How to Implement Filtering in FastAPI: Simple Guide

To implement filtering in FastAPI, define query parameters in your endpoint function that represent filter criteria. Use these parameters to filter your data source before returning results, optionally leveraging Pydantic models for validation and clarity.

Syntax

In FastAPI, filtering is done by adding query parameters to your endpoint function. These parameters can be optional and have default values. You then use these parameters to filter your data.

Example parts:

• param: type = None means the filter is optional.
• Use Query from fastapi to add metadata or validation.
• Filter your data inside the function using these parameters.

python

from fastapi import FastAPI, Query
from typing import Optional

app = FastAPI()

@app.get("/items/")
async def read_items(
    name: Optional[str] = Query(None, description="Filter by item name"),
    max_price: Optional[float] = Query(None, description="Filter by max price")
):
    # Imagine this is your data source
    items = [
        {"name": "apple", "price": 5},
        {"name": "banana", "price": 3},
        {"name": "cherry", "price": 7}
    ]
    filtered = []
    for item in items:
        if name and name.lower() not in item["name"].lower():
            continue
        if max_price and item["price"] > max_price:
            continue
        filtered.append(item)
    return filtered

Example

This example shows a FastAPI app with an endpoint /items/ that filters items by optional name and max_price query parameters. It returns only items matching the filters.

python

from fastapi import FastAPI, Query
from typing import Optional

app = FastAPI()

items = [
    {"name": "apple", "price": 5},
    {"name": "banana", "price": 3},
    {"name": "cherry", "price": 7}
]

@app.get("/items/")
async def read_items(
    name: Optional[str] = Query(None, description="Filter by item name"),
    max_price: Optional[float] = Query(None, description="Filter by max price")
):
    filtered = []
    for item in items:
        if name and name.lower() not in item["name"].lower():
            continue
        if max_price and item["price"] > max_price:
            continue
        filtered.append(item)
    return filtered

Output

GET /items/?name=ap&max_price=6
[
  {"name": "apple", "price": 5}
]

Common Pitfalls

Common mistakes when implementing filtering in FastAPI include:

• Not making query parameters optional, causing errors if filters are not provided.
• Not validating or sanitizing input, which can cause unexpected behavior.
• Filtering data inefficiently inside the endpoint, which can slow down response times.
• Ignoring case sensitivity when filtering strings.

Always use Optional types and consider using Query for validation and documentation.

python

from fastapi import FastAPI

app = FastAPI()

# Wrong: query parameters are required, causing errors if missing
@app.get("/items/")
async def read_items(name: str, max_price: float):
    # This will fail if parameters are not provided
    pass

# Right: make parameters optional with defaults
from typing import Optional
from fastapi import Query

@app.get("/items/")
async def read_items(
    name: Optional[str] = Query(None),
    max_price: Optional[float] = Query(None)
):
    pass

Quick Reference

Tips for filtering in FastAPI:

• Use Optional query parameters for filters.
• Use Query to add descriptions and validation.
• Filter your data inside the endpoint function before returning.
• Consider case-insensitive filtering for strings.
• For complex filters, use Pydantic models to group parameters.

Key Takeaways

Use optional query parameters to implement filtering in FastAPI endpoints.

Leverage Query from fastapi for validation and documentation of filters.

Filter your data inside the endpoint function based on query parameter values.

Make string filters case-insensitive for better user experience.

Avoid required query parameters unless absolutely necessary to prevent errors.