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| import urllib.request | |
| import xml.etree.ElementTree as ET | |
| from datetime import datetime, timedelta | |
| import json | |
| import os | |
| from typing import List, Dict | |
| from smolagents import Tool | |
| class ArxivSearchTool(Tool): | |
| name = "search_arxiv" | |
| description = "Search ArXiv for papers matching the query" | |
| input_types = {"query": str, "max_results": int} | |
| output_type = List[Dict] | |
| def __call__(self, query: str = "artificial intelligence", | |
| max_results: int = 50) -> List[Dict]: | |
| """Search ArXiv using their API. | |
| Args: | |
| query: Search query string | |
| max_results: Maximum number of results to return | |
| Returns: | |
| List[Dict]: List of paper results with metadata | |
| """ | |
| try: | |
| # Construct the API URL | |
| base_url = 'http://export.arxiv.org/api/query?' | |
| query_params = { | |
| 'search_query': query, | |
| 'start': 0, | |
| 'max_results': max_results | |
| } | |
| # Create the full URL | |
| url = base_url + urllib.parse.urlencode(query_params) | |
| # Make the request | |
| response = urllib.request.urlopen(url) | |
| data = response.read().decode('utf-8') | |
| # Parse the Atom XML response | |
| root = ET.fromstring(data) | |
| # Define the Atom namespace | |
| ns = {'atom': 'http://www.w3.org/2005/Atom', | |
| 'arxiv': 'http://arxiv.org/schemas/atom'} | |
| results = [] | |
| for entry in root.findall('atom:entry', ns): | |
| # Extract paper details | |
| result = { | |
| 'title': entry.find('atom:title', ns).text.strip(), | |
| 'authors': [author.find('atom:name', ns).text | |
| for author in entry.findall('atom:author', ns)], | |
| 'summary': entry.find('atom:summary', ns).text.strip() if entry.find('atom:summary', ns) is not None else '', | |
| 'published': entry.find('atom:published', ns).text.strip(), | |
| 'id': entry.find('atom:id', ns).text.strip(), | |
| 'pdf_url': next((link.get('href') for link in entry.findall('atom:link', ns) | |
| if link.get('type') == 'application/pdf'), None), | |
| 'categories': [cat.get('term') for cat in entry.findall('atom:category', ns)] | |
| } | |
| results.append(result) | |
| return results | |
| except Exception as e: | |
| return [{"error": f"Error searching ArXiv: {str(e)}"}] | |
| class LatestPapersTool(Tool): | |
| name = "get_latest_papers" | |
| description = "Get papers from the last N days from saved results" | |
| input_types = {"days_back": int} | |
| output_type = List[Dict] | |
| def __call__(self, days_back: int = 1) -> List[Dict]: | |
| papers = [] | |
| base_dir = "daily_papers" | |
| # Get dates to check | |
| dates = [ | |
| (datetime.now() - timedelta(days=i)).strftime("%Y-%m-%d") | |
| for i in range(days_back) | |
| ] | |
| # Load papers for each date | |
| for date in dates: | |
| file_path = os.path.join(base_dir, f"ai_papers_{date}.json") | |
| if os.path.exists(file_path): | |
| with open(file_path, 'r', encoding='utf-8') as f: | |
| day_papers = json.load(f) | |
| papers.extend(day_papers) | |
| return papers | |
| def save_daily_papers(output_dir: str = "daily_papers") -> List[Dict]: | |
| """Helper function to save daily papers - not exposed as a tool""" | |
| os.makedirs(output_dir, exist_ok=True) | |
| today = datetime.now().strftime("%Y-%m-%d") | |
| arxiv_tool = ArxivSearchTool() | |
| papers = arxiv_tool( | |
| query='cat:cs.AI OR cat:cs.LG OR cat:cs.CL OR "artificial intelligence"', | |
| max_results=100 | |
| ) | |
| # Filter for papers published today | |
| today_papers = [ | |
| paper for paper in papers | |
| if paper.get('published', '').startswith(today) | |
| ] | |
| output_file = os.path.join(output_dir, f"ai_papers_{today}.json") | |
| with open(output_file, 'w', encoding='utf-8') as f: | |
| json.dump(today_papers, f, indent=2) | |
| return today_papers |