#!/usr/bin/env python3 """ Script to normalize JSONL entries for LLM fine-tuning. Filters out entries without text and normalizes content using OpenAI-compatible API. """ import json import logging import os import sys from pathlib import Path from typing import Dict, Any, Optional import time import argparse try: import openai except ImportError: print("Error: openai package not found. Install with: pip install openai") sys.exit(1) # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler('normalize_log.txt'), logging.StreamHandler() ] ) logger = logging.getLogger(__name__) class JSONLNormalizer: def __init__(self, api_key: str, base_url: str = None, model: str = "gpt-3.5-turbo"): """ Initialize the normalizer with OpenAI-compatible API settings. Args: api_key: API key for the service base_url: Base URL for API (optional, defaults to OpenAI) model: Model name to use for normalization """ self.client = openai.OpenAI( api_key=api_key, base_url=base_url ) self.model = model self.processed_count = 0 self.skipped_count = 0 self.failed_count = 0 self.already_normalized_count = 0 def normalize_text(self, text: str, title: str = "", subtitle: str = "") -> Optional[str]: """ Normalize text content using the API. Args: text: Main text content to normalize title: Article title for context subtitle: Article subtitle for context Returns: Normalized text or None if normalization fails """ try: system_prompt = """You are an expert text editor helping to prepare content for LLM fine-tuning. Your task is to normalize and clean text while preserving its meaning and literary quality. Make these improvements: 1. Fix obvious typos and spelling errors 2. Normalize punctuation and spacing inconsistencies 3. Remove excessive whitespace and newlines (but preserve intentional line breaks for poetry/paragraphs) 4. Ensure proper capitalization 5. Fix encoding issues or strange characters 6. Maintain the original style and voice 7. Preserve intentional formatting (like poetry line breaks) 8. Remove any metadata or non-content text Return ONLY the cleaned text, nothing else.""" user_prompt = f"""Title: {title} Subtitle: {subtitle} Text to normalize: {text}""" response = self.client.chat.completions.create( model=self.model, messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ], temperature=0.1, max_tokens=4000 ) normalized_text = response.choices[0].message.content.strip() return normalized_text except Exception as e: logger.error(f"API normalization failed: {str(e)}") return None def is_valid_entry(self, entry: Dict[Any, Any]) -> bool: """ Check if entry has valid text content. Args: entry: JSONL entry dictionary Returns: True if entry has non-empty text field """ text = entry.get('text', '') return isinstance(text, str) and text.strip() != '' def is_already_normalized(self, entry: Dict[Any, Any]) -> bool: """ Check if entry has already been normalized. Args: entry: JSONL entry dictionary Returns: True if entry has already been normalized """ return entry.get('_normalized', False) or entry.get('_normalization_failed', False) def process_jsonl(self, input_file: str, output_file: str, failed_file: str, max_entries: Optional[int] = None, delay: float = 0.5, force_reprocess: bool = False, append: bool = False): """ Process the JSONL file and normalize entries. Args: input_file: Path to input JSONL file output_file: Path to output normalized JSONL file failed_file: Path to file for failed normalizations max_entries: Maximum number of entries to process (for testing) delay: Delay between API calls to avoid rate limits force_reprocess: If True, reprocess already normalized entries append: If True, append to existing output files instead of overwriting them """ logger.info(f"Starting normalization of {input_file}") logger.info(f"Output file: {output_file} (mode: {'append' if append else 'overwrite'})") logger.info(f"Failed entries file: {failed_file} (mode: {'append' if append else 'overwrite'})") if force_reprocess: logger.info("Force reprocess enabled - will reprocess already normalized entries") # Determine file modes based on append flag output_mode = 'a' if append else 'w' failed_mode = 'a' if append else 'w' with open(input_file, 'r', encoding='utf-8') as infile, \ open(output_file, output_mode, encoding='utf-8') as outfile, \ open(failed_file, failed_mode, encoding='utf-8') as failfile: for line_num, line in enumerate(infile, 1): try: # Parse JSON line entry = json.loads(line.strip()) # Skip entries without valid text if not self.is_valid_entry(entry): logger.debug(f"Line {line_num}: Skipping entry without text") self.skipped_count += 1 continue # Check if already normalized or failed (unless forcing reprocess) if not force_reprocess and self.is_already_normalized(entry): title = entry.get('title', '') logger.debug(f"Line {line_num}: Entry '{title[:50]}...' already processed") # Write to appropriate file based on previous result if entry.get('_normalized', False): outfile.write(json.dumps(entry, ensure_ascii=False) + '\n') elif entry.get('_normalization_failed', False): failfile.write(json.dumps(entry, ensure_ascii=False) + '\n') self.already_normalized_count += 1 # Check max_entries limit after counting already normalized entries if max_entries and (self.processed_count + self.already_normalized_count) >= max_entries: logger.info(f"Reached maximum entries limit: {max_entries}") break continue # Check max_entries limit before processing new entries if max_entries and (self.processed_count + self.already_normalized_count) >= max_entries: logger.info(f"Reached maximum entries limit: {max_entries}") break # Extract content for normalization original_text = entry['text'] title = entry.get('title', '') subtitle = entry.get('subtitle', '') logger.info(f"Line {line_num}: Normalizing entry '{title[:50]}...'") # Normalize the text normalized_text = self.normalize_text(original_text, title, subtitle) if normalized_text: # Update entry with normalized text entry['text'] = normalized_text entry['_original_length'] = len(original_text) entry['_normalized_length'] = len(normalized_text) entry['_normalized'] = True # Write to output file outfile.write(json.dumps(entry, ensure_ascii=False) + '\n') self.processed_count += 1 logger.info(f"Line {line_num}: Successfully normalized") else: # Write failed entry to failed file entry['_normalization_failed'] = True failfile.write(json.dumps(entry, ensure_ascii=False) + '\n') self.failed_count += 1 logger.warning(f"Line {line_num}: Normalization failed") # Rate limiting delay (only for new API calls) if delay > 0: time.sleep(delay) except json.JSONDecodeError as e: logger.error(f"Line {line_num}: JSON decode error: {str(e)}") self.failed_count += 1 except Exception as e: logger.error(f"Line {line_num}: Unexpected error: {str(e)}") self.failed_count += 1 # Progress update if line_num % 10 == 0: total_processed = self.processed_count + self.already_normalized_count logger.info(f"Progress: Processed {line_num} lines, " f"Total processed: {total_processed}, " f"Newly normalized: {self.processed_count}, " f"Already processed: {self.already_normalized_count}, " f"Skipped: {self.skipped_count}, " f"Failed: {self.failed_count}") # Final summary total_processed = self.processed_count + self.already_normalized_count logger.info("=" * 50) logger.info("NORMALIZATION COMPLETE") logger.info(f"Total lines processed: {line_num}") logger.info(f"Total entries processed: {total_processed}") logger.info(f"Newly normalized: {self.processed_count}") logger.info(f"Already processed (skipped): {self.already_normalized_count}") logger.info(f"Skipped (no text): {self.skipped_count}") logger.info(f"Failed: {self.failed_count}") logger.info("=" * 50) def main(): parser = argparse.ArgumentParser(description='Normalize JSONL entries for LLM fine-tuning') parser.add_argument('input_file', help='Input JSONL file path') parser.add_argument('-o', '--output', default='normalized_entries.jsonl', help='Output file for normalized entries (default: normalized_entries.jsonl)') parser.add_argument('-f', '--failed', default='failed_normalizations.jsonl', help='Output file for failed entries (default: failed_normalizations.jsonl)') parser.add_argument('-k', '--api-key', help='OpenAI API key (or set OPENAI_API_KEY env var)') parser.add_argument('-u', '--base-url', help='Base URL for OpenAI-compatible API') parser.add_argument('-m', '--model', default='gpt-3.5-turbo', help='Model to use (default: gpt-3.5-turbo)') parser.add_argument('--max-entries', type=int, help='Maximum entries to process (for testing)') parser.add_argument('--delay', type=float, default=0.5, help='Delay between API calls in seconds (default: 0.5)') parser.add_argument('--force-reprocess', action='store_true', help='Force reprocessing of already normalized entries') parser.add_argument('--append', action='store_true', help='Append to existing output files instead of overwriting them') args = parser.parse_args() # Get API key api_key = args.api_key or os.getenv('OPENAI_API_KEY') if not api_key: logger.error("API key required. Use --api-key or set OPENAI_API_KEY environment variable") sys.exit(1) # Check input file exists if not Path(args.input_file).exists(): logger.error(f"Input file not found: {args.input_file}") sys.exit(1) # Initialize normalizer normalizer = JSONLNormalizer( api_key=api_key, base_url=args.base_url, model=args.model ) # Process the file try: normalizer.process_jsonl( input_file=args.input_file, output_file=args.output, failed_file=args.failed, max_entries=args.max_entries, delay=args.delay, force_reprocess=args.force_reprocess, append=args.append ) except KeyboardInterrupt: logger.info("Process interrupted by user") except Exception as e: logger.error(f"Process failed: {str(e)}") sys.exit(1) if __name__ == "__main__": main()