Add multiple admin improvements and bug fixes

- Email settings: Add separate sender display name field
- Rounds page: Drag-and-drop reordering with visible order numbers
- Round creation: Auto-assign projects to filtering rounds, auto-activate if voting started
- Round detail: Fix incorrect "voting period ended" message for draft rounds
- Projects page: Add delete option with confirmation dialog
- AI filtering: Add configurable batch size and parallel request settings
- Filtering results: Fix duplicate criteria display
- Add seed scripts for notification settings and MOPC onboarding form

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

src/server/services/ai-filtering.ts

@@ -72,6 +72,9 @@ export type DocumentCheckConfig = {
 export type AIScreeningConfig = {
   criteriaText: string
   action: 'FLAG' // AI screening always flags for human review
+  // Performance settings
+  batchSize?: number // Projects per API call (1-50, default 20)
+  parallelBatches?: number // Concurrent API calls (1-10, default 1)
 }
 
 export type RuleConfig = FieldRuleConfig | DocumentCheckConfig | AIScreeningConfig
@@ -124,7 +127,11 @@ interface FilteringRuleInput {
 
 // ─── Constants ───────────────────────────────────────────────────────────────
 
-const BATCH_SIZE = 20
+const DEFAULT_BATCH_SIZE = 20
+const MAX_BATCH_SIZE = 50
+const MIN_BATCH_SIZE = 1
+const DEFAULT_PARALLEL_BATCHES = 1
+const MAX_PARALLEL_BATCHES = 10
 
 // Optimized system prompt (compressed for token efficiency)
 const AI_SCREENING_SYSTEM_PROMPT = `Project screening assistant. Evaluate against criteria, return JSON.
@@ -441,7 +448,18 @@ export async function executeAIScreening(
     }
 
     const model = await getConfiguredModel()
-    console.log(`[AI Filtering] Using model: ${model} for ${projects.length} projects`)
+
+    // Get batch settings from config
+    const batchSize = Math.min(
+      MAX_BATCH_SIZE,
+      Math.max(MIN_BATCH_SIZE, config.batchSize ?? DEFAULT_BATCH_SIZE)
+    )
+    const parallelBatches = Math.min(
+      MAX_PARALLEL_BATCHES,
+      Math.max(1, config.parallelBatches ?? DEFAULT_PARALLEL_BATCHES)
+    )
+
+    console.log(`[AI Filtering] Using model: ${model} for ${projects.length} projects (batch size: ${batchSize}, parallel: ${parallelBatches})`)
 
     // Convert and anonymize projects
     const projectsWithRelations = projects.map(toProjectWithRelations)
@@ -454,39 +472,56 @@ export async function executeAIScreening(
     }
 
     let totalTokens = 0
-    const totalBatches = Math.ceil(anonymized.length / BATCH_SIZE)
+    const totalBatches = Math.ceil(anonymized.length / batchSize)
+    let processedBatches = 0
 
-    // Process in batches
-    for (let i = 0; i < anonymized.length; i += BATCH_SIZE) {
-      const batchAnon = anonymized.slice(i, i + BATCH_SIZE)
-      const batchMappings = mappings.slice(i, i + BATCH_SIZE)
-      const currentBatch = Math.floor(i / BATCH_SIZE) + 1
+    // Create batch chunks for parallel processing
+    const batches: Array<{ anon: typeof anonymized; maps: typeof mappings; index: number }> = []
+    for (let i = 0; i < anonymized.length; i += batchSize) {
+      batches.push({
+        anon: anonymized.slice(i, i + batchSize),
+        maps: mappings.slice(i, i + batchSize),
+        index: batches.length,
+      })
+    }
 
-      console.log(`[AI Filtering] Processing batch ${currentBatch}/${totalBatches}`)
+    // Process batches in parallel chunks
+    for (let i = 0; i < batches.length; i += parallelBatches) {
+      const parallelChunk = batches.slice(i, i + parallelBatches)
 
-      const { results: batchResults, tokensUsed } = await processAIBatch(
-        openai,
-        model,
-        config.criteriaText,
-        batchAnon,
-        batchMappings,
-        userId,
-        entityId
-      )
+      console.log(`[AI Filtering] Processing batches ${i + 1}-${Math.min(i + parallelBatches, batches.length)} of ${totalBatches} (${parallelChunk.length} in parallel)`)
 
-      totalTokens += tokensUsed
+      // Run parallel batches concurrently
+      const batchPromises = parallelChunk.map(async (batch) => {
+        const { results: batchResults, tokensUsed } = await processAIBatch(
+          openai,
+          model,
+          config.criteriaText,
+          batch.anon,
+          batch.maps,
+          userId,
+          entityId
+        )
+        return { batchResults, tokensUsed, index: batch.index }
+      })
 
-      // Merge batch results
-      for (const [id, result] of batchResults) {
-        results.set(id, result)
+      const parallelResults = await Promise.all(batchPromises)
+
+      // Merge results from all parallel batches
+      for (const { batchResults, tokensUsed } of parallelResults) {
+        totalTokens += tokensUsed
+        for (const [id, result] of batchResults) {
+          results.set(id, result)
+        }
+        processedBatches++
       }
 
-      // Report progress
+      // Report progress after each parallel chunk
       if (onProgress) {
         await onProgress({
-          currentBatch,
+          currentBatch: processedBatches,
           totalBatches,
-          processedCount: Math.min((currentBatch) * BATCH_SIZE, anonymized.length),
+          processedCount: Math.min(processedBatches * batchSize, anonymized.length),
           tokensUsed: totalTokens,
         })
       }