Скачать или смотреть Solving 500 Internal Server Error: How to Use Multiple Pydantic Models in FastAPI Responses

Learn how to resolve issues when returning multiple Pydantic models from FastAPI endpoints, ensuring smoother API responses and error-free applications.
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Understanding the Problem: Using Multiple Pydantic Models as Response in FastAPI

When working with FastAPI, a common issue developers encounter is attempting to use multiple Pydantic models in their API responses. While the desire is to return a complex data structure that includes fields from different models, doing so often results in errors, such as the dreaded 500 Internal Server Error.

In this guide, we'll explore a specific scenario in FastAPI where an error arises when trying to use both EvergreenOutput and MappingOutput as response models, and we'll provide a clear solution to this problem.

The Initial Setup

Here's the original code setup that triggers an error:

[[See Video to Reveal this Text or Code Snippet]]

Error Message

When trying to use both models for the response, you might encounter the following error:

[[See Video to Reveal this Text or Code Snippet]]

This error indicates that the expected product_id field from the EvergreenOutput model is not found in the response.

Understanding the Error

The key point to note from the error message is that the response_model in FastAPI can only be a single model or a list of one model. When you specify multiple models in the response_model, FastAPI is unable to validate the response correctly, leading to validation errors.

The server responds with 500 Internal Server Error because it cannot map the data returned from the query to the expected response models as specified.

The Solution: Merging Models into a Single Response Model

To successfully return a response containing data from both the Evergreen and Mapping tables, we can create a new Pydantic model that encapsulates both outputs. This merged model will allow the response to correctly align with FastAPI's expectations.

Step-by-Step Solution:

Create a New Pydantic Model

Define a combined model that includes both Evergreen and Mapping outputs:

[[See Video to Reveal this Text or Code Snippet]]

Update the Endpoint Function

Modify the FastAPI endpoint to use this new model for the response:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By merging the responses into a single Pydantic model, we can resolve issues relating to using multiple models as response types in FastAPI. This solution is not only effective but also keeps your API structure clean and maintainable.

If you encounter a similar issue in your FastAPI applications, always remember that the response_model should consist of either a single model or a list containing only one model type. By defining a custom response model that combines the necessary fields, you can eliminate validation errors and deliver a seamless API experience.

Happy coding!