| 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}") |
|
|
|
|
