我需要记录一个用纯 Flask 2 编写的 API,我正在寻找什么是这样做的综合方法。
我找到了不同的可行解决方案,但我是 Python 和 Flask 的新手,我无法在其中进行选择。我找到的解决方案是:
为了分离不同的 API 端点,我使用了 Flask 蓝图。
MWE的结构如下:
我首先定义了两个简单的域对象,作者 和 预订 .
# author.py
class Author:
def __init__(self, id: str, name: str):
self.id = id
self.name = name
# book.py
class Book:
def __init__(self, id: str, name: str):
self.id = id
self.name = name
接下来,我使用两个单独的蓝图为它们创建了一个简单的 GET 端点。# author_apy.py
import json
from flask import Blueprint, Response
from domain.author import Author
author = Blueprint("author", __name__, url_prefix="/authors")
@author.get("/")
def authors():
authors: list[Author] = []
for i in range(10):
author: Author = Author(str(i), "Author " + str(i))
authors.append(author)
authors_dicts = [author.__dict__ for author in authors]
return Response(json.dumps(authors_dicts), mimetype="application/json")
和# book_api.json
import json
from flask import Blueprint, Response
from domain.book import Book
book = Blueprint("book", __name__, url_prefix="/books")
@book.get("/")
def books():
books: list[Book] = []
for i in range(10):
book: Book = Book(str(i), "Book " + str(i))
books.append(book)
books_dicts = [book.__dict__ for book in books]
return Response(json.dumps(books_dicts), mimetype="application/json")
最后我只是在 Flask 应用程序下注册了两个蓝图。# app.py
from flask import Flask
from api.author.author_api import author
from api.book.book_api import book
app = Flask(__name__)
app.register_blueprint(author, url_prefix="/authors")
app.register_blueprint(book, url_prefix="/books")
@app.get('/')
def hello_world():
return 'Flask - OpenAPI'
if __name__ == '__main__':
app.run()
整个源代码也可以在 GitHub 上找到.考虑到这个最小的工作示例,我想知道自动生成 OpenAPI v3 yaml/JSON 文件的最快方法是什么,例如在/api-doc.yaml 端点上公开。
PS:这是我第一个使用 Python 和 Flask 的 API。我正在尝试重现我能用 Spring-Boot 做的事情和 SpringDoc
最佳答案
根据从 Flask 迁移到 FastAPI 的建议,我试了一下并重写了 Flask-Example的问题。源代码也可在 GitHub 上获得.
该项目的结构几乎相同,但提供了一些附加功能(例如 CORS 中间件):
域的模型略有不同,扩展了BaseModel来自 Pydantic .
# author.py
from pydantic import BaseModel
class Author(BaseModel):
id: str
name: str
和# book.py
from pydantic import BaseModel
class Book(BaseModel):
id: str
name: str
使用 FastAPI 相当于 flask 蓝图 是 APIRouter .以下是作者的两个 Controller
# author_api.py
from fastapi import APIRouter
from domain.author import Author
router = APIRouter()
@router.get("/", tags=["Authors"], response_model=list[Author])
def get_authors() -> list[Author]:
authors: list[Author] = []
for i in range(10):
authors.append(Author(id="Author-" + str(i), name="Author-Name-" + str(i)))
return authors
和书# book_api.py
from fastapi import APIRouter
from domain.book import Book
router = APIRouter()
@router.get("/", tags=["Books"], response_model=list[Book])
def get_books() -> list[Book]:
books: list[Book] = []
for i in range(10):
books.append(Book(id="Book-" + str(i), name="Book-Name-" + str(i)))
return books
需要注意的是,由于 Pydantic,API 端点的响应模型是使用 Python 类型定义的。然后将这些对象类型转换为 OpenAPI 文档的 JSON 模式。最后我只是在 FastAPI 对象下注册/包含了 APIRouters 并添加了 CORS 的配置。
# app.py
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from domain.info import Info
from api.author.author_api import router as authors_router
from api.book.book_api import router as books_router
app = FastAPI()
app.include_router(authors_router, prefix="/authors")
app.include_router(books_router, prefix="/books")
app.add_middleware(CORSMiddleware,
allow_credentials=True,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/", response_model=Info)
def info() -> Info:
info = Info(info="FastAPI - OpenAPI")
return info
生成的 OpenAPI 文档可在端点访问 /openapi.json
而 UI(又名 Swagger UI,Redoc)可在 /docs
访问和
/redoc
总而言之,这是自动生成的 JSON 格式的 OpenAPI v3 文档,可用于轻松生成其他语言的 API 客户端(例如使用 OpenAPI-Generator tools)。
{
"openapi": "3.0.2",
"info": {
"title": "FastAPI",
"version": "0.1.0"
},
"paths": {
"/authors/": {
"get": {
"tags": [
"Authors"
],
"summary": "Get Authors",
"operationId": "get_authors_authors__get",
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"title": "Response Get Authors Authors Get",
"type": "array",
"items": {
"$ref": "#/components/schemas/Author"
}
}
}
}
}
}
}
},
"/books/": {
"get": {
"tags": [
"Books"
],
"summary": "Get Books",
"operationId": "get_books_books__get",
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"title": "Response Get Books Books Get",
"type": "array",
"items": {
"$ref": "#/components/schemas/Book"
}
}
}
}
}
}
}
},
"/": {
"get": {
"summary": "Info",
"operationId": "info__get",
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Info"
}
}
}
}
}
}
}
},
"components": {
"schemas": {
"Author": {
"title": "Author",
"required": [
"id",
"name"
],
"type": "object",
"properties": {
"id": {
"title": "Id",
"type": "string"
},
"name": {
"title": "Name",
"type": "string"
}
}
},
"Book": {
"title": "Book",
"required": [
"id",
"name"
],
"type": "object",
"properties": {
"id": {
"title": "Id",
"type": "string"
},
"name": {
"title": "Name",
"type": "string"
}
}
},
"Info": {
"title": "Info",
"required": [
"info"
],
"type": "object",
"properties": {
"info": {
"title": "Info",
"type": "string"
}
}
}
}
}
}
为了启动应用程序,我们还需要一个用于生产的 ASGI 服务器,例如 Uvicorn或 Hypercorn .我使用了 Uvicorn,应用程序是使用以下命令启动的:
uvicorn app:app --reload
然后它可以在您机器的端口 8000 上使用。
关于python - Flask:如何自动化 OpenAPI v3 文档?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/67849806/