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| #!/usr/bin/env python3 | |
| """ | |
| Web Search Tool for GAIA Agent System | |
| Handles web searches using DuckDuckGo (primary), Tavily API (secondary), and Wikipedia (fallback) | |
| """ | |
| import re | |
| import logging | |
| import time | |
| import os | |
| from typing import Dict, List, Optional, Any | |
| from urllib.parse import urlparse, urljoin | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from tools import BaseTool | |
| logger = logging.getLogger(__name__) | |
| class WebSearchResult: | |
| """Container for web search results""" | |
| def __init__(self, title: str, url: str, snippet: str, content: str = "", source: str = ""): | |
| self.title = title | |
| self.url = url | |
| self.snippet = snippet | |
| self.content = content | |
| self.source = source | |
| def to_dict(self) -> Dict[str, str]: | |
| return { | |
| "title": self.title, | |
| "url": self.url, | |
| "snippet": self.snippet, | |
| "content": self.content[:1500] + "..." if len(self.content) > 1500 else self.content, | |
| "source": self.source | |
| } | |
| class WebSearchTool(BaseTool): | |
| """ | |
| Web search tool using DuckDuckGo (primary), Tavily API (secondary), and Wikipedia (fallback) | |
| Provides multiple search engine options for reliability | |
| """ | |
| def __init__(self): | |
| super().__init__("web_search") | |
| # Configure requests session for web scraping | |
| self.session = requests.Session() | |
| self.session.headers.update({ | |
| 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36' | |
| }) | |
| self.session.timeout = 10 | |
| # Initialize search engines | |
| self.tavily_api_key = os.getenv("TAVILY_API_KEY") | |
| self.use_tavily = self.tavily_api_key is not None | |
| # Try to import DuckDuckGo | |
| try: | |
| from duckduckgo_search import DDGS | |
| self.ddgs = DDGS() | |
| self.use_duckduckgo = True | |
| logger.info("✅ DuckDuckGo search initialized") | |
| except ImportError: | |
| logger.warning("⚠️ DuckDuckGo search not available - install duckduckgo-search package") | |
| self.use_duckduckgo = False | |
| # Try to import Wikipedia | |
| try: | |
| import wikipedia | |
| self.wikipedia = wikipedia | |
| self.use_wikipedia = True | |
| logger.info("✅ Wikipedia search initialized") | |
| except ImportError: | |
| logger.warning("⚠️ Wikipedia search not available - install wikipedia package") | |
| self.use_wikipedia = False | |
| if self.use_tavily: | |
| logger.info("✅ Tavily API key found - using as secondary search") | |
| # Search engine priority: DuckDuckGo -> Tavily -> Wikipedia | |
| search_engines = [] | |
| if self.use_duckduckgo: | |
| search_engines.append("DuckDuckGo") | |
| if self.use_tavily: | |
| search_engines.append("Tavily") | |
| if self.use_wikipedia: | |
| search_engines.append("Wikipedia") | |
| logger.info(f"🔍 Available search engines: {', '.join(search_engines)}") | |
| def _execute_impl(self, input_data: Any, **kwargs) -> Dict[str, Any]: | |
| """ | |
| Execute web search operations based on input type | |
| Args: | |
| input_data: Can be: | |
| - str: Search query or URL to extract content from | |
| - dict: {"query": str, "action": str, "limit": int, "extract_content": bool} | |
| """ | |
| if isinstance(input_data, str): | |
| # Handle both search queries and URLs | |
| if self._is_url(input_data): | |
| return self._extract_content_from_url(input_data) | |
| else: | |
| return self._search_web(input_data) | |
| elif isinstance(input_data, dict): | |
| query = input_data.get("query", "") | |
| action = input_data.get("action", "search") | |
| limit = input_data.get("limit", 5) | |
| extract_content = input_data.get("extract_content", False) | |
| if action == "search": | |
| return self._search_web(query, limit, extract_content) | |
| elif action == "extract": | |
| return self._extract_content_from_url(query) | |
| else: | |
| raise ValueError(f"Unknown action: {action}") | |
| else: | |
| raise ValueError(f"Unsupported input type: {type(input_data)}") | |
| def _is_url(self, text: str) -> bool: | |
| """Check if text is a URL""" | |
| return bool(re.match(r'https?://', text)) | |
| def _extract_search_terms(self, query: str, max_length: int = 250) -> str: | |
| """ | |
| Extract key search terms from a potentially long query | |
| """ | |
| # If query is short enough, use as-is | |
| if len(query) <= max_length: | |
| return query | |
| # Remove common stop words and extract key terms | |
| stop_words = { | |
| 'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', | |
| 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did', | |
| 'will', 'would', 'could', 'should', 'may', 'might', 'must', 'can', 'this', 'that', 'these', 'those', | |
| 'i', 'you', 'he', 'she', 'it', 'we', 'they', 'me', 'him', 'her', 'us', 'them', | |
| 'what', 'where', 'when', 'why', 'how', 'which', 'who', 'whose', 'whom', | |
| 'please', 'could', 'you', 'tell', 'me', 'find', 'search', 'for', 'about' | |
| } | |
| # Split into words and filter | |
| words = re.findall(r'\b\w+\b', query.lower()) | |
| key_words = [word for word in words if word not in stop_words and len(word) > 2] | |
| # Keep important phrases and entities | |
| # Look for quoted phrases, proper nouns, numbers, dates | |
| important_patterns = [ | |
| r'"[^"]*"', # Quoted phrases | |
| r'\b[A-Z][a-z]*(?:\s+[A-Z][a-z]*)*\b', # Proper nouns | |
| r'\b\d{4}\b', # Years | |
| r'\b\d+\b', # Numbers | |
| ] | |
| important_terms = [] | |
| for pattern in important_patterns: | |
| matches = re.findall(pattern, query) | |
| important_terms.extend(matches) | |
| # Combine key words and important terms | |
| search_terms = [] | |
| # Add important terms first (they're usually more specific) | |
| for term in important_terms: | |
| if len(' '.join(search_terms + [term])) <= max_length: | |
| search_terms.append(term) | |
| # Add key words until we hit the limit | |
| for word in key_words: | |
| potential_query = ' '.join(search_terms + [word]) | |
| if len(potential_query) <= max_length: | |
| search_terms.append(word) | |
| else: | |
| break | |
| result = ' '.join(search_terms) | |
| # If still too long, truncate | |
| if len(result) > max_length: | |
| result = result[:max_length].rsplit(' ', 1)[0] | |
| # If we ended up with nothing, use first part of original query | |
| if not result.strip(): | |
| result = query[:max_length].rsplit(' ', 1)[0] | |
| if result != query: | |
| logger.info(f"📝 Extracted search terms: '{result}' from '{query[:100]}...'") | |
| return result | |
| def _search_web(self, query: str, limit: int = 5, extract_content: bool = False) -> Dict[str, Any]: | |
| """ | |
| Search the web using available search engines in priority order | |
| """ | |
| # Extract search terms to avoid length issues | |
| search_query = self._extract_search_terms(query, max_length=250) | |
| # Try DuckDuckGo first (most comprehensive for general web search) | |
| if self.use_duckduckgo: | |
| try: | |
| return self._search_with_duckduckgo(search_query, limit, extract_content) | |
| except Exception as e: | |
| logger.warning(f"DuckDuckGo search failed, trying Tavily: {e}") | |
| # Try Tavily if DuckDuckGo fails and API key is available | |
| if self.use_tavily: | |
| try: | |
| return self._search_with_tavily(search_query, limit, extract_content) | |
| except Exception as e: | |
| logger.warning(f"Tavily search failed, trying Wikipedia: {e}") | |
| # Fallback to Wikipedia search | |
| if self.use_wikipedia: | |
| return self._search_with_wikipedia(search_query, limit) | |
| # No search engines available | |
| return { | |
| "query": query, | |
| "found": False, | |
| "message": "❌ No search engines available. Please install required packages.", | |
| "results": [] | |
| } | |
| def _search_with_duckduckgo(self, query: str, limit: int = 5, extract_content: bool = False) -> Dict[str, Any]: | |
| """ | |
| Search using DuckDuckGo - primary search engine with improved error handling and rate limiting | |
| """ | |
| try: | |
| logger.info(f"🦆 DuckDuckGo search for: {query}") | |
| # Add small delay to avoid rate limiting | |
| time.sleep(0.5) | |
| # Use DuckDuckGo text search with retry logic | |
| max_retries = 2 | |
| for attempt in range(max_retries): | |
| try: | |
| ddg_results = list(self.ddgs.text(query, max_results=min(limit, 10))) | |
| break | |
| except Exception as retry_error: | |
| if attempt < max_retries - 1: | |
| logger.warning(f"DuckDuckGo attempt {attempt + 1} failed, retrying in {2 ** attempt}s: {retry_error}") | |
| time.sleep(2 ** attempt) # Exponential backoff | |
| continue | |
| else: | |
| raise retry_error | |
| if not ddg_results: | |
| logger.warning("DuckDuckGo returned no results") | |
| return self._search_with_fallback(query, limit) | |
| # Process DuckDuckGo results | |
| results = [] | |
| for result in ddg_results: | |
| web_result = WebSearchResult( | |
| title=result.get('title', 'No title'), | |
| url=result.get('href', ''), | |
| snippet=result.get('body', 'No description'), | |
| source='DuckDuckGo' | |
| ) | |
| results.append(web_result) | |
| logger.info(f"✅ DuckDuckGo found {len(results)} results") | |
| return { | |
| 'success': True, | |
| 'results': results, | |
| 'source': 'DuckDuckGo', | |
| 'query': query, | |
| 'count': len(results) | |
| } | |
| except Exception as e: | |
| logger.warning(f"DuckDuckGo search failed: {str(e)}") | |
| # Check if it's a rate limiting error and add longer delay | |
| if "ratelimit" in str(e).lower() or "429" in str(e) or "202" in str(e): | |
| logger.warning("Rate limiting detected, adding delay before fallback") | |
| time.sleep(2.0) | |
| return self._search_with_fallback(query, limit) | |
| def _search_with_fallback(self, query: str, limit: int = 5) -> Dict[str, Any]: | |
| """Enhanced fallback search when DuckDuckGo fails""" | |
| logger.info(f"🔄 Using fallback search engines for: {query}") | |
| # Try Tavily API first if available | |
| if hasattr(self, 'tavily') and self.tavily: | |
| try: | |
| logger.info("📡 Trying Tavily API search") | |
| tavily_result = self.tavily.search(query, max_results=limit) | |
| if tavily_result and 'results' in tavily_result: | |
| results = [] | |
| for result in tavily_result['results'][:limit]: | |
| web_result = WebSearchResult( | |
| title=result.get('title', 'No title'), | |
| url=result.get('url', ''), | |
| snippet=result.get('content', 'No description'), | |
| source='Tavily' | |
| ) | |
| results.append(web_result) | |
| if results: | |
| logger.info(f"✅ Tavily found {len(results)} results") | |
| return { | |
| 'success': True, | |
| 'results': results, | |
| 'source': 'Tavily', | |
| 'query': query, | |
| 'count': len(results) | |
| } | |
| except Exception as e: | |
| logger.warning(f"Tavily search failed: {str(e)}") | |
| # Fall back to Wikipedia search | |
| logger.info("📚 Wikipedia search for: " + query) | |
| try: | |
| wiki_results = self._search_with_wikipedia(query, limit) | |
| if wiki_results and wiki_results.get('success'): | |
| logger.info(f"✅ Wikipedia found {wiki_results.get('count', 0)} results") | |
| return wiki_results | |
| except Exception as e: | |
| logger.warning(f"Wikipedia fallback failed: {str(e)}") | |
| # Final fallback - return empty but successful result to allow processing to continue | |
| logger.warning("All search engines failed, returning empty results") | |
| return { | |
| 'success': True, | |
| 'results': [], | |
| 'source': 'none', | |
| 'query': query, | |
| 'count': 0, | |
| 'note': 'All search engines failed' | |
| } | |
| def _search_with_tavily(self, query: str, limit: int = 5, extract_content: bool = False) -> Dict[str, Any]: | |
| """ | |
| Search using Tavily Search API - secondary search engine | |
| """ | |
| try: | |
| logger.info(f"🔍 Tavily search for: {query}") | |
| # Prepare Tavily API request | |
| headers = { | |
| "Content-Type": "application/json" | |
| } | |
| payload = { | |
| "api_key": self.tavily_api_key, | |
| "query": query, | |
| "search_depth": "basic", | |
| "include_answer": False, | |
| "include_images": False, | |
| "include_raw_content": extract_content, | |
| "max_results": min(limit, 10) | |
| } | |
| # Make API request | |
| response = self.session.post( | |
| "https://api.tavily.com/search", | |
| json=payload, | |
| headers=headers, | |
| timeout=15 | |
| ) | |
| response.raise_for_status() | |
| tavily_data = response.json() | |
| # Process Tavily results | |
| results = [] | |
| tavily_results = tavily_data.get('results', []) | |
| for result in tavily_results: | |
| web_result = WebSearchResult( | |
| title=result.get('title', 'No title'), | |
| url=result.get('url', ''), | |
| snippet=result.get('content', 'No description'), | |
| content=result.get('raw_content', '') if extract_content else '' | |
| ) | |
| results.append(web_result) | |
| if results: | |
| logger.info(f"✅ Tavily found {len(results)} results") | |
| return { | |
| 'success': True, | |
| 'results': results, | |
| 'source': 'Tavily', | |
| 'query': query, | |
| 'count': len(results) | |
| } | |
| else: | |
| logger.warning("Tavily returned no results") | |
| # Fall back to Wikipedia | |
| if self.use_wikipedia: | |
| return self._search_with_wikipedia(query, limit) | |
| except requests.exceptions.RequestException as e: | |
| logger.error(f"Tavily API request failed: {e}") | |
| except Exception as e: | |
| logger.error(f"Tavily search error: {e}") | |
| # Fall back to Wikipedia if Tavily fails | |
| if self.use_wikipedia: | |
| return self._search_with_wikipedia(query, limit) | |
| return { | |
| 'success': False, | |
| 'results': [], | |
| 'source': 'Tavily', | |
| 'query': query, | |
| 'count': 0, | |
| 'note': 'Tavily search failed and no fallback available' | |
| } | |
| def _search_with_wikipedia(self, query: str, limit: int = 5) -> Dict[str, Any]: | |
| """ | |
| Search using Wikipedia - fallback search engine for factual information | |
| """ | |
| try: | |
| logger.info(f"📚 Wikipedia search for: {query}") | |
| self.wikipedia.set_lang("en") | |
| # Clean up query for Wikipedia search and ensure it's not too long | |
| search_terms = self._extract_search_terms(query, max_length=100) # Wikipedia has stricter limits | |
| # Search Wikipedia pages | |
| wiki_results = self.wikipedia.search(search_terms, results=min(limit * 2, 10)) | |
| if not wiki_results: | |
| return { | |
| 'success': False, | |
| 'results': [], | |
| 'source': 'Wikipedia', | |
| 'query': query, | |
| 'count': 0, | |
| 'note': 'No Wikipedia articles found for this query' | |
| } | |
| results = [] | |
| processed = 0 | |
| for page_title in wiki_results: | |
| if processed >= limit: | |
| break | |
| try: | |
| page = self.wikipedia.page(page_title) | |
| summary = page.summary[:300] + "..." if len(page.summary) > 300 else page.summary | |
| web_result = WebSearchResult( | |
| title=f"{page_title} (Wikipedia)", | |
| url=page.url, | |
| snippet=summary, | |
| content=page.summary[:1000] + "..." if len(page.summary) > 1000 else page.summary | |
| ) | |
| results.append(web_result) | |
| processed += 1 | |
| except self.wikipedia.exceptions.DisambiguationError as e: | |
| # Try the first suggestion from disambiguation | |
| try: | |
| if e.options: | |
| page = self.wikipedia.page(e.options[0]) | |
| summary = page.summary[:300] + "..." if len(page.summary) > 300 else page.summary | |
| web_result = WebSearchResult( | |
| title=f"{e.options[0]} (Wikipedia)", | |
| url=page.url, | |
| snippet=summary, | |
| content=page.summary[:1000] + "..." if len(page.summary) > 1000 else page.summary | |
| ) | |
| results.append(web_result) | |
| processed += 1 | |
| except: | |
| continue | |
| except self.wikipedia.exceptions.PageError: | |
| # Page doesn't exist, skip | |
| continue | |
| except Exception as e: | |
| # Other Wikipedia errors, skip this page | |
| logger.warning(f"Wikipedia page error for '{page_title}': {e}") | |
| continue | |
| if results: | |
| logger.info(f"✅ Wikipedia found {len(results)} results") | |
| return { | |
| 'success': True, | |
| 'results': results, | |
| 'source': 'Wikipedia', | |
| 'query': query, | |
| 'count': len(results) | |
| } | |
| else: | |
| return { | |
| 'success': False, | |
| 'results': [], | |
| 'source': 'Wikipedia', | |
| 'query': query, | |
| 'count': 0, | |
| 'note': 'No accessible Wikipedia articles found for this query' | |
| } | |
| except Exception as e: | |
| logger.error(f"Wikipedia search failed: {e}") | |
| return { | |
| 'success': False, | |
| 'results': [], | |
| 'source': 'Wikipedia', | |
| 'query': query, | |
| 'count': 0, | |
| 'note': f"Wikipedia search failed: {str(e)}" | |
| } | |
| def _extract_content_from_url(self, url: str) -> Dict[str, Any]: | |
| """ | |
| Extract readable content from a web page | |
| """ | |
| try: | |
| logger.info(f"Extracting content from: {url}") | |
| # Get page content | |
| response = self.session.get(url) | |
| response.raise_for_status() | |
| # Parse with BeautifulSoup | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| # Remove script and style elements | |
| for script in soup(["script", "style", "nav", "header", "footer", "aside"]): | |
| script.decompose() | |
| # Extract title | |
| title = soup.find('title') | |
| title_text = title.get_text().strip() if title else "No title" | |
| # Extract main content | |
| content = self._extract_main_content(soup) | |
| # Extract metadata | |
| meta_description = "" | |
| meta_desc = soup.find('meta', attrs={'name': 'description'}) | |
| if meta_desc: | |
| meta_description = meta_desc.get('content', '') | |
| # Extract links | |
| links = [] | |
| for link in soup.find_all('a', href=True)[:10]: # First 10 links | |
| link_url = urljoin(url, link['href']) | |
| link_text = link.get_text().strip() | |
| if link_text and len(link_text) > 5: # Filter out short/empty links | |
| links.append({"text": link_text, "url": link_url}) | |
| return { | |
| "url": url, | |
| "found": True, | |
| "title": title_text, | |
| "content": content, | |
| "meta_description": meta_description, | |
| "links": links, | |
| "content_length": len(content), | |
| "message": "Successfully extracted content from URL" | |
| } | |
| except requests.exceptions.RequestException as e: | |
| return { | |
| "url": url, | |
| "found": False, | |
| "message": f"Failed to fetch URL: {str(e)}", | |
| "error_type": "network_error" | |
| } | |
| except Exception as e: | |
| return { | |
| "url": url, | |
| "found": False, | |
| "message": f"Failed to extract content: {str(e)}", | |
| "error_type": "parsing_error" | |
| } | |
| def _extract_main_content(self, soup: BeautifulSoup) -> str: | |
| """ | |
| Extract main content from HTML using various strategies | |
| """ | |
| content_parts = [] | |
| # Strategy 1: Look for article/main tags | |
| main_content = soup.find(['article', 'main']) | |
| if main_content: | |
| content_parts.append(main_content.get_text()) | |
| # Strategy 2: Look for content in common div classes | |
| content_selectors = [ | |
| 'div.content', | |
| 'div.article-content', | |
| 'div.post-content', | |
| 'div.entry-content', | |
| 'div.main-content', | |
| 'div#content', | |
| 'div.text' | |
| ] | |
| for selector in content_selectors: | |
| elements = soup.select(selector) | |
| for element in elements: | |
| content_parts.append(element.get_text()) | |
| # Strategy 3: Look for paragraphs in body | |
| if not content_parts: | |
| paragraphs = soup.find_all('p') | |
| for p in paragraphs[:20]: # First 20 paragraphs | |
| text = p.get_text().strip() | |
| if len(text) > 50: # Filter out short paragraphs | |
| content_parts.append(text) | |
| # Clean and combine content | |
| combined_content = '\n\n'.join(content_parts) | |
| # Clean up whitespace and formatting | |
| combined_content = re.sub(r'\n\s*\n', '\n\n', combined_content) # Multiple newlines | |
| combined_content = re.sub(r' +', ' ', combined_content) # Multiple spaces | |
| return combined_content.strip()[:5000] # Limit to 5000 characters | |
| def test_web_search_tool(): | |
| """Test the web search tool with various queries""" | |
| tool = WebSearchTool() | |
| # Test cases | |
| test_cases = [ | |
| "Python programming tutorial", | |
| "Mercedes Sosa studio albums 2000 2009", | |
| "artificial intelligence recent developments", | |
| "climate change latest research", | |
| "https://en.wikipedia.org/wiki/Machine_learning" | |
| ] | |
| print("🧪 Testing Web Search Tool...") | |
| for i, test_case in enumerate(test_cases, 1): | |
| print(f"\n--- Test {i}: {test_case} ---") | |
| try: | |
| result = tool.execute(test_case) | |
| if result.success: | |
| print(f"✅ Success: {result.result.get('message', 'No message')}") | |
| search_engine = result.result.get('source', 'unknown') | |
| print(f" Search engine: {search_engine}") | |
| if result.result.get('found'): | |
| if 'results' in result.result: | |
| print(f" Found {len(result.result['results'])} results") | |
| # Show first result details | |
| if result.result['results']: | |
| first_result = result.result['results'][0] | |
| print(f" First result: {first_result.get('title', 'No title')}") | |
| print(f" URL: {first_result.get('url', 'No URL')}") | |
| elif 'content' in result.result: | |
| print(f" Extracted {len(result.result['content'])} characters") | |
| print(f" Title: {result.result.get('title', 'No title')}") | |
| else: | |
| print(f" Not found: {result.result.get('message', 'Unknown error')}") | |
| else: | |
| print(f"❌ Error: {result.error}") | |
| print(f" Execution time: {result.execution_time:.2f}s") | |
| except Exception as e: | |
| print(f"❌ Exception: {str(e)}") | |
| if __name__ == "__main__": | |
| # Test when run directly | |
| test_web_search_tool() |