Implementation:Openai Openai python Validators Framework

Knowledge Sources	openai-python OpenAI Fine-tuning
Domains	Data_Engineering, Fine_Tuning
Last Updated	2026-02-15 00:00 GMT

Overview

Concrete validation framework for preparing and validating fine-tuning training data provided by the OpenAI Python SDK.

Description

The _validators module provides a comprehensive data validation framework with functions for format checking, duplicate detection, length analysis, and interactive remediation. The key entry point is the CLI command openai tools fine_tunes.prepare_data which guides users through validation and fixes. Programmatic access is also available via the write_out_file() function.

Usage

Use the CLI command for interactive validation or import individual validator functions for programmatic use.

Code Reference

Source Location

Repository: openai-python
File: src/openai/lib/_validators.py
Lines: L1-809

Signature

def write_out_file(
    fname: str,
    prompts: list,
    completions: list,
) -> None:
    """Writes validated prompt-completion pairs to a JSONL file."""

# Validator functions (internal):
# - check_format(data) -> issues
# - check_duplicates(data) -> issues
# - check_completion_length(data) -> issues
# - check_prompt_length(data) -> issues
# - check_common_prefix(data) -> issues
# - check_common_suffix(data) -> issues
# - check_whitespace(data) -> issues

Import

from openai.lib._validators import write_out_file
# Or use CLI: openai tools fine_tunes.prepare_data

I/O Contract

Inputs

Name	Type	Required	Description
file	JSONL file	Yes	Training data with prompt-completion pairs or chat messages

Outputs

Name	Type	Description
validated_file	JSONL file	Cleaned and validated training data
validation_report	Console output	Issues found and remediations applied

Usage Examples

CLI Validation

# Command line usage:
# openai tools fine_tunes.prepare_data -f training_data.jsonl

Training Data Format

# Chat format (modern - for GPT-3.5/GPT-4 fine-tuning)
{"messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is Python?"},
    {"role": "assistant", "content": "Python is a programming language."}
]}

# Legacy format (prompt-completion pairs)
{"prompt": "What is Python? ->", "completion": " Python is a programming language.\n"}

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