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