Fixing Embedding Errors In Pydantic-AI With OpenWebUI

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Hey guys! Let's dive into a common issue you might encounter when using Pydantic-AI with services like OpenWebUI. It turns out that some OpenAI-compatible APIs don't always return the usage field in their responses. This can cause Pydantic-AI to throw an error because it expects that data to be there. Specifically, you might see an AttributeError: "NoneType" object has no attribute "model_dump". This article will guide you through understanding the problem, implementing a quick workaround, and discussing why Pydantic-AI should handle these situations more gracefully. So, buckle up, and let's get started!

Understanding the Issue

The core of the problem lies in how Pydantic-AI processes the responses from embedding APIs. When an API doesn't provide the usage field, the _map_usage method in pydantic_ai/embeddings/openai.py chokes because it tries to call model_dump on a None value. This is particularly annoying because the usage information might not even be crucial for locally hosted models. Imagine you're setting up your own little AI lab, and suddenly, this tiny detail brings everything to a halt!

The error occurs at line 177 in pydantic_ai/embeddings/openai.py, within the _map_usage function:

usage_data = usage.model_dump(exclude_none=True)

This line assumes that the usage variable is always a valid object with a model_dump method. However, when OpenWebUI or similar services don't return usage information, usage becomes None, leading to the dreaded AttributeError. It's like expecting your coffee machine to make tea – it just won't work!

Why is this happening?

OpenAI's API usually returns usage data, but not all OpenAI-compatible services follow this pattern. Some might omit it for simplicity, or because they don't track usage in the same way. This discrepancy creates a compatibility issue with Pydantic-AI, which is designed to work seamlessly with various providers.

A Quick Patch to the Rescue

To get around this issue, you can implement a small patch in the _map_usage method. This patch checks if the usage variable is None. If it is, it creates a default RequestUsage object with usage set to None. Here's the code:

def _map_usage(
    usage: Usage,
    provider: str,
    provider_url: str,
    model: str,
) --> RequestUsage:
    # hack
    response_data = dict(model=model, usage=None)
    if usage == None:
        return RequestUsage.extract(
            response_data,
            provider=provider,
            provider_url=provider_url,
            provider_fallback='openai',
            api_flavor='embeddings',
            details={},
        )
    # continue existing method

How does this work?

  1. Check for None: The if usage == None: line checks if the usage variable is None.
  2. Create a default RequestUsage: If usage is None, the code creates a dictionary response_data with model and usage set to None.
  3. Extract RequestUsage: It then uses RequestUsage.extract to create a RequestUsage object from this dictionary.
  4. Return: Finally, it returns this RequestUsage object, allowing the program to continue without crashing.

This patch is a temporary fix, but it allows you to keep working while waiting for a more robust solution from Pydantic-AI. It's like using duct tape to fix a leaky pipe – it's not pretty, but it gets the job done!

Potential Drawbacks

After applying this patch, you might encounter a warning: CostCalculationFailedWarning. This warning is related to the instrumentation of the method, but it doesn't affect the functionality. You can safely ignore it for now, or investigate further if cost calculation is critical for your application.

Why Pydantic-AI Should Handle This

The current behavior of Pydantic-AI is not ideal for a few reasons:

  • Lack of Flexibility: It assumes that all OpenAI-compatible APIs will return usage data, which isn't always the case.
  • Tedious Troubleshooting: The error message isn't very helpful, making it difficult to diagnose the problem. You have to dig into the code to understand what's going on.
  • Unnecessary Enforcement: For locally hosted models, the usage field might not even be relevant. Enforcing it adds unnecessary complexity.

What's the Solution?

Pydantic-AI should be more flexible in handling missing usage data. Here are a few possible solutions:

  1. Optional Usage Field: Make the usage field optional in the Usage model. This would allow the code to handle cases where the API doesn't return this data.
  2. Default Value: Provide a default value for the usage field. This would prevent the AttributeError and allow the code to continue without crashing.
  3. Error Handling: Implement better error handling to provide more informative error messages. This would make it easier to diagnose and fix the problem.

By implementing one or more of these solutions, Pydantic-AI could become more robust and easier to use with a wider range of OpenAI-compatible APIs.

Minimal, Reproducible Example

To reproduce the issue, you can use the following code snippet:

#use embeddings API with OpenWebUI

text="placeholder"
custom_domain = "http://127.0.0.1"

embed_custom_provider = OpenAIProvider(base_url=custom_domain + "/api/v1", api_key=api_key)
model_embed = OpenAIEmbeddingModel("placeholder_model:latest",provider=embed_custom_provider)
embedder= Embedder(model_embed)

result = await embedder.embed_query(text)

Steps to Reproduce:

  1. Set up OpenWebUI: Make sure you have OpenWebUI running locally.
  2. Configure the Code: Replace api_key with your API key and adjust the custom_domain if necessary.
  3. Run the Code: Execute the code snippet.
  4. Observe the Error: You should see the AttributeError in the traceback.

Conclusion

Dealing with missing usage data in OpenAI-compatible APIs can be a pain. However, with a quick patch and a better understanding of the issue, you can keep your code running smoothly. Pydantic-AI should definitely consider implementing more robust error handling to make it easier for developers to work with different providers. In the meantime, I hope this guide has been helpful in troubleshooting and resolving this issue. Happy coding, and may your embeddings always be error-free!