Web Scraping Crew

Agents equipped with web scraping tools for data collection, analysis, and automated information gathering

Web Scraping Crew

This example demonstrates a comprehensive web scraping system using RCrewAI agents equipped with specialized scraping tools for data collection, analysis, and automated information gathering. The system handles multi-site scraping, data validation, rate limiting, and ethical scraping practices.

Overview

Our web scraping team includes:

• Web Scraper - Multi-site data collection and extraction
• Data Validator - Quality control and data validation
• Content Analyzer - Text processing and content analysis
• Rate Limiter - Traffic management and ethical scraping
• Data Processor - Data transformation and structuring
• Scraping Coordinator - Strategic oversight and workflow management

Complete Implementation

require 'rcrewai'
require 'nokogiri'
require 'open-uri'
require 'json'
require 'csv'

# Configure RCrewAI for web scraping
RCrewAI.configure do |config|
  config.llm_provider = :openai
  config.temperature = 0.3  # Lower temperature for precise data extraction
end

# ===== WEB SCRAPING TOOLS =====

# Web Scraping Tool
class WebScrapingTool < RCrewAI::Tools::Base
  def initialize(**options)
    super
    @name = 'web_scraper'
    @description = 'Extract data from web pages with rate limiting and error handling'
    @scraped_data = {}
    @request_history = []
    @rate_limit_delay = options[:delay] || 1.0
  end
  
  def execute(**params)
    action = params[:action]
    
    case action
    when 'scrape_url'
      scrape_single_url(params[:url], params[:selectors], params[:options])
    when 'scrape_multiple'
      scrape_multiple_urls(params[:urls], params[:selectors])
    when 'extract_links'
      extract_all_links(params[:url], params[:filters])
    when 'scrape_table'
      scrape_html_table(params[:url], params[:table_selector])
    when 'check_robots'
      check_robots_txt(params[:domain])
    else
      "Web scraper: Unknown action #{action}"
    end
  end
  
  private
  
  def scrape_single_url(url, selectors, options = {})
    # Simulate web scraping with rate limiting
    respect_rate_limit
    
    begin
      # Simulate HTTP request and parsing
      scraped_content = {
        url: url,
        timestamp: Time.now,
        status_code: 200,
        response_time: "1.2s",
        data: extract_with_selectors(selectors),
        metadata: {
          title: "Sample Page Title",
          description: "Sample page description",
          keywords: ["web scraping", "data extraction"],
          last_modified: "2024-01-15T10:30:00Z"
        }
      }
      
      @scraped_data[url] = scraped_content
      log_request(url, 200)
      
      scraped_content.to_json
      
    rescue StandardError => e
      log_request(url, 500, e.message)
      handle_scraping_error(url, e)
    end
  end
  
  def extract_with_selectors(selectors)
    # Simulate CSS selector-based extraction
    extracted_data = {}
    
    selectors.each do |key, selector|
      case key
      when 'title'
        extracted_data[key] = "Sample Article Title: Advanced Web Scraping Techniques"
      when 'content'
        extracted_data[key] = "Comprehensive content about web scraping methodologies and best practices for data extraction..."
      when 'author'
        extracted_data[key] = "Dr. Jane Smith, Data Science Expert"
      when 'date'
        extracted_data[key] = "2024-01-15"
      when 'tags'
        extracted_data[key] = ["web scraping", "data mining", "automation", "python"]
      when 'links'
        extracted_data[key] = [
          { text: "Related Article 1", href: "/article-1" },
          { text: "Related Article 2", href: "/article-2" }
        ]
      else
        extracted_data[key] = "Extracted content for #{key}"
      end
    end
    
    extracted_data
  end
  
  def scrape_multiple_urls(urls, selectors)
    # Simulate batch scraping with progress tracking
    results = []
    total_urls = urls.length
    
    urls.each_with_index do |url, index|
      progress = ((index + 1).to_f / total_urls * 100).round(1)
      
      result = JSON.parse(scrape_single_url(url, selectors))
      result['progress'] = "#{progress}%"
      results << result
      
      # Respect rate limiting between requests
      sleep(@rate_limit_delay) if index < urls.length - 1
    end
    
    {
      total_urls: total_urls,
      successful_scrapes: results.count { |r| r['status_code'] == 200 },
      failed_scrapes: results.count { |r| r['status_code'] != 200 },
      results: results,
      total_processing_time: "#{total_urls * 1.5}s",
      average_response_time: "1.2s"
    }.to_json
  end
  
  def extract_all_links(url, filters = {})
    # Simulate link extraction
    respect_rate_limit
    
    {
      source_url: url,
      total_links_found: 45,
      internal_links: 28,
      external_links: 17,
      filtered_links: apply_link_filters(filters),
      link_analysis: {
        domain_distribution: {
          "example.com" => 28,
          "external1.com" => 8,
          "external2.com" => 9
        },
        link_types: {
          "article" => 22,
          "category" => 12,
          "external" => 11
        }
      }
    }.to_json
  end
  
  def apply_link_filters(filters)
    # Simulate link filtering
    sample_links = [
      { url: "/technology/ai-automation", text: "AI Automation Guide", type: "internal" },
      { url: "/business/digital-transformation", text: "Digital Transformation", type: "internal" },
      { url: "https://external.com/research", text: "External Research", type: "external" }
    ]
    
    if filters[:internal_only]
      sample_links.select { |link| link[:type] == "internal" }
    else
      sample_links
    end
  end
  
  def check_robots_txt(domain)
    # Simulate robots.txt checking
    {
      domain: domain,
      robots_txt_exists: true,
      crawl_delay: 1.0,
      allowed_paths: ["/", "/articles/", "/blog/"],
      disallowed_paths: ["/admin/", "/private/", "/api/"],
      user_agent_rules: {
        "*" => "Crawl-delay: 1",
        "Googlebot" => "Crawl-delay: 0.5"
      },
      sitemap_urls: ["#{domain}/sitemap.xml", "#{domain}/sitemap-articles.xml"]
    }.to_json
  end
  
  def respect_rate_limit
    last_request = @request_history.last
    if last_request && (Time.now - last_request[:timestamp]) < @rate_limit_delay
      sleep(@rate_limit_delay)
    end
  end
  
  def log_request(url, status_code, error = nil)
    @request_history << {
      url: url,
      status_code: status_code,
      timestamp: Time.now,
      error: error
    }
  end
  
  def handle_scraping_error(url, error)
    {
      url: url,
      error: error.message,
      timestamp: Time.now,
      retry_recommended: true,
      error_type: classify_error(error)
    }.to_json
  end
  
  def classify_error(error)
    case error.message
    when /timeout/i
      "timeout_error"
    when /403/
      "forbidden_access"
    when /404/
      "not_found"
    when /500/
      "server_error"
    else
      "unknown_error"
    end
  end
end

# Data Validation Tool
class DataValidationTool < RCrewAI::Tools::Base
  def initialize(**options)
    super
    @name = 'data_validator'
    @description = 'Validate and clean scraped data'
  end
  
  def execute(**params)
    action = params[:action]
    
    case action
    when 'validate_data'
      validate_scraped_data(params[:data], params[:validation_rules])
    when 'clean_data'
      clean_scraped_data(params[:data], params[:cleaning_rules])
    when 'detect_duplicates'
      detect_duplicate_records(params[:dataset])
    when 'quality_score'
      calculate_data_quality(params[:data])
    else
      "Data validator: Unknown action #{action}"
    end
  end
  
  private
  
  def validate_scraped_data(data, validation_rules)
    # Simulate data validation
    validation_results = {
      total_records: data.is_a?(Array) ? data.length : 1,
      validation_summary: {
        valid_records: 0,
        invalid_records: 0,
        warnings: 0
      },
      field_validation: {},
      quality_issues: []
    }
    
    # Simulate field-by-field validation
    if validation_rules
      validation_rules.each do |field, rules|
        validation_results[:field_validation][field] = {
          required: rules[:required] || false,
          data_type: rules[:type] || "string",
          validation_status: "passed",
          invalid_count: 0,
          examples: ["Valid data example"]
        }
      end
    end
    
    # Simulate quality issues
    validation_results[:quality_issues] = [
      { type: "missing_data", field: "author", count: 3, severity: "medium" },
      { type: "invalid_date", field: "publish_date", count: 1, severity: "low" },
      { type: "duplicate_content", field: "title", count: 2, severity: "medium" }
    ]
    
    validation_results[:validation_summary] = {
      valid_records: validation_results[:total_records] - 6,
      invalid_records: 6,
      warnings: 3
    }
    
    validation_results.to_json
  end
  
  def clean_scraped_data(data, cleaning_rules)
    # Simulate data cleaning
    {
      original_count: data.is_a?(Array) ? data.length : 1,
      cleaned_count: data.is_a?(Array) ? data.length - 2 : 1,
      cleaning_operations: [
        { operation: "remove_duplicates", records_affected: 2 },
        { operation: "normalize_text", records_affected: 15 },
        { operation: "fix_encoding", records_affected: 3 },
        { operation: "validate_urls", records_affected: 8 }
      ],
      data_quality_improvement: "15% improvement in data quality score",
      cleaned_data_sample: {
        title: "Cleaned: Advanced Web Scraping Techniques",
        content: "Normalized content with proper encoding...",
        author: "Dr. Jane Smith",
        date: "2024-01-15T10:30:00Z"
      }
    }.to_json
  end
end

# Content Analysis Tool
class ContentAnalysisTool < RCrewAI::Tools::Base
  def initialize(**options)
    super
    @name = 'content_analyzer'
    @description = 'Analyze and process scraped content'
  end
  
  def execute(**params)
    action = params[:action]
    
    case action
    when 'analyze_content'
      analyze_text_content(params[:content], params[:analysis_type])
    when 'extract_entities'
      extract_named_entities(params[:text])
    when 'sentiment_analysis'
      analyze_sentiment(params[:text])
    when 'keyword_extraction'
      extract_keywords(params[:text], params[:max_keywords])
    when 'summarize_content'
      summarize_text(params[:text], params[:summary_length])
    else
      "Content analyzer: Unknown action #{action}"
    end
  end
  
  private
  
  def analyze_text_content(content, analysis_type)
    # Simulate content analysis
    base_analysis = {
      word_count: 1250,
      character_count: 7845,
      paragraph_count: 8,
      sentence_count: 42,
      average_sentence_length: 18.5,
      readability_score: 72,
      language: "en",
      content_type: "article"
    }
    
    case analysis_type
    when 'technical'
      base_analysis.merge({
        technical_terms: ["API", "algorithm", "database", "framework"],
        complexity_score: 8.2,
        jargon_density: 0.15,
        code_snippets: 3
      })
    when 'marketing'
      base_analysis.merge({
        call_to_action: "Learn more about our services",
        marketing_keywords: ["premium", "exclusive", "limited time"],
        persuasion_score: 7.5,
        emotional_triggers: ["urgency", "scarcity", "social proof"]
      })
    else
      base_analysis
    end.to_json
  end
  
  def extract_named_entities(text)
    # Simulate named entity recognition
    {
      entities: {
        persons: ["Dr. Jane Smith", "John Doe", "Mary Johnson"],
        organizations: ["OpenAI", "Google", "Microsoft", "Stanford University"],
        locations: ["San Francisco", "New York", "London"],
        dates: ["2024-01-15", "January 2024", "Q1 2024"],
        technologies: ["Python", "JavaScript", "Machine Learning", "API"]
      },
      entity_counts: {
        total_entities: 15,
        persons: 3,
        organizations: 4,
        locations: 3,
        dates: 3,
        technologies: 4
      },
      confidence_scores: {
        average_confidence: 0.87,
        high_confidence: 12,
        medium_confidence: 3,
        low_confidence: 0
      }
    }.to_json
  end
  
  def extract_keywords(text, max_keywords = 10)
    # Simulate keyword extraction
    {
      keywords: [
        { keyword: "web scraping", frequency: 12, relevance: 0.92 },
        { keyword: "data extraction", frequency: 8, relevance: 0.89 },
        { keyword: "automation", frequency: 6, relevance: 0.85 },
        { keyword: "python programming", frequency: 5, relevance: 0.78 },
        { keyword: "API integration", frequency: 4, relevance: 0.75 }
      ].first(max_keywords),
      keyword_density: 0.08,
      total_unique_words: 450,
      stopwords_removed: 180,
      stemming_applied: true
    }.to_json
  end
end

# ===== WEB SCRAPING AGENTS =====

# Web Scraper
web_scraper = RCrewAI::Agent.new(
  name: "web_scraper_specialist",
  role: "Web Scraping Specialist",
  goal: "Extract data from web sources efficiently and ethically while respecting rate limits and robots.txt",
  backstory: "You are a web scraping expert with deep knowledge of HTML parsing, CSS selectors, and ethical scraping practices. You excel at extracting structured data from various web sources while maintaining compliance with website policies.",
  tools: [
    WebScrapingTool.new(delay: 1.0),
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Data Validator
data_validator = RCrewAI::Agent.new(
  name: "data_validator",
  role: "Data Quality Specialist",
  goal: "Ensure scraped data quality through validation, cleaning, and quality control processes",
  backstory: "You are a data quality expert who specializes in validating, cleaning, and ensuring the integrity of scraped data. You excel at identifying and resolving data quality issues.",
  tools: [
    DataValidationTool.new,
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Content Analyzer
content_analyzer = RCrewAI::Agent.new(
  name: "content_analyzer",
  role: "Content Analysis Specialist",
  goal: "Analyze and process scraped content to extract insights and structured information",
  backstory: "You are a content analysis expert with knowledge of natural language processing, sentiment analysis, and information extraction. You excel at transforming unstructured text into actionable insights.",
  tools: [
    ContentAnalysisTool.new,
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Rate Limiter
rate_limiter = RCrewAI::Agent.new(
  name: "rate_limiter",
  role: "Ethical Scraping Manager",
  goal: "Manage scraping traffic and ensure compliance with website policies and ethical practices",
  backstory: "You are an ethical scraping expert who ensures all data collection activities comply with robots.txt, terms of service, and ethical guidelines. You excel at managing request rates and preventing server overload.",
  tools: [
    WebScrapingTool.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Data Processor
data_processor = RCrewAI::Agent.new(
  name: "data_processor",
  role: "Data Processing Specialist", 
  goal: "Transform and structure scraped data into usable formats for analysis and storage",
  backstory: "You are a data processing expert who specializes in transforming raw scraped data into structured, analysis-ready formats. You excel at data transformation, normalization, and preparation.",
  tools: [
    DataValidationTool.new,
    ContentAnalysisTool.new,
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Scraping Coordinator
scraping_coordinator = RCrewAI::Agent.new(
  name: "scraping_coordinator",
  role: "Web Scraping Operations Manager",
  goal: "Coordinate scraping operations, ensure workflow efficiency, and maintain data quality standards",
  backstory: "You are a scraping operations expert who manages complex web scraping projects from planning to execution. You excel at coordinating teams, optimizing workflows, and ensuring successful data collection.",
  manager: true,
  allow_delegation: true,
  tools: [
    RCrewAI::Tools::FileReader.new,
    RCrewAI::Tools::FileWriter.new
  ],
  verbose: true
)

# Create web scraping crew
scraping_crew = RCrewAI::Crew.new("web_scraping_crew", process: :hierarchical)

# Add agents to crew
scraping_crew.add_agent(scraping_coordinator)  # Manager first
scraping_crew.add_agent(web_scraper)
scraping_crew.add_agent(data_validator)
scraping_crew.add_agent(content_analyzer)
scraping_crew.add_agent(rate_limiter)
scraping_crew.add_agent(data_processor)

# ===== WEB SCRAPING PROJECT TASKS =====

# Web Scraping Task
web_scraping_task = RCrewAI::Task.new(
  name: "comprehensive_web_scraping",
  description: "Scrape data from multiple technology news and research websites to collect articles about AI automation and business applications. Extract titles, content, authors, dates, and metadata. Respect rate limits and robots.txt policies.",
  expected_output: "Complete dataset of scraped articles with metadata, organized in structured format",
  agent: web_scraper,
  async: true
)

# Data Validation Task
data_validation_task = RCrewAI::Task.new(
  name: "scraped_data_validation",
  description: "Validate and clean scraped data to ensure quality and consistency. Remove duplicates, fix encoding issues, validate URLs, and ensure data integrity. Generate data quality reports and recommendations.",
  expected_output: "Cleaned and validated dataset with quality assessment report and improvement recommendations",
  agent: data_validator,
  context: [web_scraping_task],
  async: true
)

# Content Analysis Task
content_analysis_task = RCrewAI::Task.new(
  name: "content_analysis_processing",
  description: "Analyze scraped content to extract insights, keywords, entities, and sentiment. Perform text analysis, keyword extraction, and content categorization. Generate content summaries and analytical insights.",
  expected_output: "Content analysis report with extracted insights, keywords, entities, and categorized content",
  agent: content_analyzer,
  context: [data_validation_task],
  async: true
)

# Ethical Compliance Task
ethical_compliance_task = RCrewAI::Task.new(
  name: "ethical_scraping_compliance",
  description: "Review scraping activities for ethical compliance and website policy adherence. Check robots.txt compliance, manage request rates, and ensure respectful scraping practices. Document compliance measures.",
  expected_output: "Ethical compliance report with policy adherence verification and best practices documentation",
  agent: rate_limiter,
  context: [web_scraping_task]
)

# Data Processing Task
data_processing_task = RCrewAI::Task.new(
  name: "data_processing_transformation",
  description: "Transform validated scraped data into analysis-ready formats. Create structured datasets, normalize content, and prepare data for downstream analysis. Generate data dictionaries and processing documentation.",
  expected_output: "Processed dataset in multiple formats with documentation and data dictionaries",
  agent: data_processor,
  context: [data_validation_task, content_analysis_task]
)

# Coordination Task
coordination_task = RCrewAI::Task.new(
  name: "scraping_operations_coordination",
  description: "Coordinate all scraping operations to ensure efficiency, quality, and ethical compliance. Monitor progress, manage workflows, and optimize scraping processes. Provide strategic oversight and recommendations.",
  expected_output: "Operations coordination report with workflow optimization, quality metrics, and strategic recommendations",
  agent: scraping_coordinator,
  context: [web_scraping_task, data_validation_task, content_analysis_task, ethical_compliance_task, data_processing_task]
)

# Add tasks to crew
scraping_crew.add_task(web_scraping_task)
scraping_crew.add_task(data_validation_task)
scraping_crew.add_task(content_analysis_task)
scraping_crew.add_task(ethical_compliance_task)
scraping_crew.add_task(data_processing_task)
scraping_crew.add_task(coordination_task)

# ===== SCRAPING PROJECT CONFIGURATION =====

scraping_project = {
  "project_name" => "AI Business Intelligence Data Collection",
  "target_domains" => [
    "techcrunch.com",
    "venturebeat.com", 
    "wired.com",
    "mit.edu",
    "arxiv.org"
  ],
  "data_targets" => [
    "AI automation articles",
    "Business technology news",
    "Research publications",
    "Industry reports",
    "Expert interviews"
  ],
  "scraping_parameters" => {
    "rate_limit" => "1 request per second",
    "max_pages_per_site" => 100,
    "content_types" => ["articles", "blog posts", "research papers"],
    "date_range" => "Last 6 months"
  },
  "quality_requirements" => {
    "minimum_content_length" => 500,
    "required_fields" => ["title", "content", "date", "author"],
    "validation_rules" => "Standard web content validation",
    "deduplication" => "Content similarity < 80%"
  },
  "ethical_guidelines" => {
    "robots_txt_compliance" => true,
    "rate_limiting" => true,
    "attribution_required" => true,
    "fair_use_only" => true
  },
  "expected_outputs" => [
    "Structured dataset with 500+ articles",
    "Content analysis and insights",
    "Keyword and entity extraction", 
    "Data quality reports"
  ]
}

File.write("scraping_project_config.json", JSON.pretty_generate(scraping_project))

puts "🕷️ Web Scraping Project Starting"
puts "="*60
puts "Project: #{scraping_project['project_name']}"
puts "Target Domains: #{scraping_project['target_domains'].length}"
puts "Expected Articles: 500+"
puts "Rate Limit: #{scraping_project['scraping_parameters']['rate_limit']}"
puts "="*60

# Sample scraped data structure
sample_data = {
  "articles" => [
    {
      "id" => "article_001",
      "url" => "https://techcrunch.com/ai-automation-business",
      "title" => "How AI Automation is Transforming Business Operations",
      "content" => "Comprehensive article content about AI automation...",
      "author" => "Sarah Johnson",
      "publish_date" => "2024-01-15T10:30:00Z",
      "category" => "Technology",
      "tags" => ["AI", "automation", "business", "technology"],
      "word_count" => 1250,
      "source_domain" => "techcrunch.com"
    }
  ],
  "scraping_stats" => {
    "total_urls_scraped" => 127,
    "successful_scrapes" => 119,
    "failed_scrapes" => 8,
    "success_rate" => 93.7,
    "total_articles_collected" => 119,
    "average_response_time" => "1.2s",
    "total_processing_time" => "6.5 minutes"
  }
}

File.write("sample_scraped_data.json", JSON.pretty_generate(sample_data))

puts "\n📊 Scraping Configuration Summary:"
puts "  • #{scraping_project['target_domains'].length} target domains identified"
puts "  • #{scraping_project['scraping_parameters']['max_pages_per_site']} max pages per site"
puts "  • #{scraping_project['quality_requirements']['minimum_content_length']} word minimum content length"
puts "  • Rate limiting: #{scraping_project['scraping_parameters']['rate_limit']}"

# ===== EXECUTE WEB SCRAPING PROJECT =====

puts "\n🚀 Starting Web Scraping Operations"
puts "="*60

# Execute the scraping crew
results = scraping_crew.execute

# ===== SCRAPING RESULTS =====

puts "\n📊 WEB SCRAPING PROJECT RESULTS"
puts "="*60

puts "Scraping Success Rate: #{results[:success_rate]}%"
puts "Total Scraping Components: #{results[:total_tasks]}"
puts "Completed Components: #{results[:completed_tasks]}"
puts "Project Status: #{results[:success_rate] >= 80 ? 'SUCCESSFUL' : 'NEEDS REVIEW'}"

scraping_categories = {
  "comprehensive_web_scraping" => "🕷️ Web Scraping",
  "scraped_data_validation" => "✅ Data Validation",
  "content_analysis_processing" => "📊 Content Analysis",
  "ethical_scraping_compliance" => "⚖️ Ethical Compliance",
  "data_processing_transformation" => "🔄 Data Processing",
  "scraping_operations_coordination" => "🎯 Operations Coordination"
}

puts "\n📋 SCRAPING OPERATIONS BREAKDOWN:"
puts "-"*50

results[:results].each do |scraping_result|
  task_name = scraping_result[:task].name
  category_name = scraping_categories[task_name] || task_name
  status_emoji = scraping_result[:status] == :completed ? "✅" : "❌"
  
  puts "#{status_emoji} #{category_name}"
  puts "   Specialist: #{scraping_result[:assigned_agent] || scraping_result[:task].agent.name}"
  puts "   Status: #{scraping_result[:status]}"
  
  if scraping_result[:status] == :completed
    puts "   Operation: Successfully completed"
  else
    puts "   Issue: #{scraping_result[:error]&.message}"
  end
  puts
end

# ===== SAVE SCRAPING DELIVERABLES =====

puts "\n💾 GENERATING WEB SCRAPING DELIVERABLES"
puts "-"*50

completed_operations = results[:results].select { |r| r[:status] == :completed }

# Create web scraping project directory
scraping_dir = "web_scraping_project_#{Date.today.strftime('%Y%m%d')}"
Dir.mkdir(scraping_dir) unless Dir.exist?(scraping_dir)

completed_operations.each do |operation_result|
  task_name = operation_result[:task].name
  operation_content = operation_result[:result]
  
  filename = "#{scraping_dir}/#{task_name}_deliverable.md"
  
  formatted_deliverable = <<~DELIVERABLE
    # #{scraping_categories[task_name] || task_name.split('_').map(&:capitalize).join(' ')} Deliverable
    
    **Scraping Specialist:** #{operation_result[:assigned_agent] || operation_result[:task].agent.name}  
    **Project:** #{scraping_project['project_name']}  
    **Completion Date:** #{Time.now.strftime('%B %d, %Y')}
    
    ---
    
    #{operation_content}
    
    ---
    
    **Project Parameters:**
    - Target Domains: #{scraping_project['target_domains'].join(', ')}
    - Rate Limit: #{scraping_project['scraping_parameters']['rate_limit']}
    - Content Types: #{scraping_project['scraping_parameters']['content_types'].join(', ')}
    - Quality Standards: #{scraping_project['quality_requirements']['minimum_content_length']}+ words
    
    *Generated by RCrewAI Web Scraping System*
  DELIVERABLE
  
  File.write(filename, formatted_deliverable)
  puts "  ✅ #{File.basename(filename)}"
end

# ===== WEB SCRAPING DASHBOARD =====

scraping_dashboard = <<~DASHBOARD
  # Web Scraping Operations Dashboard
  
  **Project:** #{scraping_project['project_name']}  
  **Last Updated:** #{Time.now.strftime('%Y-%m-%d %H:%M:%S')}  
  **Operations Success Rate:** #{results[:success_rate]}%
  
  ## Project Overview
  
  ### Scraping Targets
  - **Target Domains:** #{scraping_project['target_domains'].length}
  - **Content Types:** #{scraping_project['scraping_parameters']['content_types'].join(', ')}
  - **Date Range:** #{scraping_project['scraping_parameters']['date_range']}
  - **Max Pages/Site:** #{scraping_project['scraping_parameters']['max_pages_per_site']}
  
  ### Performance Metrics
  - **Total URLs Scraped:** #{sample_data['scraping_stats']['total_urls_scraped']}
  - **Success Rate:** #{sample_data['scraping_stats']['success_rate']}%
  - **Articles Collected:** #{sample_data['scraping_stats']['total_articles_collected']}
  - **Average Response Time:** #{sample_data['scraping_stats']['average_response_time']}
  
  ## Domain Performance
  
  ### Target Domain Status
  | Domain | Pages Scraped | Success Rate | Articles Collected |
  |--------|---------------|--------------|-------------------|
  | techcrunch.com | 45 | 96% | 43 |
  | venturebeat.com | 38 | 92% | 35 |
  | wired.com | 25 | 88% | 22 |
  | mit.edu | 12 | 100% | 12 |
  | arxiv.org | 7 | 86% | 6 |
  
  ### Content Distribution
  - **AI Automation Articles:** 48 articles (40%)
  - **Business Technology:** 36 articles (30%)
  - **Research Publications:** 19 articles (16%)
  - **Industry Reports:** 11 articles (9%)
  - **Expert Interviews:** 5 articles (4%)
  
  ## Data Quality Metrics
  
  ### Content Quality
  - **Average Word Count:** 1,247 words
  - **Content with Required Fields:** 95% (113/119)
  - **Duplicate Content Detected:** 6 articles (5%)
  - **Data Validation Score:** 91%
  
  ### Content Analysis Results
  - **Total Keywords Extracted:** 1,450
  - **Named Entities Identified:** 890
  - **Sentiment Analysis:** 78% positive, 18% neutral, 4% negative
  - **Content Categories:** 12 distinct categories identified
  
  ## Ethical Compliance Status
  
  ### Policy Adherence
  - **Robots.txt Compliance:** ✅ 100% compliant
  - **Rate Limiting:** ✅ 1 second delay enforced
  - **Terms of Service:** ✅ All sites reviewed and compliant
  - **Fair Use Guidelines:** ✅ Attribution and source tracking maintained
  
  ### Scraping Ethics
  - **Server Load Impact:** Minimal (rate limited)
  - **Content Attribution:** Complete source tracking
  - **Copyright Compliance:** Fair use only
  - **Privacy Protection:** No personal data collected
  
  ## Processing Pipeline Status
  
  ### Data Pipeline Flow

Web Scraping → Data Validation → Content Analysis → Processing → Quality Control → Output Generation

  
  ### Pipeline Performance
  - **Scraping Phase:** ✅ 119 articles collected
  - **Validation Phase:** ✅ 95% validation success rate
  - **Analysis Phase:** ✅ Complete content analysis
  - **Processing Phase:** ✅ Multiple output formats generated
  - **Quality Control:** ✅ All quality thresholds met
  
  ## Output Deliverables
  
  ### Generated Datasets
  - **Raw Scraped Data:** JSON format with full metadata
  - **Cleaned Dataset:** CSV format for analysis
  - **Content Analysis:** Structured insights and keywords
  - **Entity Database:** Named entities and relationships
  
  ### Analysis Reports
  - **Content Summary:** Executive summary of findings
  - **Trend Analysis:** Emerging themes and topics
  - **Source Analysis:** Domain and author insights
  - **Quality Report:** Data quality metrics and recommendations
  
  ## Operational Insights
  
  ### Performance Patterns
  - **Best Performing Time:** 10 AM - 2 PM EST (lowest error rates)
  - **Most Reliable Domains:** Academic sites (.edu) - 100% success
  - **Content Rich Sources:** TechCrunch, VentureBeat for business content
  - **Quality Leaders:** MIT and arXiv for research content
  
  ### Optimization Opportunities
  - **Rate Limit Optimization:** Could increase to 1.5 requests/second for some domains
  - **Content Filtering:** Enhanced filtering could improve relevance by 15%
  - **Duplicate Detection:** Real-time deduplication could improve efficiency
  - **Source Expansion:** Additional academic sources recommended
  
  ## Next Phase Recommendations
  
  ### Immediate Actions (Next Week)
  - [ ] Deploy automated monitoring for ongoing collection
  - [ ] Implement real-time duplicate detection
  - [ ] Set up content freshness alerts
  - [ ] Optimize rate limits for high-performance domains
  
  ### Strategic Enhancements (Next Month)
  - [ ] Add video content transcription capabilities  
  - [ ] Implement advanced content categorization
  - [ ] Develop predictive content quality scoring
  - [ ] Create automated trend detection system
  
  ### Long-term Development (Next Quarter)
  - [ ] Build real-time content analysis pipeline
  - [ ] Develop custom NLP models for domain-specific content
  - [ ] Implement federated scraping across multiple data centers
  - [ ] Create self-optimizing scraping algorithms
DASHBOARD

File.write("#{scraping_dir}/scraping_operations_dashboard.md", scraping_dashboard)
puts "  ✅ scraping_operations_dashboard.md"

# ===== WEB SCRAPING PROJECT SUMMARY =====

scraping_summary = <<~SUMMARY
  # Web Scraping Project Executive Summary
  
  **Project:** #{scraping_project['project_name']}  
  **Project Completion Date:** #{Time.now.strftime('%B %d, %Y')}  
  **Operations Success Rate:** #{results[:success_rate]}%
  
  ## Executive Overview
  
  The comprehensive web scraping project for AI Business Intelligence Data Collection has been successfully executed, delivering a rich dataset of #{sample_data['scraping_stats']['total_articles_collected']} articles from #{scraping_project['target_domains'].length} leading technology and research domains. The project achieved a #{sample_data['scraping_stats']['success_rate']}% success rate while maintaining full ethical compliance and data quality standards.
  
  ## Project Achievements
  
  ### Data Collection Success
  - **Articles Collected:** #{sample_data['scraping_stats']['total_articles_collected']} high-quality articles
  - **Success Rate:** #{sample_data['scraping_stats']['success_rate']}% across all target domains
  - **Content Quality:** 95% of articles meet all quality requirements
  - **Processing Efficiency:** #{sample_data['scraping_stats']['total_processing_time']} total processing time
  
  ### Domain Coverage
  - **Technology News:** TechCrunch, VentureBeat, Wired (108 articles)
  - **Academic Research:** MIT, arXiv (19 articles)
  - **Content Variety:** Articles, blog posts, research papers
  - **Time Range:** #{scraping_project['scraping_parameters']['date_range']} comprehensive coverage
  
  ### Quality Assurance
  - **Data Validation:** 91% overall data quality score
  - **Content Standards:** #{scraping_project['quality_requirements']['minimum_content_length']}+ words per article maintained
  - **Duplication Control:** 5% duplicate rate (well within acceptable limits)
  - **Field Completeness:** 95% articles contain all required fields
  
  ## Operations Excellence
  
  ### ✅ Web Scraping Operations
  - **Efficient Extraction:** Scraped #{sample_data['scraping_stats']['total_urls_scraped']} URLs with #{sample_data['scraping_stats']['average_response_time']} average response time
  - **Rate Limit Compliance:** Maintained #{scraping_project['scraping_parameters']['rate_limit']} to respect server resources
  - **Error Handling:** Robust error recovery with detailed logging
  - **Metadata Collection:** Complete source attribution and timestamp tracking
  
  ### ✅ Data Validation & Quality Control
  - **Comprehensive Validation:** Multi-layer validation ensuring data integrity
  - **Content Cleaning:** Removed encoding issues, normalized text, validated URLs
  - **Duplicate Detection:** Identified and handled 6 duplicate articles
  - **Quality Scoring:** Implemented automated quality assessment
  
  ### ✅ Advanced Content Analysis
  - **Keyword Extraction:** 1,450 relevant keywords identified and categorized
  - **Entity Recognition:** 890 named entities (persons, organizations, locations)
  - **Sentiment Analysis:** 78% positive sentiment in AI automation content
  - **Content Categorization:** 12 distinct content categories automatically assigned
  
  ### ✅ Ethical Compliance Excellence
  - **Robots.txt Compliance:** 100% adherence to robots.txt directives
  - **Fair Use Principles:** All content used within fair use guidelines
  - **Attribution Tracking:** Complete source and author attribution
  - **Privacy Protection:** No personal or sensitive data collected
  
  ### ✅ Data Processing & Transformation
  - **Format Conversion:** Generated JSON, CSV, and structured data formats
  - **Content Normalization:** Standardized formatting and encoding
  - **Analysis-Ready Datasets:** Prepared data for downstream analysis
  - **Comprehensive Documentation:** Data dictionaries and processing logs
  
  ### ✅ Operational Coordination
  - **Workflow Optimization:** Streamlined processes for maximum efficiency
  - **Quality Monitoring:** Real-time quality control and validation
  - **Resource Management:** Optimized resource utilization and cost control
  - **Strategic Oversight:** Coordinated operations across all functional areas
  
  ## Business Value Delivered
  
  ### Immediate Value
  - **Rich Dataset:** 119 high-quality articles ready for analysis
  - **Content Insights:** Comprehensive analysis of AI automation trends
  - **Competitive Intelligence:** Industry trend identification and analysis
  - **Research Foundation:** Solid data foundation for strategic decisions
  
  ### Strategic Intelligence
  - **Market Trends:** Identified emerging themes in AI automation
  - **Industry Sentiment:** Positive outlook on AI business applications
  - **Key Players:** Mapped influential authors and thought leaders
  - **Innovation Patterns:** Tracked technology adoption and development cycles
  
  ### Operational Efficiency
  - **Automated Collection:** Eliminated manual research across #{scraping_project['target_domains'].length} domains
  - **Time Savings:** Reduced research time from weeks to hours
  - **Quality Consistency:** Standardized content collection and validation
  - **Scalable Process:** Established framework for ongoing data collection
  
  ## Content Analysis Insights
  
  ### Topic Distribution
  - **AI Automation (40%):** Business process automation and efficiency
  - **Business Technology (30%):** Enterprise technology adoption
  - **Research & Development (16%):** Academic and industrial research
  - **Industry Analysis (9%):** Market analysis and competitive intelligence
  - **Expert Opinions (4%):** Thought leadership and expert interviews
  
  ### Emerging Themes
  - **Human-AI Collaboration:** Increasing focus on augmentation vs. replacement
  - **Ethical AI Implementation:** Growing emphasis on responsible AI adoption
  - **Industry-Specific Applications:** Customized AI solutions for specific sectors
  - **ROI Measurement:** Emphasis on quantifiable business benefits
  
  ### Key Insights
  - **Positive Industry Sentiment:** 78% positive coverage of AI automation
  - **Implementation Focus:** Shift from theoretical to practical applications
  - **Quality Over Quantity:** Emphasis on strategic rather than broad implementation
  - **Collaboration Models:** Growing interest in human-AI partnership approaches
  
  ## Technology Performance
  
  ### Scraping Infrastructure
  - **Reliability:** 93.7% success rate across diverse content sources
  - **Performance:** 1.2-second average response time
  - **Scalability:** Successfully handled 127 concurrent URL extractions
  - **Error Recovery:** Robust handling of network issues and content variations
  
  ### Data Processing Capabilities
  - **Natural Language Processing:** Advanced text analysis and entity extraction
  - **Content Classification:** Automated categorization with high accuracy
  - **Quality Assessment:** Multi-dimensional quality scoring and validation
  - **Format Flexibility:** Multiple output formats for different use cases
  
  ## Future Enhancements
  
  ### Immediate Improvements (Next 30 Days)
  - **Real-Time Monitoring:** Deploy continuous content monitoring
  - **Alert System:** Automated notifications for new relevant content
  - **Quality Enhancement:** Advanced duplicate detection and content filtering
  - **Performance Optimization:** Fine-tune rate limits and error handling
  
  ### Strategic Development (Next 90 Days)
  - **Source Expansion:** Add 5-10 additional high-value content sources
  - **Advanced Analytics:** Implement predictive trend analysis
  - **Content Enrichment:** Add social media sentiment and engagement data
  - **API Integration:** Connect to real-time content feeds
  
  ### Innovation Roadmap (6+ Months)
  - **AI-Powered Curation:** Machine learning for content relevance scoring
  - **Multi-Modal Processing:** Add video, audio, and image content analysis
  - **Predictive Intelligence:** Forecast industry trends based on content patterns
  - **Automated Insights:** Generate automated reports and recommendations
  
  ## Competitive Advantages
  
  ### Data Quality Leadership
  - **Comprehensive Validation:** Multi-layer quality control exceeds industry standards
  - **Ethical Excellence:** 100% compliance with ethical scraping practices
  - **Content Richness:** Average 1,247 words per article with complete metadata
  - **Processing Speed:** 6.5-minute processing time for 127 articles
  
  ### Technical Innovation
  - **Advanced NLP:** Sophisticated content analysis and entity recognition
  - **Automated Quality Control:** Real-time validation and error correction
  - **Scalable Architecture:** Framework supports 10x data volume growth
  - **Integration Ready:** Multiple output formats for diverse applications
  
  ## Return on Investment
  
  ### Quantifiable Benefits
  - **Research Time Savings:** Eliminated 40+ hours of manual research
  - **Data Quality Improvement:** 91% quality score vs. 70% industry average
  - **Content Comprehensiveness:** 5x more comprehensive than manual collection
  - **Ongoing Value:** Reusable framework for continuous intelligence gathering
  
  ### Strategic Value
  - **Market Intelligence:** Real-time visibility into industry trends
  - **Competitive Advantage:** Access to comprehensive, structured industry data
  - **Decision Support:** Data-driven insights for strategic planning
  - **Innovation Intelligence:** Early identification of emerging trends and opportunities
  
  ## Conclusion
  
  The Web Scraping Project for AI Business Intelligence Data Collection has successfully delivered a comprehensive, high-quality dataset while maintaining exceptional ethical and technical standards. With 119 articles collected at a 93.7% success rate, the project provides a solid foundation for strategic intelligence and business decision-making.
  
  ### Project Status: SUCCESSFULLY COMPLETED
  - **All objectives achieved with #{results[:success_rate]}% success rate**
  - **Comprehensive dataset ready for analysis and strategic application**
  - **Ethical compliance and quality standards exceeded throughout**
  - **Scalable framework established for ongoing intelligence gathering**
  
  ---
  
  **Web Scraping Team Performance:**
  - Web scraping specialists delivered efficient, compliant data collection
  - Data validators ensured exceptional quality control and data integrity
  - Content analyzers provided deep insights and comprehensive text analysis
  - Rate limiters maintained perfect ethical compliance across all operations
  - Data processors created analysis-ready datasets in multiple formats
  - Operations coordinators optimized workflows and maintained strategic oversight
  
  *This comprehensive web scraping project demonstrates the power of coordinated specialist teams in delivering high-quality, ethically-compliant data collection that provides exceptional business intelligence value.*
SUMMARY

File.write("#{scraping_dir}/WEB_SCRAPING_PROJECT_SUMMARY.md", scraping_summary)
puts "  ✅ WEB_SCRAPING_PROJECT_SUMMARY.md"

puts "\n🎉 WEB SCRAPING PROJECT COMPLETED!"
puts "="*70
puts "📁 Complete scraping package saved to: #{scraping_dir}/"
puts ""
puts "🕷️ **Scraping Results:**"
puts "   • #{sample_data['scraping_stats']['total_articles_collected']} articles successfully collected"
puts "   • #{sample_data['scraping_stats']['success_rate']}% success rate across #{scraping_project['target_domains'].length} domains"
puts "   • #{sample_data['scraping_stats']['average_response_time']} average response time"
puts "   • 100% ethical compliance maintained"
puts ""
puts "📊 **Data Quality:**"
puts "   • 91% overall data quality score"
puts "   • 95% field completion rate"
puts "   • 5% duplicate rate (within acceptable limits)"
puts "   • 1,247 average word count per article"
puts ""
puts "🎯 **Key Insights:**"
puts "   • 1,450 keywords extracted and categorized"
puts "   • 890 named entities identified"
puts "   • 78% positive sentiment on AI automation"
puts "   • 12 distinct content categories identified"

Key Web Scraping Features

1. Comprehensive Scraping Architecture

Full-spectrum web scraping with specialized expertise:

web_scraper              # Multi-site data extraction
data_validator          # Quality control and validation  
content_analyzer        # Text processing and analysis
rate_limiter           # Ethical compliance management
data_processor         # Data transformation and structuring
scraping_coordinator   # Strategic oversight and coordination (Manager)

2. Advanced Scraping Tools

Specialized tools for professional web scraping:

WebScrapingTool         # Rate-limited, ethical web scraping
DataValidationTool      # Data quality control and cleaning
ContentAnalysisTool     # NLP and content analysis

3. Ethical Compliance Framework

Responsible scraping practices:

• Robots.txt compliance verification
• Rate limiting and server respect
• Fair use and attribution tracking
• Privacy protection measures

4. Quality Assurance System

Multi-layer quality control:

• Data validation and cleaning
• Content analysis and categorization
• Duplicate detection and removal
• Automated quality scoring

5. Scalable Data Pipeline

End-to-end data processing:

# Processing workflow
Web Scraping → Data Validation → Content Analysis →
Rate Limiting → Data Processing → Coordination & Delivery

This web scraping system provides a complete framework for ethical, high-quality data collection from web sources, delivering structured datasets with comprehensive analysis and insights while maintaining full compliance with legal and ethical standards.