Multiple path parameters in FastAPI

Introduction

Sometimes you want to send more than one piece of information in the body of a request. FastAPI lets you handle multiple body parameters easily.

When you want to receive several pieces of data in a POST request body.

When your API endpoint needs to accept multiple objects or values from the client.

When you want to keep your data organized by separating it into different parameters.

When you want to validate each part of the request body separately.

When you want to clearly define what data your API expects in the request body.

Syntax

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

class User(BaseModel):
    username: str
    email: str

@app.post("/create")
async def create_item_user(item: Item, user: User):
    return {"item": item, "user": user}

Each body parameter must be a Pydantic model or a simple type.

FastAPI automatically reads and validates each parameter from the request body.

Examples

This example shows two body parameters: item and user. FastAPI reads both from the JSON body.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

class User(BaseModel):
    username: str
    email: str

@app.post("/create")
async def create_item_user(item: Item, user: User):
    return {"item_name": item.name, "user_email": user.email}

Here, the endpoint accepts a product and a customer as separate body parameters.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Product(BaseModel):
    title: str
    quantity: int

class Customer(BaseModel):
    name: str
    phone: str

@app.post("/order")
async def place_order(product: Product, customer: Customer):
    return {"product": product.title, "customer": customer.name}

Sample Program

This FastAPI app defines two models: Item and User. The endpoint /create accepts both as body parameters. It returns a message combining their data.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

class User(BaseModel):
    username: str
    email: str

@app.post("/create")
async def create_item_user(item: Item, user: User):
    return {
        "message": f"User {user.username} created item {item.name} costing ${item.price}"
    }

Important Notes

When sending the request, the JSON body must include both parameters as separate JSON objects.

Example request body:
{
"item": {"name": "Book", "price": 12.99},
"user": {"username": "alice", "email": "[e-mail]"}
}

FastAPI uses the parameter names to find the matching keys in the JSON body.

Summary

FastAPI lets you accept multiple body parameters by defining them as function arguments with Pydantic models.

Each parameter is read from the JSON body using its name as the key.

This helps organize and validate complex request data easily.

Practice

1. In FastAPI, how do you accept multiple JSON body parameters in a single endpoint function?

easy

A. Use query parameters instead of body parameters.

B. Use a single parameter with a list of dictionaries.

C. Define multiple function parameters each with a Pydantic model type.

D. Combine all parameters into a single string and parse manually.

Solution

1. Step 1: Understand FastAPI body parameter handling

   FastAPI reads JSON body parameters by matching function argument names and types, typically Pydantic models.

2. Step 2: Accept multiple parameters

   By defining multiple function parameters with Pydantic models, FastAPI expects each as a separate JSON object in the request body.

3. Final Answer:

   Define multiple function parameters each with a Pydantic model type. -> Option C

4. Quick Check:

   Multiple body parameters = multiple Pydantic model arguments [OK]

Hint: Use separate Pydantic models as function parameters for multiple bodies [OK]

Common Mistakes:

• Trying to pass multiple JSON objects in one body without models
• Using query parameters instead of body for complex data
• Combining all data into one string and parsing manually

2. Which of the following is the correct syntax to declare two body parameters user and item in a FastAPI endpoint?

easy

A. async def endpoint(user: User = Body(), item: Item = Body()):

B. async def endpoint(user: User, item: Item = Query()):

C. async def endpoint(user: User, item: Item):

D. async def endpoint(user: User = Query(), item: Item = Body()):

Solution

1. Step 1: Understand parameter declaration for multiple bodies

   FastAPI requires explicit use of Body() for multiple body parameters to distinguish them.

2. Step 2: Check each option

   async def endpoint(user: User = Body(), item: Item = Body()): correctly uses = Body() for both parameters. async def endpoint(user: User, item: Item): misses Body(), so FastAPI treats only the first as body. Options B and D incorrectly mix Query() and Body().

3. Final Answer:

   async def endpoint(user: User = Body(), item: Item = Body()): -> Option A

4. Quick Check:

   Multiple bodies require = Body() [OK]

Hint: Use = Body() for each body parameter to avoid errors [OK]

Common Mistakes:

• Omitting = Body() for multiple body parameters
• Mixing query and body decorators incorrectly
• Using default values without Body()

3. Given the following FastAPI endpoint, what will be the response if the client sends JSON body with {"user": {"name": "Alice"}, "item": {"id": 5}}?

from fastapi import FastAPI, Body
from pydantic import BaseModel

app = FastAPI()

class User(BaseModel):
    name: str

class Item(BaseModel):
    id: int

@app.post("/create")
async def create(user: User = Body(), item: Item = Body()):
    return {"user_name": user.name, "item_id": item.id}

medium

A. 500 Internal Server Error

B. 422 Unprocessable Entity error

C. {"user": {"name": "Alice"}, "item": {"id": 5}}

D. {"user_name": "Alice", "item_id": 5}

Solution

1. Step 1: Understand parameter parsing

   FastAPI matches the JSON keys "user" and "item" to the parameters user and item respectively, parsing each into the corresponding Pydantic model.

2. Step 2: Check the return statement

   The function returns a dictionary with keys "user_name" and "item_id" extracted from the parsed models, so the response JSON will contain these values.

3. Final Answer:

   {"user_name": "Alice", "item_id": 5} -> Option D

4. Quick Check:

   Parsed models return selected fields as response [OK]

Hint: Response keys come from return dict, not original JSON keys [OK]

Common Mistakes:

• Expecting the entire input JSON as output
• Confusing 422 error with correct parsing
• Assuming server error without code issues

4. Identify the error in this FastAPI endpoint that tries to accept two body parameters user and item:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class User(BaseModel):
    name: str

class Item(BaseModel):
    id: int

@app.post("/add")
async def add(user: User, item: Item):
    return {"user": user, "item": item}

medium

A. Pydantic models cannot be used as parameters.

B. Missing = Body() for multiple body parameters causes validation error.

C. Return statement must convert models to dict before returning.

D. Endpoint must be synchronous, not async.

Solution

1. Step 1: Check parameter declarations

   When multiple body parameters are declared, each must use = Body() to tell FastAPI to parse them from the body separately.

2. Step 2: Analyze the return statement

   Returning Pydantic models directly is allowed; FastAPI serializes them automatically, so no error here.

3. Final Answer:

   Missing = Body() for multiple body parameters causes validation error. -> Option B

4. Quick Check:

   Multiple bodies require = Body() [OK]

Hint: Add = Body() for each body parameter to fix errors [OK]

Common Mistakes:

• Omitting = Body() for multiple body parameters
• Thinking Pydantic models can't be returned directly
• Confusing async with sync function requirements

5. You want to create a FastAPI endpoint that accepts two body parameters: user with fields name and age, and preferences with optional color and food. Which code correctly defines the endpoint and handles missing optional fields gracefully?

hard

A. class User(BaseModel):
name: str
age: int

class Preferences(BaseModel):
color: str | None = None
food: str | None = None

@app.post("/profile")
async def profile(user: User = Body(), preferences: Preferences = Body()):
return {"user": user.dict(), "preferences": preferences.dict()}

B. class User(BaseModel):
name: str
age: int

class Preferences(BaseModel):
color: str
food: str

@app.post("/profile")
async def profile(user: User, preferences: Preferences):
return {"user": user, "preferences": preferences}

C. class User(BaseModel):
name: str
age: int

@app.post("/profile")
async def profile(user: User, preferences: dict = Body()):
return {"user": user, "preferences": preferences}

D. class User(BaseModel):
name: str
age: int

class Preferences(BaseModel):
color: Optional[str]
food: Optional[str]

@app.post("/profile")
async def profile(user: User = Body(), preferences: Preferences = Body()):
return {"user": user.dict(), "preferences": preferences.dict()}

Solution

1. Step 1: Define optional fields correctly

   Using str | None = None sets optional fields with default None, allowing missing keys without error.

2. Step 2: Declare multiple body parameters with = Body()

   This tells FastAPI to parse both user and preferences from the JSON body separately.

3. Step 3: Return dict representations for JSON serialization

   Calling .dict() on Pydantic models converts them to plain dicts for clean JSON output.

4. Final Answer:

   Defines optional fields with `str | None = None`, uses `= Body()` for multiple parameters, and returns `.dict()` representations. -> Option A

5. Quick Check:

   Optional fields + multiple bodies + dict() return = class User(BaseModel): name: str age: int class Preferences(BaseModel): color: str | None = None food: str | None = None @app.post("/profile") async def profile(user: User = Body(), preferences: Preferences = Body()): return {"user": user.dict(), "preferences": preferences.dict()} [OK]

Hint: Use field: type | None = None for optional fields [OK]

Common Mistakes:

• Not marking optional fields with default None
• Omitting = Body() for multiple parameters
• Returning Pydantic models without converting to dict