from fastapi import FastAPI
from fastapi.responses import JSONResponse
from pydantic import BaseModel


class Item(BaseModel):
    id: str
    value: str


class Message(BaseModel):
    message: str


app = FastAPI()


@app.get(
    "/items/{item_id}",
    response_model=Item,
    responses={
        404: {"model": Message, "description": "The item was not found"},
        200: {

 * deleting stopword items, as well as reloading the dictionary
 * from its source file.
 */
public class StopwordsFile extends DictionaryFile<StopwordsItem> {
    private static final String STOPWORDS = "stopwords";

    /** The list of stopword items loaded from the dictionary file. */
    List<StopwordsItem> stopwordsItemList;

    /**
     * Constructs a new stopwords file.

from fastapi import FastAPI
from fastapi.testclient import TestClient
from inline_snapshot import snapshot
from pydantic import BaseModel, ConfigDict, Field

app = FastAPI()


class Model(BaseModel):
    name: str = Field(alias="alias")


class ModelNoAlias(BaseModel):
    name: str

    model_config = ConfigDict(
        json_schema_extra={
            "description": (

from typing import Annotated

from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(
    *,
    item_id: int,
    item: Annotated[
        Item,
        Body(
            openapi_examples={

A plugin manager would need to know how to find the dependencies of a plugin whether that be from a workspace of an IDE, a local Maven repository, or a remote Maven repository. During development in an IDE we would need layered resolution approach that would allow resolution from the workspace, then the local Maven repository and then from any number of remote Maven repositories. During development from the command line we would need a layered resolution approach that would allow resolution from...

So, you would be able to, for example, share the same data from a Django application in a database with a FastAPI application. Or gradually migrate a Django application using the same database.

And your users would be able to login from your Django app or from your **FastAPI** app, at the same time.

///

## Hash and verify the passwords { #hash-and-verify-the-passwords }

Import the tools we need from `pwdlib`.

# Classes as Dependencies { #classes-as-dependencies }

Before diving deeper into the **Dependency Injection** system, let's upgrade the previous example.

## A `dict` from the previous example { #a-dict-from-the-previous-example }

In the previous example, we were returning a `dict` from our dependency ("dependable"):

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

        }

        def xslClasspath = classpath.plus(objects.fileCollection().from(ClasspathUtil.getClasspathForClass(XslTransformer)))

        fs.copy {
            from(getStylesheetDirectory()) {
                include "**/*.xml"
                include "*.xsl"
            }
            from(archives.zipTree(getDocbookStylesheets().singleFile)) {
                eachFile {

from fastapi import FastAPI
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str | None = None
    description: str | None = None
    price: float | None = None
    tax: float = 10.5
    tags: list[str] = []


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},

     *
     * @param contextHandle the context handle from registration
     */
    public WitnessUnregisterMessage(byte[] contextHandle) {
        super(WITNESS_UNREGISTER);
        this.contextHandle = contextHandle != null ? contextHandle.clone() : new byte[20];
    }

    /**
     * Sets the context handle for unregistration.
     *
     * @param contextHandle the context handle from registration
     */