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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")]
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