How to handle models when programmatically creating endpoints in fastapi?

@ryleigh 

When programmatically creating endpoints in FastAPI, you can handle models by using Pydantic models. Pydantic models help you define the structure of data that will be sent and received by your API endpoints.

Here's a step-by-step guide on how to handle models when programmatically creating endpoints in FastAPI:

1. Define your Pydantic models: Start by defining your Pydantic models to represent the data that your API will accept and return. You can create a Pydantic model by creating a class that inherits from BaseModel and adding fields to define the structure of the data.

For example, you could define a model for a user like this:

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from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    age: int

1. Use your Pydantic models in your endpoint functions: When creating your endpoint functions, use your Pydantic models as parameters for request bodies or responses. FastAPI will automatically validate the data against your models.

For example, you could create an endpoint to get a user by ID like this:

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from fastapi import FastAPI
from typing import List
from models import User

app = FastAPI()

users = [{ "id": 1, "name": "Alice", "age": 30 }, { "id": 2, "name": "Bob", "age": 25 }]

@app.get("/users/{user_id}", response_model=User)
async def read_user(user_id: int):
    user = next((user for user in users if user["id"] == user_id), None)
    return user

1. Use your Pydantic models to generate OpenAPI documentation: FastAPI generates OpenAPI documentation automatically based on your endpoint functions and Pydantic models. You can view the documentation by visiting /docs or /redoc on your API endpoint.

By following these steps, you can easily handle models when programmatically creating endpoints in FastAPI. This allows you to define and enforce a consistent structure for your data and automatically generate API documentation based on your Pydantic models.