feat: Enhance error handling and logging in expense and interest duplicate detection, add retry logic for document deletion, and improve PDF generation with detailed receipt processing

server/api/interests/duplicates/find.ts

@@ -86,6 +86,9 @@ export default defineEventHandler(async (event) => {
  * Find duplicate interests based on multiple criteria
  */
 function findDuplicateInterests(interests: any[], threshold: number = 0.8) {
+  console.log('[INTERESTS] Starting duplicate detection with threshold:', threshold);
+  console.log('[INTERESTS] Total interests to analyze:', interests.length);
+  
   const duplicateGroups: Array<{
     id: string;
     interests: any[];
@@ -95,6 +98,7 @@ function findDuplicateInterests(interests: any[], threshold: number = 0.8) {
   }> = [];
 
   const processedIds = new Set<number>();
+  let comparisons = 0;
 
   for (let i = 0; i < interests.length; i++) {
     const interest1 = interests[i];
@@ -109,14 +113,21 @@ function findDuplicateInterests(interests: any[], threshold: number = 0.8) {
       if (processedIds.has(interest2.Id)) continue;
 
       const similarity = calculateSimilarity(interest1, interest2);
+      comparisons++;
+      
+      console.log(`[INTERESTS] Comparing ${interest1.Id} vs ${interest2.Id}: score=${similarity.score.toFixed(3)}, threshold=${threshold}`);
       
       if (similarity.score >= threshold) {
+        console.log(`[INTERESTS] MATCH FOUND! ${interest1.Id} vs ${interest2.Id} (score: ${similarity.score.toFixed(3)})`);
+        console.log('[INTERESTS] Match details:', similarity.details);
         matches.push(interest2);
         processedIds.add(interest2.Id);
       }
     }
 
     if (matches.length > 1) {
+      console.log(`[INTERESTS] Creating duplicate group with ${matches.length} matches`);
+      
       // Mark all as processed
       matches.forEach(match => processedIds.add(match.Id));
 
@@ -138,6 +149,7 @@ function findDuplicateInterests(interests: any[], threshold: number = 0.8) {
     }
   }
 
+  console.log(`[INTERESTS] Completed ${comparisons} comparisons, found ${duplicateGroups.length} duplicate groups`);
   return duplicateGroups;
 }
 
@@ -146,37 +158,68 @@ function findDuplicateInterests(interests: any[], threshold: number = 0.8) {
  */
 function calculateSimilarity(interest1: any, interest2: any) {
   const scores: Array<{ type: string; score: number; weight: number }> = [];
+  
+  console.log(`[INTERESTS] Calculating similarity between:`, {
+    id1: interest1.Id,
+    name1: interest1['Full Name'],
+    email1: interest1['Email Address'],
+    phone1: interest1['Phone Number'],
+    id2: interest2.Id,
+    name2: interest2['Full Name'],
+    email2: interest2['Email Address'],
+    phone2: interest2['Phone Number']
+  });
 
-  // Email similarity (highest weight)
+  // Email similarity (highest weight) - exact match required
   if (interest1['Email Address'] && interest2['Email Address']) {
-    const emailScore = normalizeEmail(interest1['Email Address']) === normalizeEmail(interest2['Email Address']) ? 1.0 : 0.0;
-    scores.push({ type: 'email', score: emailScore, weight: 0.4 });
+    const email1 = normalizeEmail(interest1['Email Address']);
+    const email2 = normalizeEmail(interest2['Email Address']);
+    const emailScore = email1 === email2 ? 1.0 : 0.0;
+    scores.push({ type: 'email', score: emailScore, weight: 0.5 });
+    console.log(`[INTERESTS] Email comparison: "${email1}" vs "${email2}" = ${emailScore}`);
   }
 
-  // Phone similarity
+  // Phone similarity - exact match on normalized numbers
   if (interest1['Phone Number'] && interest2['Phone Number']) {
     const phone1 = normalizePhone(interest1['Phone Number']);
     const phone2 = normalizePhone(interest2['Phone Number']);
-    const phoneScore = phone1 === phone2 ? 1.0 : 0.0;
-    scores.push({ type: 'phone', score: phoneScore, weight: 0.3 });
+    const phoneScore = phone1 === phone2 && phone1.length >= 8 ? 1.0 : 0.0; // Require at least 8 digits
+    scores.push({ type: 'phone', score: phoneScore, weight: 0.4 });
+    console.log(`[INTERESTS] Phone comparison: "${phone1}" vs "${phone2}" = ${phoneScore}`);
   }
 
-  // Name similarity
+  // Name similarity - fuzzy matching
   if (interest1['Full Name'] && interest2['Full Name']) {
     const nameScore = calculateNameSimilarity(interest1['Full Name'], interest2['Full Name']);
-    scores.push({ type: 'name', score: nameScore, weight: 0.2 });
+    scores.push({ type: 'name', score: nameScore, weight: 0.3 });
+    console.log(`[INTERESTS] Name comparison: "${interest1['Full Name']}" vs "${interest2['Full Name']}" = ${nameScore.toFixed(3)}`);
   }
 
   // Address similarity
   if (interest1.Address && interest2.Address) {
     const addressScore = calculateStringSimilarity(interest1.Address, interest2.Address);
-    scores.push({ type: 'address', score: addressScore, weight: 0.1 });
+    scores.push({ type: 'address', score: addressScore, weight: 0.2 });
+    console.log(`[INTERESTS] Address comparison: ${addressScore.toFixed(3)}`);
   }
 
-  // Calculate weighted average
+  // Special case: if we have exact email OR phone match, give high score regardless of other fields
+  const hasExactEmailMatch = scores.find(s => s.type === 'email' && s.score === 1.0);
+  const hasExactPhoneMatch = scores.find(s => s.type === 'phone' && s.score === 1.0);
+  
+  if (hasExactEmailMatch || hasExactPhoneMatch) {
+    console.log('[INTERESTS] Exact email or phone match found - high confidence');
+    return {
+      score: 0.95, // High confidence for exact email/phone match
+      details: scores
+    };
+  }
+
+  // Calculate weighted average for other cases
   const totalWeight = scores.reduce((sum, s) => sum + s.weight, 0);
   const weightedScore = scores.reduce((sum, s) => sum + (s.score * s.weight), 0) / (totalWeight || 1);
 
+  console.log(`[INTERESTS] Weighted score: ${weightedScore.toFixed(3)} (weights: ${totalWeight})`);
+
   return {
     score: weightedScore,
     details: scores