Debugging nested JSON for NVIDIA's garak tool

While testing Garak against a local OpenAI-compatible API endpoint (using oobabooga's text-generation-webui), I encountered an issue with the REST generator's JSON field access. The problem arose when trying to access nested response fields, particularly the completion text located at 'choices[0].text' in the API response.

The fix required modifying how Garak's REST generator handles nested JSON paths.

Here's what a typical API response looks like:

{
  "id": "conv-1732143549602938112",
  "object": "text_completion",
  "choices": [
    {
      "index": 0,
      "finish_reason": "length",
      "text": "! When I was in college my best friend turned me on to a spiritual mentor"
    }
  ]
}

The original code attempted to access this nested structure directly, which resulted in a KeyError. The solution was to implement a helper method that could properly traverse nested JSON paths.

Here's the fixed code snippet:

def _get_nested_value(self, obj, path):
    """Helper method to get nested dictionary values using a path string."""
    try:
        current = obj
        parts = path.replace("][", "].").replace("[", ".").replace("]", "").split(".")
        for part in parts:
            if part.isdigit():
                current = current[int(part)]
            else:
                current = current[part]
        return current
    except (KeyError, IndexError, TypeError) as e:
        logging.error(f"Failed to access path '{path}' in object: {e}")
        return None

The fix can be applied in two ways:

1. Modifying the installed package directly in the virtual environment
2. Using pip's editable install mode with: pip install -e .

This modification allows Garak to properly handle nested JSON responses from OpenAI-compatible APIs, making it more robust for testing local language models. We got success! Now to begin the long process of actually using the tool for testing.

A key learning from this experience is the importance of properly handling nested data structures when working with API responses. As more organizations deploy local LLMs with OpenAI-compatible APIs, tools like Garak need to handle various response formats effectively.

Remember to use pip's editable install mode (-e flag) during development to make the development process smoother! I needed to increase the token limit in the .json file, and then subsequently increase the timeout in rest.py to account for this.

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