Solving the Json Object Validation Error in FastAPI with Pydantic

Learn how to fix the `Json` object validation error in FastAPI using Pydantic by understanding the cause and applying a simple solution.
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Understanding the Json Object Validation Error in FastAPI with Pydantic

When working with FastAPI and Pydantic, it is common to encounter validation errors, especially when dealing with nested JSON objects. One of our readers faced an issue regarding a Json object inside another JSON object. In this guide, we'll explore the problem and present a clear solution to help you understand how to avoid similar pitfalls.

The Problem

The reader described a scenario where a ValidationError occurred due to a nested JSON. Here’s the structure of the classes being used:

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

In the provided input JSON, when the otherData field was included, a validation error was triggered, specifically:

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

The Cause of the Validation Error

The error arises from the fact that the Json type in Pydantic expects a JSON string for deserialization. In the validation process, it checks whether the provided input fits the expected type, which can be either str, bytes, or bytearray. By the time the otherData is being processed, it has already been deserialized into a Python dictionary, which is incompatible with the Json type expectations.

How to Fix the Issue

To address the validation issue, we can modify the definition of otherData in the relevant classes. Instead of using Optional[Json], we can set it as Optional[Dict]. This indicates that the field can either be an empty value or a dictionary with key-value pairs that reflects a JSON structure.

Here’s the Revised Code:

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

Additional Context

Using Dict: By changing otherData to Optional[Dict], we are allowing for a more flexible structure that Pydantic can easily validate, thus preventing deserialization errors related to JSON objects.

Handling Complexity: When building nested schemas, always consider how data is serialized and deserialized, especially with types that require specific formatting.

Conclusion

Validation errors in FastAPI when dealing with nested JSON can be tricky, but understanding how types like Json work can help you navigate these challenges. By following our suggested changes, you should be able to handle otherData successfully without encountering validation errors. Always remember to test your input thoroughly, especially when working with complex data structures!

If you find yourself facing similar issues, don't hesitate to reach out for more guidance or share your experiences in the comments below!