Resolving FastAPI Custom Validator Errors: Understanding the RuntimeError in Your Application

Learn how to address the `RuntimeError: no validator found` issue in FastAPI when dealing with custom types and validators. We'll provide a detailed explanation of the problem and the effective solution.
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Understanding the RuntimeError in FastAPI Custom Validators

If you’re working with FastAPI and customizing models with third-party libraries like HuggingFace's transformers or peft, you might run into a frustrating problem: a RuntimeError stating "no validator found." This often occurs when you attempt to create models using custom types, leading to confusion over how FastAPI recognizes these validators. In this guide, we will explore this error in detail, understand the context surrounding it, and present a straightforward solution.

Context of the Problem

What is the Error?

The specific error you encounter looks something like this:

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

This indicates that the FastAPI and Pydantic libraries need to have a built-in validator for the custom or third-party types you are trying to use in your model schema. Here’s a typical scenario in which this issue arises:

You are using a custom model schema, like SaicModelForTextGen, with fields that include types not recognized as built-in, such as PeftModelForCausalLM.

FastAPI/Pydantic defaults to validator checks for model fields, failing when it can't find validators for those custom types.

Investigating Possible Solutions

In an attempt to resolve this error, many developers experiment with variations such as setting response_model=None, modifying library versions, or tweaking the validation logic. However, these workarounds often provide unsatisfactory results, leaving you back at square one.

Implementing a Solution

Important Note: Use with Caution

Modifying Your Model Schema

Given the nature of your project and the requirement of custom types, you can use the following approach to create your custom model:

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

Implementing the API Endpoint

When you use this model within an API endpoint, it may look like this:

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

Benefits of This Approach

Immediate Solution: This enables you to sidestep the validator issues that were causing your application to fail.

Flexibility with Trusted Data: Using this method allows you to utilize data you trust, while still leveraging the power of FastAPI for other aspects of your application.

Conclusion

Remember always to validate data as best as possible; this method should only be a fallback when you are confident about your input! Thank you for reading, and happy coding!