Principle:Openai Openai node Training Data Upload
| Knowledge Sources | |
|---|---|
| Domains | Fine_Tuning, Data_Preparation |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
A principle for uploading structured training data files to the OpenAI platform as a prerequisite for model fine-tuning.
Description
Training Data Upload is the first step in the fine-tuning pipeline. Training data must be formatted as JSONL (JSON Lines), where each line is a training example with a messages array following the chat format. The file is uploaded via multipart form POST to the Files API with purpose: 'fine-tune' .
The upload process handles cross-platform file construction (Node.js streams, browser blobs, Bun file handles) through the SDK's toFile() utility, and uses multipart form encoding internally.
Usage
Use this principle when preparing to fine-tune an OpenAI model. Upload must complete and the file must reach processed status before creating a fine-tuning job.
Theoretical Basis
Training data upload follows a Stage-then-Reference pattern:
// 1. Prepare JSONL training file
// Each line: {"messages": [{"role": "system", ...}, {"role": "user", ...}, {"role": "assistant", ...}]}
// 2. Upload file via multipart form
fileObject = await files.create({
file: trainingFile,
purpose: 'fine-tune',
})
// 3. File enters server-side processing pipeline
// Status: uploaded → processing → processed (or error)
// 4. Use fileObject.id as reference in fine-tuning job creation