To: OpenAI Technical Support Team Subject: Bug Report – Recursive Structure Error in GPT-Generated JSON Files Causing Python-docx Parsing Failure
Dear OpenAI Technical Support Team,
I am writing to report a significant technical issue related to GPT-generated JSON files that appears to reflect a recursive structural problem, which causes Python-docx to fail during document parsing and generation.
Issue Description:
I generated a JSON file using GPT, which included complex nested data structures.
When attempting to process this file using Python-docx for document generation, I encountered repeated structural errors, including:
Recursive paragraph nesting
Duplicate content injection into the table of contents (TOC)
Overlapping and conflicting page numbering
Memory allocation failures due to internal reference loops
The problem persisted even when:
The content was simplified
The TOC was manually generated
Recursive references were explicitly removed
Root Cause Analysis:
The JSON file generated by GPT contains deep recursive structural patterns that Python-docx cannot properly parse.
It appears that GPT’s output includes hidden metadata or segment identifiers that Python-docx mistakenly recognizes as paragraph markers, causing recursive conflicts and memory overflow.
This leads to self-referencing issues where content becomes embedded in the TOC and creates cyclic formatting conflicts.
Impact:
Python-docx crashes or becomes unresponsive when processing GPT-generated files.
TOC generation fails due to cyclic references.
Content alignment and formatting become unstable.
Proposed Solution:
Review GPT’s JSON generation process to detect and prevent recursive metadata or segment markers.
Ensure that paragraph markers are uniquely identified and not inherited across nested structures.
Consider implementing a content sanitization step to remove hidden recursive patterns before exporting structured data.
Thank You for Your Attention
As a frequent and dedicated user of OpenAI’s products, I hope this feedback helps improve the platform’s robustness and enhance user experience. Please feel free to contact me if you need additional details or test data.
Hi!
I expect that you are using ChatGPT. If you are building on top of the API, you can look into structured outputs or maybe even JSON mode.
Also, it’s an option to look into the prompt you are using. Otherwise, it is a known limitation of language models to sometimes produce invalid JSON output. Programmatically, this can be dealt with, but since the introduction of structured outputs, we have seen only a few reports of issues with JSON generated by the models.
Thank you for your response. I appreciate the explanation. However, after using GPT for an extended period and accumulating a large number of project-based conversations, I’ve encountered a significant issue that greatly affects usability and workflow efficiency.
First, I want to clarify that I am a non-technical user. Despite this, I have been using GPT extensively to manage multiple projects, which makes this issue particularly disruptive for me.
Specifically, the inability to edit and organize past conversations creates confusion and reduces productivity, especially for users managing complex or ongoing projects. Over time, the accumulated content becomes increasingly disorganized, making it difficult to reference or retrieve relevant information effectively.
This is a critical limitation for users who rely on GPT for complex, long-term projects. The lack of conversation organization or the ability to categorize and edit past content leads to:
Increased difficulty in finding relevant information
Higher risk of redundant or conflicting content
Loss of efficiency in managing multi-project workflows
I strongly recommend implementing a feature that allows users to edit, merge, and organize conversations more effectively. This would significantly improve the experience for advanced and long-term users.
For non-technical users like myself, the ability to easily organize and manage past conversations would make GPT more intuitive and user-friendly, ultimately increasing efficiency and overall satisfaction.
Thank you for considering this suggestion. I believe it would be a valuable improvement for the GPT platform.
Issue Description:
Root Cause Analysis:
Impact:
Proposed Solution:
Thank You for Your Attention