Web Search Scraper
The Web Search Scraper component combines search capabilities with web scraping functionality to find and extract content from multiple websites. It enables automated search-driven data collection, allowing for gathering specific information across various web sources.
Web Search Scraper interface
Component Inputs
- Language Model: AI model for processing search queries and results
Determines how search is interpreted and results are processed
- Search Query: The text query to search for
Required search term to be processed
- URLs (comma separated): Specific URLs to scrape
Optional list of specific websites to target
- Max Results: Maximum number of results to return
Limits the final quantity of processed results
Component Outputs
- Scraping and Search Results: Combined search and scraped content data
Use Cases
- Research Automation: Gather information from multiple websites on specific topics
- Content Aggregation: Collect content from various sources based on search criteria
- Competitive Intelligence: Research competitor websites and content systematically
- Industry Monitoring: Keep track of industry news and updates across multiple sources
- Product Research: Gather information about products from various retailers
- Knowledge Base Creation: Build comprehensive knowledge bases from web sources
Best Practices
- Use specific, targeted search queries for better relevance
- Respect robots.txt directives and website terms of service
- Apply appropriate rate limiting to avoid overloading servers
- Use blacklist/whitelist features to focus on relevant content
- Enable readability processing for cleaner content extraction
- Set reasonable request timeouts to handle slow-responding sites
- Limit depth and max results to manage processing time and resource usage
- Consider legal and ethical implications of web scraping activities
- Implement error handling for failed searches or scraping attempts