| import pandas as pd | |
| from openai import OpenAI | |
| import os | |
| import dotenv | |
| import time | |
| from tqdm import tqdm | |
| dotenv.load_dotenv() | |
| client = OpenAI( | |
| api_key=os.environ.get("OPENAI_API_KEY") | |
| ) | |
| def create_prompt(row): | |
| impacts = [] | |
| if row['Infrastructural Impact'] > 0: | |
| impacts.append('infrastructure') | |
| if row['Political Impact'] > 0: | |
| impacts.append('political') | |
| if row['Economic Impact'] > 0: | |
| impacts.append('economic') | |
| if row['Ecological Impact'] > 0: | |
| impacts.append('ecological') | |
| if row['Agricultural Impact'] > 0: | |
| impacts.append('agricultural') | |
| if row['Human Health Impact'] > 0: | |
| impacts.append('human health') | |
| impact_str = ', '.join(impacts) if impacts else 'general' | |
| prompt = f"""Given the following passage about {row['Weather']}, generate a specific question that: | |
| 1. Can be answered using ONLY the information in this passage | |
| 2. Focuses on the {impact_str} impacts mentioned | |
| 3. Is detailed and specific to this exact situation | |
| 4. Requires understanding the passage's unique context | |
| 5. Cannot be answered by other similar passages about {row['Weather']} | |
| Passage: | |
| {row['Text']} | |
| Generate a single, focused question that meets these criteria.""" | |
| return prompt | |
| def generate_query(prompt, max_retries=3): | |
| """Generate a query using GPT-4 with retry logic.""" | |
| for attempt in range(max_retries): | |
| try: | |
| response = client.chat.completions.create( | |
| model="gpt-4o", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant that generates specific, focused questions about weather-related passages. Your questions should be answerable using only the information in the given passage."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| temperature=0.7, | |
| max_tokens=400 | |
| ) | |
| return response.choices[0].message.content.strip() | |
| except Exception as e: | |
| if attempt == max_retries - 1: | |
| print(f"Error after {max_retries} attempts: {e}") | |
| return "Error generating query" | |
| time.sleep(5) | |
| df = pd.read_csv('datasets/context_data/reranking_passage.csv') | |
| df['Generated_Query'] = '' | |
| for idx in tqdm(df.index): | |
| if df.loc[idx, 'Remove'] == 0: | |
| prompt = create_prompt(df.loc[idx]) | |
| query = generate_query(prompt) | |
| df.loc[idx, 'Generated_Query'] = query | |
| time.sleep(1) | |
| output_file = 'reranking_passage_with_queries.csv' | |
| df.to_csv(output_file, index=False) | |
| print(f"Results saved to {output_file}") | |