering from FastAPI.

How does this work? Let's check that out. 🤓

### Type Annotations and Tooling

First let's see how editors, mypy and other tools would see this.

!!! tip
    You might remember that request bodies are also declared with Pydantic models.

    Here **FastAPI** won't get confused because you are using `Depends`.

!!! check
    The way this dependency system is designed allows us to have different dependencies (different "dependables") that all return a `User` model.