Web Scraping with AI: Modern Techniques for 2026

# Traditional scraping approach
from bs4 import BeautifulSoup
soup = BeautifulSoup(html, 'html.parser')
prices = soup.find_all('span', class_='product-price')

// Headless browser approach
const browser = await puppeteer.launch();
const page = await browser.newPage();
await page.goto(url);
const data = await page.evaluate(() => {
  return Array.from(document.querySelectorAll('.product')).map(el => ({
    name: el.querySelector('.name').textContent,
    price: el.querySelector('.price').textContent
  }));
});

// AI-powered approach
const scraper = new AiScraper({
  useVision: true,
  useNLP: true
});

const result = await scraper.extract({
  url: targetUrl,
  instruction: "Extract all product names, prices, and ratings",
  schema: ProductSchema
});

interface VisualAnalyzer {
  detectElements(screenshot: Buffer): Promise<ElementBoundingBox[]>;
  classifyElement(bounds: ElementBoundingBox): ElementType;
  extractVisualFeatures(element: Element): VisualFeatures;
}

interface NLPProcessor {
  parseInstruction(userQuery: string): ExtractionIntent;
  extractEntities(text: string): Entity[];
  classifyContent(text: string): ContentCategory;
}

interface DOMAnalyzer {
  buildSemanticTree(dom: Document): SemanticTree;
  identifyPatterns(tree: SemanticTree): Pattern[];
  predictSelectors(target: ElementDescription): SelectorPrediction[];
}

interface ExecutionEngine {
  navigate(url: string, options: NavigationOptions): Promise<void>;
  extract(strategy: ExtractionStrategy): Promise<ExtractedData>;
  adapt(error: ExtractionError): Promise<AdaptedStrategy>;
}

interface AntiDetection {
  randomizeFingerprint(): BrowserFingerprint;
  simulateHumanBehavior(actions: Action[]): Promise<void>;
  rotateIdentity(): Promise<void>;
}

User Instruction
    ↓
[NLP Parsing] → Extraction Intent
    ↓
[Visual Analysis] + [DOM Analysis] → Element Identification
    ↓
[Execution Engine] → Browser Actions
    ↓
[Data Extraction] → Raw Data
    ↓
[Validation & Transformation] → Structured Data
    ↓
[Quality Assessment] → Validated Output

const price = document.querySelector('.product-detail__price-current');

const price = await semanticDetector.findElement({
  type: 'price',
  context: 'product',
  characteristics: ['monetary value', 'prominently displayed', 'near product title']
});

interface ContentExtractor {
  extractStructuredData(content: string, schema: Schema): Promise<StructuredData>;
  inferRelationships(elements: Element[]): Relationship[];
  normalizeData(raw: RawData): NormalizedData;
}

// Example usage
const extractor = new AIContentExtractor();
const products = await extractor.extractStructuredData(pageContent, {
  type: 'product',
  fields: {
    name: { type: 'string', required: true },
    price: { type: 'currency', required: true },
    rating: { type: 'number', min: 0, max: 5 },
    availability: { type: 'enum', values: ['in-stock', 'out-of-stock', 'pre-order'] }
  }
});

class AdaptiveSelectorEngine {
  async findElement(description: ElementDescription): Promise<Element> {
    // Generate multiple selector candidates
    const candidates = await this.generateSelectorCandidates(description);

    // Score each candidate
    const scored = await Promise.all(
      candidates.map(async selector => ({
        selector,
        score: await this.scoreSelector(selector, description)
      }))
    );

    // Use highest scoring selector
    const best = scored.sort((a, b) => b.score - a.score)[0];
    return document.querySelector(best.selector);
  }

  private async generateSelectorCandidates(description: ElementDescription): Promise<string[]> {
    return [
      this.generateSemanticSelector(description),
      this.generateVisualSelector(description),
      this.generatePatternSelector(description),
      this.generateAttributeSelector(description),
      this.generateContextualSelector(description)
    ];
  }
}

interface PatternRecognizer {
  detectRepeatingStructures(dom: Document): RepeatingPattern[];
  identifyListItems(pattern: RepeatingPattern): Element[];
  extractSchema(items: Element[]): DataSchema;
}

// Automatically detects product grids, article lists, etc.
const patterns = await patternRecognizer.detectRepeatingStructures(document);
const productList = patterns.find(p => p.type === 'product-grid');
const products = await patternRecognizer.identifyListItems(productList);

interface MLScraperModel {
  train(examples: TrainingExample[]): Promise<void>;
  predict(page: Document): Promise<ExtractionResult>;
  update(feedback: UserFeedback): Promise<void>;
}

// Training workflow
const model = new MLScraperModel();

await model.train([
  {
    url: 'https://example.com/product/1',
    annotations: {
      productName: { selector: 'h1.product-title', value: 'Example Product' },
      price: { selector: 'span.price', value: '$29.99' },
      rating: { selector: 'div.rating', value: '4.5' }
    }
  },
  // More training examples...
]);

// Model generalizes to new pages
const result = await model.predict(newProductPage);

class TransferLearningScraper {
  constructor(
    private baseModel: PretrainedModel,
    private domainAdapter: DomainAdapter
  ) {}

  async extractFrom(url: string, schema: Schema): Promise<Data> {
    // Base model provides general web understanding
    const features = await this.baseModel.extractFeatures(url);

    // Domain adapter specializes for specific website patterns
    const adapted = await this.domainAdapter.transform(features);

    // Extract according to schema
    return this.extractBySchema(adapted, schema);
  }
}

interface RLScrapingAgent {
  selectAction(state: PageState): Action;
  executeAction(action: Action): Promise<ActionResult>;
  updatePolicy(reward: number): void;
}

// Agent learns best navigation and extraction sequences
const agent = new RLScrapingAgent();

while (!task.complete) {
  const action = agent.selectAction(currentState);
  const result = await agent.executeAction(action);

  // Reward based on extraction quality and efficiency
  const reward = calculateReward(result);
  agent.updatePolicy(reward);
}

interface AnomalyDetector {
  trainOnValidData(examples: ValidData[]): Promise<void>;
  detectAnomalies(extracted: ExtractedData): Anomaly[];
  suggestCorrections(anomaly: Anomaly): Correction[];
}

const detector = new AnomalyDetector();
await detector.trainOnValidData(historicalData);

const extracted = await scraper.extract(url);
const anomalies = detector.detectAnomalies(extracted);

if (anomalies.length > 0) {
  const corrections = await detector.suggestCorrections(anomalies[0]);
  // Apply corrections or flag for human review
}

interface VisualDetector {
  detectButtons(screenshot: Image): Promise<ButtonLocation[]>;
  identifyForms(screenshot: Image): Promise<FormBoundingBox[]>;
  findDataTables(screenshot: Image): Promise<TableRegion[]>;
  classifyVisualElements(regions: Region[]): ElementClassification[];
}

// Detect clickable elements visually
const detector = new VisionModel();
const screenshot = await page.screenshot();
const buttons = await detector.detectButtons(screenshot);

// Click button by visual characteristics
const loginButton = buttons.find(b => b.label === 'Login' || b.color === 'primary');
await page.click(loginButton.center);

interface OCRExtractor {
  extractText(image: Image, options?: OCROptions): Promise<TextBlock[]>;
  detectLanguage(text: string): Language;
  correctErrors(text: string): string;
}

// Extract text from product images
const ocr = new OCRExtractor();
const productImage = await page.screenshot({ clip: productBounds });
const textBlocks = await ocr.extractText(productImage);

const productInfo = textBlocks.find(block =>
  block.confidence > 0.9 && block.contains(/\$\d+/)
);

interface LayoutAnalyzer {
  segmentPage(screenshot: Image): Promise<ContentRegion[]>;
  identifyMainContent(regions: ContentRegion[]): ContentRegion;
  detectNavigation(regions: ContentRegion[]): NavigationRegion;
  findSidebars(regions: ContentRegion[]): SidebarRegion[];
}

// Focus extraction on main content area
const analyzer = new LayoutAnalyzer();
const regions = await analyzer.segmentPage(screenshot);
const mainContent = analyzer.identifyMainContent(regions);

// Extract only from main content region
const data = await scraper.extractFromRegion(mainContent);

interface VisualMatcher {
  computeVisualFingerprint(element: Element): VisualFingerprint;
  findSimilarElements(target: VisualFingerprint, threshold: number): Element[];
  matchAcrossPages(reference: Element, newPage: Page): Element | null;
}

// Identify "Add to Cart" button across different pages
const matcher = new VisualMatcher();
const referenceButton = await page.$('button.add-to-cart');
const fingerprint = await matcher.computeVisualFingerprint(referenceButton);

// On different product page
await page.goto(newProductUrl);
const similarButton = await matcher.findSimilarElements(fingerprint, 0.85);
await similarButton[0].click();

interface IntentParser {
  parseQuery(query: string): ExtractionIntent;
  extractEntities(query: string): Entity[];
  inferSchema(query: string): DataSchema;
}

// User query: "Get me the product name, price, and customer reviews"
const intent = await intentParser.parseQuery(userQuery);
// Result: {
//   action: 'extract',
//   entities: ['product_name', 'price', 'reviews'],
//   context: 'product_page'
// }

interface SemanticSearch {
  findSimilarContent(query: string, content: string[]): ScoredContent[];
  extractAnswerFromContext(question: string, context: string): string;
  summarizeContent(content: string, maxLength: number): string;
}

// Find product specifications semantically
const search = new SemanticSearch();
const allText = await page.evaluate(() => document.body.innerText);

const batteryInfo = await search.extractAnswerFromContext(
  "What is the battery life?",
  allText
);
// Returns: "Up to 12 hours of video playback"

interface EntityExtractor {
  extractPrices(text: string): Price[];
  extractDates(text: string): Date[];
  extractLocations(text: string): Location[];
  extractOrganizations(text: string): Organization[];
  normalizeEntity(entity: RawEntity): NormalizedEntity;
}

// Extract and normalize prices from varying formats
const extractor = new EntityExtractor();
const prices = extractor.extractPrices(productDescription);
// Input: "$29.99", "29,99 EUR", "¥3,000"
// Output: [
//   { amount: 29.99, currency: 'USD' },
//   { amount: 29.99, currency: 'EUR' },
//   { amount: 3000, currency: 'JPY' }
// ]

interface ContextualExtractor {
  extractWithContext(element: Element, radius: number): ContextualData;
  resolvePronouns(text: string, context: Context): ResolvedText;
  inferMissingData(partial: PartialData, context: Context): CompleteData;
}

// Extract product price with contextual validation
const contextual = new ContextualExtractor();
const priceData = await contextual.extractWithContext(priceElement, 200);

// Validates price is actually for the product, not shipping cost
if (priceData.context.nearbyText.includes('shipping')) {
  continue; // Skip shipping price
}

interface ScrapingAgent {
  role: 'navigator' | 'extractor' | 'validator' | 'coordinator';
  execute(task: Task, context: Context): Promise<Result>;
}

class NavigatorAgent implements ScrapingAgent {
  role = 'navigator' as const;

  async execute(task: NavigationTask): Promise<NavigationResult> {
    // Handle page navigation, pagination, form filling
    await this.handleCookieConsent();
    await this.navigateToTarget(task.url);
    await this.handlePagination(task.paginationStrategy);
    return { success: true, currentUrl: page.url() };
  }
}

class ExtractorAgent implements ScrapingAgent {
  role = 'extractor' as const;

  async execute(task: ExtractionTask): Promise<ExtractionResult> {
    // Specialized extraction logic
    const elements = await this.findTargetElements(task.schema);
    const data = await this.extractData(elements);
    return { data, confidence: this.assessConfidence(data) };
  }
}

class ValidatorAgent implements ScrapingAgent {
  role = 'validator' as const;

  async execute(task: ValidationTask): Promise<ValidationResult> {
    // Data quality checking
    const issues = await this.validateData(task.data, task.schema);
    const suggestions = await this.suggestCorrections(issues);
    return { valid: issues.length === 0, issues, suggestions };
  }
}

class MultiAgentScraper {
  constructor(
    private agents: ScrapingAgent[],
    private coordinator: CoordinatorAgent
  ) {}

  async scrape(instruction: string, url: string): Promise<ScrapingResult> {
    const context = new SharedContext();
    const plan = await this.coordinator.createPlan(instruction);

    for (const step of plan.steps) {
      const agent = this.selectAgent(step.type);
      const result = await agent.execute(step, context);

      context.addResult(step.id, result);

      if (result.requiresAdaptation) {
        const adapted = await this.coordinator.adaptPlan(plan, result);
        plan = adapted;
      }
    }

    return context.getFinalResult();
  }

  private selectAgent(taskType: TaskType): ScrapingAgent {
    return this.agents.find(a => a.role === this.roleForTask(taskType));
  }
}

interface ErrorRecoveryStrategy {
  diagnose(error: Error, context: Context): Diagnosis;
  selectRecoveryAgent(diagnosis: Diagnosis): ScrapingAgent;
  attemptRecovery(agent: ScrapingAgent, diagnosis: Diagnosis): Promise<Recovery>;
}

// When navigator agent encounters CAPTCHA
const error = new CaptchaDetectedError();
const diagnosis = recoveryStrategy.diagnose(error, context);
// diagnosis: { type: 'captcha', severity: 'high', recoverable: true }

const recoveryAgent = recoveryStrategy.selectRecoveryAgent(diagnosis);
// Selects specialized CAPTCHA-solving agent

const recovery = await recoveryAgent.attemptRecovery(diagnosis);
if (recovery.success) {
  // Continue with extraction agent
  const extractor = this.selectAgent('extractor');
  await extractor.execute(extractionTask, context);
}

interface HumanBehaviorSimulator {
  generateMouseMovement(start: Point, end: Point): MousePath;
  simulateKeyboardTyping(text: string): KeystrokePattern;
  addRandomPauses(actions: Action[]): Action[];
  simulateScrolling(pattern: ScrollPattern): ScrollAction[];
}

class BiometricSimulator implements HumanBehaviorSimulator {
  generateMouseMovement(start: Point, end: Point): MousePath {
    // Generate bezier curve with random micro-movements
    const path = this.bezierCurve(start, end);

    // Add human-like imprecision
    return path.map(point => ({
      x: point.x + this.randomJitter(),
      y: point.y + this.randomJitter(),
      timestamp: this.humanTimestamp()
    }));
  }

  simulateKeyboardTyping(text: string): KeystrokePattern {
    return text.split('').map((char, i) => ({
      key: char,
      delay: this.humanTypingDelay(i, char),
      pressure: this.randomPressure(),
      errors: this.occasionalTypo(i, 0.02) // 2% typo rate
    }));
  }
}

interface FingerprintManager {
  generateFingerprint(): BrowserFingerprint;
  rotateFingerprint(): Promise<void>;
  matchFingerprint(profile: UserProfile): BrowserFingerprint;
}

class AdaptiveFingerprintManager implements FingerprintManager {
  generateFingerprint(): BrowserFingerprint {
    return {
      userAgent: this.randomUserAgent(),
      screenResolution: this.commonResolution(),
      timezone: this.matchingTimezone(),
      languages: this.naturalLanguages(),
      plugins: this.realisticPlugins(),
      fonts: this.systemFonts(),
      webgl: this.webglFingerprint(),
      canvas: this.canvasFingerprint(),
      audio: this.audioFingerprint()
    };
  }

  // Ensure consistency within session
  private ensureConsistency(fingerprint: BrowserFingerprint): void {
    // User agent and platform must match
    if (fingerprint.userAgent.includes('Mac') && fingerprint.platform !== 'MacIntel') {
      fingerprint.platform = 'MacIntel';
    }

    // Screen resolution should match hardware capabilities
    // Language preferences should align with timezone
    // etc.
  }
}

interface TimingStrategy {
  calculateDelay(action: Action, context: Context): number;
  scheduleRequests(urls: string[]): ScheduledRequest[];
  simulateThinkTime(): number;
}

class HumanTimingStrategy implements TimingStrategy {
  calculateDelay(action: Action, context: Context): number {
    const baseDelay = this.actionBaseDelay(action.type);

    // Add variability
    const jitter = this.gaussian(0, baseDelay * 0.2);

    // Factor in page complexity
    const complexityFactor = this.assessPageComplexity(context);

    // Occasional longer pauses (like reading)
    const thinkPause = Math.random() < 0.1 ? this.simulateThinkTime() : 0;

    return baseDelay + jitter + complexityFactor + thinkPause;
  }

  simulateThinkTime(): number {
    // 2-8 seconds with long tail distribution
    return this.exponential(3000) + 2000;
  }

  scheduleRequests(urls: string[]): ScheduledRequest[] {
    return urls.map((url, i) => ({
      url,
      delay: this.cumulativeDelay(i),
      priority: this.assignPriority(url)
    }));
  }
}

interface TLSManager {
  configureTLS(browserProfile: BrowserProfile): TLSConfig;
  matchCipherSuites(browser: string, version: string): CipherSuite[];
  rotateSessionTicket(): void;
}

// Ensure TLS fingerprint matches user agent
const tlsManager = new TLSManager();
const config = tlsManager.configureTLS({
  browser: 'Chrome',
  version: '121.0',
  os: 'Windows 10'
});

// Resulting TLS configuration matches Chrome 121 exactly
// including cipher suite order, extensions, and elliptic curves

interface ProxyManager {
  getProxy(criteria: ProxyCriteria): Promise<Proxy>;
  rotateProxy(reason: RotationReason): Promise<Proxy>;
  assessProxyHealth(proxy: Proxy): Promise<HealthScore>;
  retireProxy(proxy: Proxy, reason: string): void;
}

class SmartProxyManager implements ProxyManager {
  async getProxy(criteria: ProxyCriteria): Promise<Proxy> {
    // Select proxy matching geographic and performance criteria
    const candidates = await this.filterProxies(criteria);

    // Prefer proxies with successful recent history
    const scored = candidates.map(p => ({
      proxy: p,
      score: this.scoreProxy(p, criteria)
    }));

    return scored.sort((a, b) => b.score - a.score)[0].proxy;
  }

  async rotateProxy(reason: RotationReason): Promise<Proxy> {
    // Intelligent rotation based on failure reason
    if (reason.type === 'rate-limit') {
      // Get proxy from different subnet
      return this.getProxy({ excludeSubnet: currentProxy.subnet });
    } else if (reason.type === 'blocked') {
      // Retire proxy and get completely different one
      this.retireProxy(currentProxy, reason.details);
      return this.getProxy({ excludeProvider: currentProxy.provider });
    }

    return this.getProxy({});
  }
}

interface WaitStrategy {
  waitForContent(prediction: LoadPrediction): Promise<void>;
  detectStableState(): Promise<boolean>;
  waitForNetworkIdle(threshold: number): Promise<void>;
  predictLoadTime(url: string): Promise<number>;
}

class PredictiveWaitStrategy implements WaitStrategy {
  async waitForContent(prediction: LoadPrediction): Promise<void> {
    // Learn from previous loads of similar pages
    const history = await this.getLoadHistory(prediction.pattern);
    const predictedTime = this.mlModel.predict(history);

    // Wait for predicted time with timeout
    await Promise.race([
      this.waitForSelector(prediction.selector),
      this.delay(predictedTime * 1.2), // 20% buffer
      this.timeout(prediction.maxWait)
    ]);
  }

  async detectStableState(): Promise<boolean> {
    // Monitor DOM mutations to detect when page settles
    const mutations = await this.observeMutations(1000);

    // Page is stable when mutation rate drops below threshold
    return mutations.rate < this.stabilityThreshold;
  }
}

interface NetworkMonitor {
  waitForRequest(pattern: URLPattern): Promise<Request>;
  waitForResponse(pattern: URLPattern): Promise<Response>;
  interceptRequest(pattern: URLPattern, handler: RequestHandler): void;
}

// Wait for product data API call
const monitor = new NetworkMonitor(page);

const dataPromise = monitor.waitForResponse(/api\/products\/\d+/);

await page.click('.load-more');

const response = await dataPromise;
const productData = await response.json();

// Use API data directly instead of parsing DOM
return productData;

interface ScrollStrategy {
  detectInfiniteScroll(): Promise<boolean>;
  scrollToLoad(targetCount: number): Promise<void>;
  extractFromVirtualList(container: Element): Promise<Data[]>;
}

class InfiniteScrollHandler implements ScrollStrategy {
  async scrollToLoad(targetCount: number): Promise<void> {
    let itemCount = 0;
    let previousHeight = 0;
    let stableScrolls = 0;

    while (itemCount < targetCount) {
      // Scroll to bottom
      await this.scrollToBottom();

      // Wait for new content
      await this.waitForNewContent();

      // Count items
      itemCount = await this.countItems();

      // Detect end of content
      const currentHeight = await this.getScrollHeight();
      if (currentHeight === previousHeight) {
        stableScrolls++;
        if (stableScrolls >= 3) break; // No new content after 3 attempts
      } else {
        stableScrolls = 0;
      }

      previousHeight = currentHeight;
    }
  }

  async extractFromVirtualList(container: Element): Promise<Data[]> {
    // Virtual lists only render visible items
    // Must scroll through to capture all data
    const allData: Data[] = [];

    await this.scrollContainer(container, async (visibleItems) => {
      const extracted = await this.extractVisible(visibleItems);
      allData.push(...extracted);
    });

    // Deduplicate
    return this.deduplicateData(allData);
  }
}

interface ShadowDOMNavigator {
  findInShadowDOM(selector: string, depth: number): Promise<Element[]>;
  extractFromShadowRoot(root: ShadowRoot): Promise<Data>;
  traverseShadowTrees(callback: (element: Element) => void): Promise<void>;
}

// Find elements across shadow DOM boundaries
const navigator = new ShadowDOMNavigator();

const elements = await navigator.findInShadowDOM('.product-name', 5);
// Searches through up to 5 levels of shadow DOM

for (const element of elements) {
  const data = await this.extractData(element);
  results.push(data);
}

interface WebSocketCapture {
  interceptWebSocket(pattern: URLPattern): WebSocketProxy;
  captureMessages(filter: MessageFilter): Promise<Message[]>;
  extractFromStream(duration: number): Promise<StreamData>;
}

// Capture real-time price updates
const wsCapture = new WebSocketCapture(page);

const proxy = wsCapture.interceptWebSocket(/wss:\/\/.*\/prices/);

proxy.on('message', (message) => {
  const priceUpdate = JSON.parse(message.data);
  priceHistory.push({
    timestamp: Date.now(),
    price: priceUpdate.currentPrice
  });
});

// Collect 60 seconds of price data
await this.delay(60000);

return priceHistory;

import { z } from 'zod';

const ProductSchema = z.object({
  name: z.string().min(1).max(200),
  price: z.number().positive(),
  currency: z.enum(['USD', 'EUR', 'GBP', 'JPY']),
  rating: z.number().min(0).max(5).optional(),
  availability: z.enum(['in-stock', 'out-of-stock', 'pre-order']),
  url: z.string().url(),
  imageUrl: z.string().url().optional()
});

interface SchemaValidator {
  validate(data: unknown, schema: z.ZodSchema): ValidationResult;
  coerceTypes(data: unknown, schema: z.ZodSchema): CoercedData;
  suggestCorrections(data: unknown, errors: ValidationError[]): Suggestion[];
}

// Validate and correct extracted data
const validator = new SchemaValidator();
const result = validator.validate(extractedData, ProductSchema);

if (!result.success) {
  // AI suggests corrections
  const suggestions = validator.suggestCorrections(extractedData, result.errors);
  const corrected = await this.applySuggestions(extractedData, suggestions);

  // Re-validate
  const retryResult = validator.validate(corrected, ProductSchema);
}

interface AnomalyDetector {
  detectOutliers(data: Data[], field: string): Outlier[];
  detectInconsistencies(data: Data[]): Inconsistency[];
  detectMissingPatterns(data: Data[], expected: Pattern[]): MissingData[];
}

class MLAnomalyDetector implements AnomalyDetector {
  detectOutliers(data: Data[], field: string): Outlier[] {
    const values = data.map(d => d[field]);
    const stats = this.calculateStats(values);

    return data
      .map((item, index) => ({
        item,
        index,
        score: this.outlierScore(item[field], stats)
      }))
      .filter(o => o.score > this.threshold)
      .map(o => ({
        item: o.item,
        field,
        reason: `Value ${o.item[field]} deviates significantly from mean ${stats.mean}`
      }));
  }

  detectInconsistencies(data: Data[]): Inconsistency[] {
    // Find format inconsistencies
    const formats = this.inferFormats(data);

    return data
      .filter(item => !this.matchesExpectedFormat(item, formats))
      .map(item => ({
        item,
        reason: 'Format differs from majority pattern',
        suggestion: this.suggestFormatCorrection(item, formats)
      }));
  }
}

interface CompletenessChecker {
  checkCompleteness(data: Data[], schema: Schema): CompletenessReport;
  identifyMissing(data: Data[]): MissingField[];
  suggestRecovery(missing: MissingField[]): RecoveryStrategy[];
}

// Verify data completeness
const checker = new CompletenessChecker();
const report = checker.checkCompleteness(extractedProducts, ProductSchema);

if (report.completeness < 0.95) {
  // Less than 95% complete
  const missing = checker.identifyMissing(extractedProducts);
  const strategies = checker.suggestRecovery(missing);

  // Attempt to recover missing data
  for (const strategy of strategies) {
    await this.attemptRecovery(strategy);
  }
}

interface DuplicationDetector {
  findDuplicates(data: Data[], similarity: number): DuplicateGroup[];
  mergeDuplicates(group: DuplicateGroup): Data;
  resolveConflicts(duplicates: Data[]): Data;
}

class FuzzyDuplicationDetector implements DuplicationDetector {
  findDuplicates(data: Data[], similarity = 0.85): DuplicateGroup[] {
    const groups: DuplicateGroup[] = [];

    for (let i = 0; i < data.length; i++) {
      const group = [data[i]];

      for (let j = i + 1; j < data.length; j++) {
        const score = this.similarityScore(data[i], data[j]);
        if (score >= similarity) {
          group.push(data[j]);
        }
      }

      if (group.length > 1) {
        groups.push({ items: group, similarity });
      }
    }

    return groups;
  }

  mergeDuplicates(group: DuplicateGroup): Data {
    // Merge duplicates, preferring most complete and recent data
    const merged = {};

    for (const item of group.items) {
      for (const [key, value] of Object.entries(item)) {
        if (!merged[key] || this.isMoreReliable(value, merged[key])) {
          merged[key] = value;
        }
      }
    }

    return merged;
  }
}

interface ComplianceChecker {
  checkRobotsTxt(url: string): Promise<RobotsDirective>;
  respectRateLimit(domain: string): Promise<void>;
  honorDoNotTrack(): boolean;
  validateLegalBasis(scrape: ScrapeConfig): ComplianceReport;
}

class EthicalScrapingGuard implements ComplianceChecker {
  async checkRobotsTxt(url: string): Promise<RobotsDirective> {
    const robotsUrl = new URL('/robots.txt', url).href;
    const robots = await this.fetchAndParse(robotsUrl);

    return {
      allowed: robots.isAllowed(url, this.userAgent),
      crawlDelay: robots.getCrawlDelay(this.userAgent),
      restrictions: robots.getRestrictions(this.userAgent)
    };
  }

  async respectRateLimit(domain: string): Promise<void> {
    const lastRequest = this.getLastRequestTime(domain);
    const crawlDelay = this.getCrawlDelay(domain) || 1000;

    const elapsed = Date.now() - lastRequest;
    if (elapsed < crawlDelay) {
      await this.delay(crawlDelay - elapsed);
    }

    this.setLastRequestTime(domain, Date.now());
  }

  validateLegalBasis(scrape: ScrapeConfig): ComplianceReport {
    const checks = [
      this.isPublicData(scrape),
      this.hasLegitimateInterest(scrape),
      this.respectsTermsOfService(scrape),
      this.doesNotCircumventProtection(scrape),
      this.anonymizesPersonalData(scrape)
    ];

    return {
      compliant: checks.every(c => c.passed),
      issues: checks.filter(c => !c.passed),
      recommendations: this.generateRecommendations(checks)
    };
  }
}

interface PrivacyProtection {
  anonymizeData(data: Data[]): AnonymizedData[];
  detectPersonalInfo(text: string): PersonalInfo[];
  applyDataMinimization(data: Data[], necessary: string[]): MinimizedData[];
  implementRetentionPolicy(data: Data[], policy: RetentionPolicy): void;
}

class PrivacyGuard implements PrivacyProtection {
  anonymizeData(data: Data[]): AnonymizedData[] {
    return data.map(item => {
      const anonymized = { ...item };

      // Remove or hash personal identifiers
      if (anonymized.email) {
        anonymized.email = this.hashEmail(anonymized.email);
      }

      if (anonymized.phone) {
        delete anonymized.phone;
      }

      if (anonymized.ip) {
        anonymized.ip = this.anonymizeIP(anonymized.ip);
      }

      return anonymized;
    });
  }

  detectPersonalInfo(text: string): PersonalInfo[] {
    const detectors = [
      this.emailDetector,
      this.phoneDetector,
      this.ssnDetector,
      this.creditCardDetector
    ];

    return detectors.flatMap(detector => detector.find(text));
  }
}

const ethicalScraper = new EthicalScraper({
  userAgent: 'ResearchBot/1.0 (+https://example.com/bot)',
  respectRobotsTxt: true,
  rateLimit: {
    requestsPerSecond: 1,
    burstSize: 3
  },
  privacy: {
    excludePersonalData: true,
    anonymizeResults: true
  },
  compliance: {
    checkTermsOfService: true,
    validateLegalBasis: true
  }
});

interface ConcurrentScraper {
  scrapeMany(urls: string[], concurrency: number): Promise<Data[]>;
  manageConcurrency(limit: number): ConcurrencyManager;
  balanceLoad(tasks: Task[]): LoadBalance;
}

class OptimizedConcurrentScraper implements ConcurrentScraper {
  async scrapeMany(urls: string[], concurrency: number): Promise<Data[]> {
    const queue = new PQueue({ concurrency });
    const results: Data[] = [];

    // Group URLs by domain to respect per-domain rate limits
    const byDomain = this.groupByDomain(urls);

    for (const [domain, domainUrls] of byDomain) {
      const domainLimit = this.getDomainLimit(domain);
      const domainQueue = new PQueue({ concurrency: domainLimit });

      for (const url of domainUrls) {
        queue.add(() =>
          domainQueue.add(() => this.scrapeOne(url))
        );
      }
    }

    await queue.onIdle();
    return results;
  }
}

interface CacheStrategy {
  getCached(key: string): Promise<CachedData | null>;
  setCached(key: string, data: Data, ttl: number): Promise<void>;
  invalidateCache(pattern: string): Promise<void>;
  predictCacheHit(key: string): number;
}

class AdaptiveCacheStrategy implements CacheStrategy {
  async getCached(key: string): Promise<CachedData | null> {
    const cached = await this.cache.get(key);

    if (!cached) return null;

    // Check if cached data is still fresh
    const freshness = this.calculateFreshness(cached);

    if (freshness < this.freshnessThreshold) {
      // Cached data is stale
      return null;
    }

    // Update cache statistics for adaptive TTL
    this.updateCacheStats(key, 'hit');

    return cached;
  }

  async setCached(key: string, data: Data, baseTtl: number): Promise<void> {
    // Adapt TTL based on historical update frequency
    const updateFrequency = this.getUpdateFrequency(key);
    const adaptiveTtl = this.calculateAdaptiveTTL(baseTtl, updateFrequency);

    await this.cache.set(key, {
      data,
      timestamp: Date.now(),
      ttl: adaptiveTtl
    });
  }
}

interface ResourceManager {
  manageBrowserInstances(count: number): BrowserPool;
  optimizeMemory(): Promise<void>;
  monitorResources(): ResourceMetrics;
  cleanupResources(): Promise<void>;
}

class BrowserPool implements ResourceManager {
  private browsers: Browser[] = [];
  private maxBrowsers: number = 5;

  async manageBrowserInstances(count: number): Promise<Browser> {
    // Reuse existing browsers when possible
    const available = this.browsers.find(b => !b.busy);

    if (available) {
      return available;
    }

    // Create new browser if under limit
    if (this.browsers.length < this.maxBrowsers) {
      const browser = await this.createOptimizedBrowser();
      this.browsers.push(browser);
      return browser;
    }

    // Wait for browser to become available
    return this.waitForAvailable();
  }

  private async createOptimizedBrowser(): Promise<Browser> {
    return await puppeteer.launch({
      headless: true,
      args: [
        '--disable-dev-shm-usage',
        '--disable-setuid-sandbox',
        '--no-sandbox',
        '--disable-gpu',
        '--disable-software-rasterizer',
        '--disable-extensions',
        '--disable-images', // Don't load images unless needed
        '--disable-javascript-harmony', // Reduce memory
      ]
    });
  }

  async optimizeMemory(): Promise<void> {
    // Close idle browsers
    const idleTime = 5 * 60 * 1000; // 5 minutes

    for (const browser of this.browsers) {
      if (Date.now() - browser.lastUsed > idleTime) {
        await browser.close();
        this.browsers = this.browsers.filter(b => b !== browser);
      }
    }

    // Clear caches
    for (const browser of this.browsers) {
      const pages = await browser.pages();
      for (const page of pages) {
        await page.evaluate(() => {
          if (window.gc) window.gc();
        });
      }
    }
  }
}

interface NetworkOptimizer {
  blockUnnecessaryResources(types: ResourceType[]): void;
  compressRequests(): void;
  optimizeHeaders(headers: Headers): Headers;
  enableHTTP2(): void;
}

class ScrapingNetworkOptimizer implements NetworkOptimizer {
  blockUnnecessaryResources(types: ResourceType[]): void {
    page.on('request', (request) => {
      // Block images, fonts, stylesheets if not needed for extraction
      if (types.includes(request.resourceType())) {
        request.abort();
      } else {
        request.continue();
      }
    });
  }

  optimizeHeaders(headers: Headers): Headers {
    return {
      ...headers,
      'Accept-Encoding': 'gzip, deflate, br', // Enable compression
      'Accept': 'text/html,application/json', // Only accept needed types
      'Connection': 'keep-alive', // Reuse connections
      'Cache-Control': 'max-age=3600' // Allow caching
    };
  }
}

interface PriceMonitor {
  trackProducts(products: Product[]): Promise<void>;
  detectPriceChanges(): Promise<PriceChange[]>;
  alertOnThreshold(threshold: number): Promise<Alert[]>;
}

class AIPriceMonitor implements PriceMonitor {
  async trackProducts(products: Product[]): Promise<void> {
    const scraper = new AIWebScraper();

    for (const product of products) {
      const result = await scraper.extract({
        url: product.url,
        instruction: "Extract current price, original price, discount percentage, and availability",
        schema: PriceSchema
      });

      // Store in time-series database
      await this.storePricePoint({
        productId: product.id,
        timestamp: Date.now(),
        ...result.data
      });

      // Detect significant changes
      const changes = await this.detectPriceChanges();

      if (changes.length > 0) {
        await this.notifyChanges(changes);
      }
    }
  }

  async detectPriceChanges(): Promise<PriceChange[]> {
    // AI detects meaningful price changes vs noise
    const recent = await this.getRecentPrices(24); // Last 24 hours
    const baseline = await this.getBaselinePrice(30); // 30 day average

    return recent
      .filter(price => {
        const change = Math.abs(price.value - baseline) / baseline;
        return change > 0.05; // 5% threshold
      })
      .map(price => ({
        product: price.productId,
        previous: baseline,
        current: price.value,
        change: ((price.value - baseline) / baseline) * 100,
        timestamp: price.timestamp
      }));
  }
}

interface ContentAggregator {
  aggregateFrom(sources: Source[]): Promise<AggregatedContent>;
  deduplicateContent(content: Content[]): Content[];
  enrichContent(content: Content): Promise<EnrichedContent>;
}

class AIContentAggregator implements ContentAggregator {
  async aggregateFrom(sources: Source[]): Promise<AggregatedContent> {
    const scraper = new AIWebScraper();
    const allContent: Content[] = [];

    // Scrape all sources concurrently
    const results = await Promise.all(
      sources.map(source =>
        scraper.extract({
          url: source.url,
          instruction: source.extractionTemplate,
          schema: source.schema
        })
      )
    );

    // Flatten and deduplicate
    const flattened = results.flatMap(r => r.data);
    const deduplicated = this.deduplicateContent(flattened);

    // Enrich with additional data
    const enriched = await Promise.all(
      deduplicated.map(item => this.enrichContent(item))
    );

    return {
      content: enriched,
      sources: sources.map(s => s.name),
      timestamp: Date.now(),
      count: enriched.length
    };
  }

  deduplicateContent(content: Content[]): Content[] {
    // Use semantic similarity instead of exact matching
    const deduper = new SemanticDeduplicator();
    return deduper.deduplicate(content, 0.9); // 90% similarity threshold
  }
}

interface LeadGenerator {
  extractLeads(sources: string[]): Promise<Lead[]>;
  qualifyLeads(leads: Lead[]): Promise<QualifiedLead[]>;
  enrichLeadData(lead: Lead): Promise<EnrichedLead>;
}

class AILeadGenerator implements LeadGenerator {
  async extractLeads(sources: string[]): Promise<Lead[]> {
    const scraper = new AIWebScraper();
    const leads: Lead[] = [];

    for (const source of sources) {
      const result = await scraper.extract({
        url: source,
        instruction: `
          Extract company information including:
          - Company name
          - Website URL
          - Industry/category
          - Contact email if available
          - Company size indicators
          - Location
        `,
        schema: LeadSchema,
        pagination: true
      });

      leads.push(...result.data);
    }

    return leads;
  }

  async qualifyLeads(leads: Lead[]): Promise<QualifiedLead[]> {
    // AI scoring based on criteria
    const scorer = new LeadScoringModel();

    return Promise.all(
      leads.map(async lead => {
        const enriched = await this.enrichLeadData(lead);
        const score = await scorer.score(enriched);

        return {
          ...enriched,
          score,
          qualified: score > this.qualificationThreshold
        };
      })
    );
  }
}

// Example: Building with modern tools
import { chromium } from 'playwright';
import { z } from 'zod';
import { Pipeline } from 'transformers';

class ModernAIScraper {
  private browser: Browser;
  private nlp: Pipeline;

  async initialize() {
    this.browser = await chromium.launch();
    this.nlp = await pipeline('text-classification');
  }

  async scrape(url: string, instruction: string) {
    const page = await this.browser.newPage();
    await page.goto(url);

    // Use AI to understand instruction
    const intent = await this.nlp(instruction);

    // Extract based on intent
    const data = await this.extractByIntent(page, intent);

    return data;
  }
}

// Future: Vision transformer-based scraping
const visionScraper = new VisionTransformerScraper();

const result = await visionScraper.extract({
  url: targetUrl,
  query: "Find all product cards and extract their details",
  // Model understands visual layout without DOM analysis
});

interface MultimodalScraper {
  extractTextAndImages(url: string): Promise<MultimodalData>;
  analyzeVisualContent(image: Image): Promise<ImageAnalysis>;
  transcribeAudio(audio: AudioSource): Promise<Transcript>;
}

// Extract product info from images when text extraction fails
const product = await multimodalScraper.extractTextAndImages(productUrl);

if (!product.description) {
  // Extract text from product images using OCR and vision models
  product.description = await multimodalScraper.analyzeVisualContent(
    product.images[0]
  );
}