| import pandas as pd | |
| df = pd.read_csv('Output/summary/eth_md_summary.csv') | |
| def truncate_content(content, max_tokens=7000): | |
| words = content.split() | |
| return ' '.join(words[:max_tokens]) | |
| df['Content'] = df['Content'].apply(lambda x: truncate_content(x)) | |
| df['Summary and Q&A'] = df['Summary and Q&A'].apply(lambda x: truncate_content(x)) | |
| df.to_csv('Output/summary/eth_md_summary_trun.csv', index=False) | |
| ''' | |
| import pandas as pd | |
| df = pd.read_csv('input_file.csv') | |
| def split_content(row, max_words=5000): | |
| content = row['Content'] | |
| words = content.split() | |
| chunks = [words[i:i + max_words] for i in range(0, len(words), max_words)] | |
| return [{'Path': row['Path'], 'Content': ' '.join(chunk)} for chunk in chunks] | |
| new_rows = [] | |
| for index, row in df.iterrows(): | |
| new_rows.extend(split_content(row)) | |
| new_df = pd.DataFrame(new_rows) | |
| new_df.to_csv('output_file.csv', index=False) | |
| ''' |