huhuih

hzgjuigik

server/services/ai-sorter.mjs

@@ -234,6 +234,121 @@ export class AISorterService {
     return null
   }
 
+  /**
+   * Generate suggested rules based on email patterns
+   * Analyzes email samples to detect patterns and suggest rules
+   */
+  async generateSuggestedRules(userId, emailSamples) {
+    if (!emailSamples || emailSamples.length === 0) {
+      return []
+    }
+
+    const suggestions = []
+    const senderCounts = {}
+    const domainCounts = {}
+    const subjectPatterns = {}
+    const categoryPatterns = {}
+
+    // Analyze patterns
+    for (const email of emailSamples) {
+      const from = email.from?.toLowerCase() || ''
+      const subject = email.subject?.toLowerCase() || ''
+      
+      // Extract domain
+      const emailMatch = from.match(/@([^\s>]+)/)
+      if (emailMatch) {
+        const domain = emailMatch[1].toLowerCase()
+        domainCounts[domain] = (domainCounts[domain] || 0) + 1
+      }
+
+      // Count senders
+      const senderEmail = from.split('<')[1]?.split('>')[0] || from
+      senderCounts[senderEmail] = (senderCounts[senderEmail] || 0) + 1
+
+      // Detect category patterns
+      const category = email.category || 'review'
+      categoryPatterns[category] = (categoryPatterns[category] || 0) + 1
+    }
+
+    const totalEmails = emailSamples.length
+    const threshold = Math.max(3, Math.ceil(totalEmails * 0.1)) // At least 3 emails or 10% of total
+
+    // Suggest VIP senders (frequent senders)
+    const frequentSenders = Object.entries(senderCounts)
+      .filter(([_, count]) => count >= threshold)
+      .sort(([_, a], [__, b]) => b - a)
+      .slice(0, 3)
+
+    for (const [sender, count] of frequentSenders) {
+      suggestions.push({
+        type: 'vip_sender',
+        name: `Mark ${sender.split('@')[0]} as VIP`,
+        description: `${count} emails from this sender`,
+        confidence: Math.min(0.9, count / totalEmails),
+        action: {
+          type: 'add_vip',
+          email: sender,
+        },
+      })
+    }
+
+    // Suggest company labels (frequent domains)
+    const frequentDomains = Object.entries(domainCounts)
+      .filter(([domain, count]) => count >= threshold && !KNOWN_COMPANIES[domain])
+      .sort(([_, a], [__, b]) => b - a)
+      .slice(0, 3)
+
+    for (const [domain, count] of frequentDomains) {
+      const companyName = domain.split('.')[0].charAt(0).toUpperCase() + domain.split('.')[0].slice(1)
+      suggestions.push({
+        type: 'company_label',
+        name: `Label ${companyName} emails`,
+        description: `${count} emails from ${domain}`,
+        confidence: Math.min(0.85, count / totalEmails),
+        action: {
+          type: 'add_company_label',
+          name: companyName,
+          condition: `from:${domain}`,
+          category: 'promotions', // Default, user can change
+        },
+      })
+    }
+
+    // Suggest category-specific rules based on patterns
+    if (categoryPatterns.newsletters >= threshold) {
+      suggestions.push({
+        type: 'category_rule',
+        name: 'Archive newsletters automatically',
+        description: `${categoryPatterns.newsletters} newsletter emails detected`,
+        confidence: 0.8,
+        action: {
+          type: 'enable_category',
+          category: 'newsletters',
+          action: 'archive_read',
+        },
+      })
+    }
+
+    if (categoryPatterns.promotions >= threshold) {
+      suggestions.push({
+        type: 'category_rule',
+        name: 'Archive promotions automatically',
+        description: `${categoryPatterns.promotions} promotion emails detected`,
+        confidence: 0.75,
+        action: {
+          type: 'enable_category',
+          category: 'promotions',
+          action: 'archive_read',
+        },
+      })
+    }
+
+    // Sort by confidence and return top 5
+    return suggestions
+      .sort((a, b) => b.confidence - a.confidence)
+      .slice(0, 5)
+  }
+
   /**
    * Check if email matches a company label condition
    */