响应模型与状态码
学习定义明确的响应模型,并为各种 API 操作设置恰当的 HTTP 状态码。
响应模型与状态码 是 CoddyKit 上的免费 FastAPI Backend Development Bootcamp 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 FastAPI Backend Development Bootcamp 学习路径的一部分,你的进度在网页和 CoddyKit 应用中同步。 FastAPI Backend Development Bootcamp 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。
API Responses: The Basics
When you make a request to an API, the server sends back a response. This response isn't just data; it also includes important information about the request's outcome.
FastAPI makes it easy to return data, usually as JSON. But we can make our APIs even better by being explicit about what data to expect and what happened.
Why Use Response Models?
Response models define the exact structure of the data your API will send back. This is crucial for several reasons:
- Data Consistency: Ensures your API always returns data in a predictable format.
- Automatic Docs: FastAPI automatically generates OpenAPI documentation showing the expected response structure.
- Data Validation: FastAPI can validate the outgoing data against your model, catching errors before sending.
Defining a Simple Response Model
We use Pydantic models to define response structures. Then, we tell FastAPI which model to use with the response_model parameter in our endpoint decorator.
Try running this example and check the /docs endpoint!
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Product(BaseModel):
name: str
price: float
is_available: bool = True
@app.get("/products/single", response_model=Product)
def get_single_product():
return {"name": "Coffee Mug", "price": 9.99, "is_available": True}
# To run: uvicorn main:app --reloadReturning Lists with Response Models
What if your endpoint returns a list of items? You can specify this in your response_model by using Python's List type from the typing module.
This tells FastAPI to expect a list where each item matches your Pydantic model.
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
app = FastAPI()
class Book(BaseModel):
title: str
author: str
@app.get("/books", response_model=List[Book])
def get_all_books():
return [
{"title": "The Hobbit", "author": "J.R.R. Tolkien"},
{"title": "1984", "author": "George Orwell"}
]
# To run: uvicorn main:app --reloadUnderstanding HTTP Status Codes
Beyond the data, every API response includes an HTTP Status Code. This three-digit number tells the client about the outcome of their request.
- 2xx Success: Request was successfully received, understood, and accepted. (e.g., 200 OK, 201 Created)
- 4xx Client Error: The client made an error. (e.g., 400 Bad Request, 404 Not Found)
- 5xx Server Error: The server failed to fulfill an apparently valid request. (e.g., 500 Internal Server Error)
FastAPI's Default Status Codes
FastAPI automatically assigns default status codes based on the HTTP method:
- GET:
200 OK - POST:
200 OK(but often201 Createdis better) - PUT/DELETE:
200 OK
While these defaults work, explicitly setting status codes makes your API more precise and user-friendly.
Setting Custom Success Codes (201)
For operations that create a new resource (like a POST request), returning a 201 Created status code is best practice. You can specify this directly in your path operation decorator.
Run this and observe the network response code!
from fastapi import FastAPI, status
from pydantic import BaseModel
app = FastAPI()
class NewItem(BaseModel):
name: str
description: str | None = None
@app.post("/items", status_code=status.HTTP_201_CREATED)
def create_item(item: NewItem):
# Imagine saving 'item' to a database here
return {"message": "Item created successfully", "item": item}
# To run: uvicorn main:app --reloadHandling Errors with HTTPException (404)
When a requested resource isn't found, you should return a 404 Not Found status. FastAPI provides HTTPException to raise these errors easily.
This stops execution and returns a standard JSON error response.
from fastapi import FastAPI, HTTPException, status
app = FastAPI()
fake_items_db = {"foo": {"name": "Foo"}, "bar": {"name": "Bar"}}
@app.get("/items/{item_id}")
def read_item(item_id: str):
if item_id not in fake_items_db:
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Item not found")
return fake_items_db[item_id]
# To run: uvicorn main:app --reloadCombining Models & Status Codes
You'll often use both response models and custom status codes together. For example, a successful update might return a 200 OK with the updated resource, while a failed update might return a 400 Bad Request.
This creates robust and predictable API behavior.
from fastapi import FastAPI, status
from pydantic import BaseModel
app = FastAPI()
class UserOut(BaseModel):
id: int
username: str
@app.put("/users/{user_id}", response_model=UserOut, status_code=status.HTTP_200_OK)
def update_user(user_id: int, new_username: str):
# Imagine updating user in DB
if user_id == 1:
return {"id": user_id, "username": new_username}
return {"id": user_id, "username": "default_user"}
# To run: uvicorn main:app --reloadQuick Check: Status Codes
A client sends a POST request to create a new user. The server successfully processes the request and saves the user data. Which HTTP status code is the most appropriate to return?
Recap: Clear Responses
In this lesson, you learned how to make your FastAPI responses clear and predictable. We covered:
- Defining response models with Pydantic for consistent data and automatic documentation.
- Understanding and explicitly setting HTTP status codes like
201 Createdor handling errors withHTTPExceptionfor404 Not Found.
These practices greatly improve the usability and robustness of your API.
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常见问题解答
「响应模型与状态码」课时是免费的吗?
是的 — 「响应模型与状态码」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 FastAPI Backend Development Bootcamp 课程的其余内容,请升级到 CoddyKit PRO。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。
「响应模型与状态码」这节课中我会学到什么?
学习定义明确的响应模型,并为各种 API 操作设置恰当的 HTTP 状态码。 你通过在浏览器中直接运行的动手代码来练习 FastAPI Backend Development Bootcamp,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 FastAPI Backend Development Bootcamp 需要有经验吗?
无需任何先前经验。CoddyKit 上的 FastAPI Backend Development Bootcamp 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。
「响应模型与状态码」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 FastAPI Backend Development Bootcamp 课中编写并运行代码吗?
能。每节 FastAPI Backend Development Bootcamp 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。