Request and Response Models

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In this section, you'll learn how to validate and structure input and output using Pydantic models in Esmerald.

Why Use Models?

Pydantic models offer: - Automatic validation of incoming request data - Auto-generated documentation - Clear structure for responses

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Request Model Example

Define a Pydantic model to validate incoming data:

from pydantic import BaseModel
from esmerald import post

class CreateUserRequest(BaseModel):
    name: str
    age: int

@post("/users")
def create_user(data: CreateUserRequest) -> dict:
    return {"message": f"Created user {data.name} who is {data.age} years old"}

When a POST request is made to /users, Esmerald: - Automatically parses the JSON - Validates it against CreateUserRequest - Injects the model instance into the handler

Invalid request? Esmerald returns a 422 with helpful details.

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Response Model Example

Define a model to structure the output:

from pydantic import BaseModel
from esmerald import get

class UserResponse(BaseModel):
    id: int
    name: str

@get("/users/{user_id}")
def get_user(user_id: int) -> UserResponse:
    return UserResponse(id=user_id, name=f"User {user_id}")

• Esmerald converts the returned model into JSON automatically

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Nested Models

class Address(BaseModel):
    city: str
    country: str

class UserProfile(BaseModel):
    name: str
    address: Address

@get("/profile")
def get_profile() -> UserProfile:
    return UserProfile(name="Alice", address=Address(city="Berlin", country="Germany"))

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List of Models

Returning a list? Just use list[Model] or List[Model].

@get("/users")
def list_users() -> list[UserResponse]:
    return [UserResponse(id=1, name="Alice"), UserResponse(id=2, name="Bob")]

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What's Next?

You've now learned how to: - Use request models for validation - Use response models for output structure - Handle nesting and collections

👉 Continue to the next section to learn about custom error handling, exceptions, and status codes in Esmerald.