How to create complex schema in fastapi?

@daisha 

To create a complex schema in FastAPI, you can define a Pydantic model class with nested fields and relationships. Pydantic is the library used by FastAPI for creating data validation and serialization schemas. Here's how you can create a complex schema in FastAPI using Pydantic:

1. Define the nested Pydantic model classes for each part of the complex schema. For example, if you have a schema that includes nested objects like a user with multiple addresses, you would create separate Pydantic models for the user and address:

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from pydantic import BaseModel
from typing import List

class Address(BaseModel):
    street: str
    city: str
    state: str
    zip_code: str

class User(BaseModel):
    name: str
    age: int
    addresses: List[Address]

1. Use the nested Pydantic models in the parent Pydantic model that represents the entire complex schema. For example, you can use the User model defined above in a more complex schema definition:

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class Order(BaseModel):
    order_id: int
    user: User
    items: List[str]
    total_price: float

1. Use the complex schema in FastAPI endpoints by defining request and response models using the Pydantic models you've created. For example, in a FastAPI endpoint, you can use the Order model as the request and response models:

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from fastapi import FastAPI
from fastapi import Body

app = FastAPI()

@app.post("/order/")
async def create_order(order: Order):
    # Process the order request
    return {"message": "Order created successfully", "order": order}

@app.get("/order/{order_id}")
async def get_order(order_id: int):
    # Retrieve the order with id
    return {"order_id": order_id, "items": ["item1", "item2"], "total_price": 100.0}

By using Pydantic for creating complex schemas and validation in FastAPI, you can easily define and maintain structured and reliable data models for your web applications.