I’ve got this huge Figma JSON file with about 6000 lines of nested data. I need to break it down into smaller parts, like 200 lines each. Then I want to feed these parts to a GPT model with a prompt.
The tricky part is I’m not sure how to split it up so each chunk is still valid JSON. Any ideas on how to do this smartly?
Here’s a bit of what the JSON looks like:
{
"projectName": "UI Dashboard",
"lastUpdated": "2023-08-03T15:30:00Z",
"previewImage": "dashboard-preview.jpg",
"versionNumber": "1.2.3",
"userAccess": "editor",
"components": {
"header": {
"id": "comp001",
"type": "FRAME",
"children": [
{
"id": "elem001",
"name": "Logo",
"type": "IMAGE"
}
]
}
}
}
This goes on for thousands more lines. Any tips on splitting it up would be super helpful!
Having worked extensively with large JSON files from design tools, I’d recommend a streaming approach combined with a depth-aware parser. This method allows you to process the file incrementally without loading it all into memory.
Implement a parser that tracks the JSON structure depth as it reads. When you reach close to 200 lines, find the next logical breakpoint (end of an object or array) and split there. Ensure each chunk starts and ends with proper nesting to maintain validity.
For very large nested objects, consider implementing a reference system where you can split mid-object and include a reference to continue in the next chunk. This maintains context across segments.
Remember to handle edge cases like long string values that might exceed your line limit. With some fine-tuning, this approach should give you manageable, valid JSON segments suitable for GPT processing.
I have encountered a similar issue with large JSON files. In my experience, converting the file into a nested tree structure is a solid starting point. Once in that format, you can traverse the tree and count the size in terms of lines or characters. When you reach around 200 lines, you can slice out the current segment and ensure you wrap it with the necessary contextual keys so the JSON remains valid. It is also essential to manage cases where a single element may be too large for these segments, often requiring custom handling to keep the data consistent.
hey, ive dealt with this before. one trick is to use a json parser library (like json in python) to load the whole thing, then recursively go thru the structure. when u hit bout 200 lines, cut it off there and wrap it in {} to keep it valid. might need to tweak a bit to handle nested stuff, but its a good start. good luck!
I’ve tackled similar challenges with massive JSON files from Figma. Here’s what worked for me:
First, I used a JSON streaming parser to read the file chunk by chunk. This avoids loading the entire file into memory at once. Then, I implemented a custom splitter function that tracks the nesting level and object boundaries.
As it processes the JSON, it builds up each segment until hitting the 200-line threshold. At that point, it closes any open objects/arrays to ensure valid JSON, then starts a new segment.
One caveat - sometimes you’ll get segments that aren’t perfectly balanced in terms of content. But in my experience, GPT models are pretty forgiving as long as the JSON itself is valid.
I’ve found this approach to be memory-efficient and reasonably fast, even with files containing tens of thousands of lines. Hope this helps with your project!