from astroquery.nasa_ads import ADS import re import logging import os def extract_keywords_with_gpt(context, client, max_tokens=100, temperature=0.3): keyword_prompt = f"Extract 3 most important scientific keywords from the following user query:\n\n{context}" response = client.chat.completions.create( model="gpt-4", messages=[ {"role": "system", "content": "You are an expert in identifying key scientific terms and concepts."}, {"role": "user", "content": keyword_prompt} ], max_tokens=max_tokens, temperature=temperature ) extracted_keywords = response.choices[0].message.content.strip() cleaned_keywords = re.sub(r'\d+\.\s*', '', extracted_keywords) keywords_list = [kw.strip() for kw in cleaned_keywords.split("\n") if kw.strip()] return keywords_list def fetch_nasa_ads_references(ads_query): """Fetch relevant NASA ADS papers and format them for readability.""" ADS.TOKEN = os.getenv('ADS_API_KEY') try: # Query NASA ADS for relevant papers papers = ADS.query_simple(ads_query) if not papers or len(papers) == 0: return [("No results found", "N/A", "N/A", "N/A", "N/A", "N/A")] # Include authors in the references references = [] for paper in papers[:5]: # Limit to 5 references title = paper.get('title', ['Title not available'])[0] abstract = paper.get('abstract', 'Abstract not available') authors = ", ".join(paper.get('author', [])[:3]) + (" et al." if len(paper.get('author', [])) > 3 else "") bibcode = paper.get('bibcode', 'N/A') pub = paper.get('pub', 'Unknown Journal') pubdate = paper.get('pubdate', 'Unknown Date') references.append((title, abstract, authors, bibcode, pub, pubdate)) return references except Exception as e: logging.error(f"Error fetching ADS references: {str(e)}") return [("Error fetching references", "See logs for details", "N/A", "N/A", "N/A", "N/A")]