#3 Request Bodies & Pydantic Models

Module 3 — Request Bodies & Pydantic Models

In this module, you’ll learn how to receive JSON bodies in FastAPI using Pydantic models. FastAPI uses Pydantic for data validation and automatic docs generation.

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1. Creating a Pydantic Model

A request body must be represented by a Pydantic model.

from fastapi import FastAPI
from pydantic import BaseModel

class Item(BaseModel):
    name: str
    price: float
    description: str | None = None
    in_stock: bool = True

app = FastAPI()

2. Receiving a Request Body

FastAPI automatically validates incoming JSON.

from fastapi import FastAPI
from pydantic import BaseModel

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

app = FastAPI()

@app.post("/items/")
async def create_item(item: Item):
    return {"message": "Item received", "item": item}

3. Nested Models

Pydantic supports nested models for structured data.

from fastapi import FastAPI
from pydantic import BaseModel

class Manufacturer(BaseModel):
    name: str
    country: str

class Product(BaseModel):
    title: str
    price: float
    manufacturer: Manufacturer

app = FastAPI()

@app.post("/products/")
async def create_product(product: Product):
    return product

4. Model Validation — Automatic Errors

If data is missing or wrong type, FastAPI returns a structured JSON error.

{
  "detail": [
    {
      "loc": ["body", "price"],
      "msg": "field required",
      "type": "value_error.missing"
    }
  ]
}

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