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使用 Pydantic 验证请求

在格式错误的输入到达模型前将其拒绝

使用 Pydantic 验证请求 是 CoddyKit 上的免费 MLOps Academy 课时。这是第 2 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MLOps Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MLOps Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Garbage In, Garbage Out

If a client sends the wrong fields or types, your model gets nonsense. You want to reject bad input before it ever reaches the model. 🛡️

Meet Pydantic

FastAPI uses Pydantic to validate requests. You describe the shape of the data with a Python class, and validation happens for free.

Define a Schema

You subclass BaseModel and list the fields with their types. This class becomes the contract for your /predict request body.

from pydantic import BaseModel

class IrisInput(BaseModel):
    sepal_length: float
    petal_width: float

Use It in the Route

You type-annotate the endpoint argument with your model. FastAPI parses, validates, and hands you a clean object.

@app.post("/predict")
def predict(data: IrisInput):
    ...

Access the Fields

Inside the function the validated data is a normal object. You read each value with simple dot access.

x = [data.sepal_length, data.petal_width]
pred = model.predict([x])

Automatic 422 Errors

If a field is missing or has the wrong type, FastAPI returns a 422 response with a clear message. You write zero error-handling code.

Add Value Constraints

You can enforce ranges with Field. Here a measurement must be greater than zero, so negatives are rejected automatically.

from pydantic import Field

sepal_length: float = Field(gt=0)

Validate a List of Rows

To score many samples at once, you accept a list of your model. Pydantic validates every item in the batch for you.

@app.post("/predict")
def predict(rows: list[IrisInput]):
    ...

Document Fields

Add descriptions and examples to fields, and they show up in the /docs page so callers know exactly what to send. 📘

sepal_length: float = Field(description="cm", examples=[5.1])

Shape the Response Too

You can declare a response_model so the output is validated and documented just like the input, keeping your API contract tight.

@app.post("/predict", response_model=Prediction)
def predict(data: IrisInput):
    ...

Why This Matters

Strong input validation is your first line of defense in production. It blocks malformed requests so the model only ever sees clean, expected data. ✅

Quick Check

A client sends a request missing a required field. What does FastAPI do thanks to Pydantic?

Recap

You defined a BaseModel schema, typed your route with it, added field constraints, and let FastAPI auto-reject bad input with clear 422 errors. Clean data in! 🙌

常见问题解答

「使用 Pydantic 验证请求」课时是免费的吗？

是的 — 「使用 Pydantic 验证请求」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MLOps Academy 课程的其余内容，请升级到 CoddyKit PRO。 MLOps Academy 课程共包含 4 节课。

「使用 Pydantic 验证请求」这节课中我会学到什么？

在格式错误的输入到达模型前将其拒绝你通过在浏览器中直接运行的动手代码来练习 MLOps Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 MLOps Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MLOps Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 2 节课，共 4 节。

「使用 Pydantic 验证请求」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 MLOps Academy 课中编写并运行代码吗？

能。每节 MLOps Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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