What will you learn?

Discover how to seamlessly render a list array as a query parameter in FastAPI Swagger Docs, ensuring accurate and informative API documentation.

Introduction to the Problem and Solution

Unveil the challenge of FastAPI’s Swagger documentation failing to exhibit a list array as a query parameter. This hurdle can hinder interactions with APIs that mandate passing lists of values via query parameters. However, fear not, as we present an ingenious solution to surmount this limitation and guarantee precise API documentation.

To tackle this issue head-on, we harness FastAPI’s functionalities and employ Python data models to explicitly define request parameters. By furnishing unambiguous type hints for our endpoint parameters, we can ensure that Swagger accurately showcases the list array in the documentation. This strategy streamlines the synchronization between our code implementation and API documentation effortlessly.


from fastapi import FastAPI, Query
from pydantic import BaseModel
from typing import List

app = FastAPI()

class Item(BaseModel):
    id: int
async def read_items(tags: List[str] = Query(...)):
    return {"tags": tags}

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# Copyright PHD


In the provided code snippet: – Define a data model Item using Pydantic with an attribute tags expecting a list of strings. – The endpoint /items/ features a query parameter tags, defined as a list of strings utilizing FastAPI’s Query. – Users accessing /items/ can furnish multiple tag values in the query parameter, which are then included in the response.

This methodology ensures that Swagger accurately represents the tags parameter as a list array in the API documentation. By explicitly outlining these specifics in our data model and endpoint definition, we enhance both code clarity and API usability.

    How can I pass multiple values for the ‘tags’ parameter?

    You can pass multiple tag values by separating them with commas like so: /items/?tags=value1,value2.

    Can I set default values for query parameters that are lists?

    Yes, you can specify default values for list-type query parameters by setting them within the Query function during endpoint definition.

    Does Pydantic support validation for complex data structures like nested lists?

    Pydantic provides robust support for validating complex data structures including nested lists through its declarative modeling capabilities.

    Is it possible to document additional metadata about query parameters using FastAPI?

    FastAPI allows you to include metadata such as descriptions, aliases, and deprecated status for query parameters to enrich your API documentation further.

    How does defining explicit types improve API development workflow?

    By defining explicit types using tools like Pydantic alongside FastAPI, developers benefit from enhanced auto-completion features, improved error handling, and better self-documenting APIs.


    Mastering how to effectively showcase list arrays as query parameters in FastAPI Swagger Docs empowers developers to create comprehensive and user-friendly API documentation seamlessly. Embrace this technique to elevate your API development process with precision and clarity.



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