Junior — Middle
What libraries or modules are available for data type validation in FastAPI?
sobes.tech AI
Answer from AI
In FastAPI, the Pydantic library is used for data type validation, allowing you to describe data models with types and automatically validate incoming requests. Pydantic supports validation of complex structures, type conversion, and documentation generation.
Besides Pydantic, you can use standard types from the typing module for annotations, as well as additional libraries, for example:
- dataclasses — for simple data models (but without built-in validation like Pydantic)
- Marshmallow — an alternative library for serialization and validation, but less integrated with FastAPI
Example with Pydantic in FastAPI:
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
price: float
in_stock: bool
@app.post("/items/")
async def create_item(item: Item):
return item
Here, FastAPI automatically checks that the fields in the request match the types and will return an error if the data is incorrect.