* Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

    * 没有新的模式定义 micro-language 需要学习。
    * 如果你知道 Python types，你就知道如何使用 Pydantic。
* 和你 **<abbr title="集成开发环境，和代码编辑器类似">IDE</abbr>/<abbr title="一个检查代码错误的程序">linter</abbr>/brain** 适配:
    * 因为 pydantic 数据结构仅仅是你定义的类的实例；自动补全，linting，mypy 以及你的直觉应该可以和你验证的数据一起正常工作。
* 验证**复杂结构**:
    * 使用分层的 Pydantic 模型, Python `typing`的 `List` 和 `Dict` 等等。
    * 验证器使我们能够简单清楚的将复杂的数据模式定义、检查并记录为 JSON Schema。
    * 你可以拥有深度**嵌套的 JSON** 对象并对它们进行验证和注释。
* **可扩展**:

    The most complex problem is building an authentication/authorization provider like those, but **FastAPI** gives you the tools to do it easily, while doing the heavy lifting for you.

## **FastAPI** utilities

FastAPI provides several tools for each of these security schemes in the `fastapi.security` module that simplify using these security mechanisms.

from fastapi.exceptions import HTTPException
from fastapi.testclient import TestClient

app = FastAPI()

router = APIRouter()


class ContentSizeLimitMiddleware:
    """Content size limiting middleware for ASGI applications
    Args:
      app (ASGI application): ASGI application
      max_content_size (optional): the maximum content size allowed in bytes, None for no limit
    """