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Add MCP server and enhance AI content tools
Implement self-hosted MCP server for admin actions, update AI generation tools in admin dashboard, and refine mobile app stores and components.
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@@ -39,47 +39,68 @@ export async function reviewGeneratedQuestions(
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intensity: q.intensity,
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location_type: q.location_type,
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effort_level: q.effort_level,
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// Include targeting for review (may not always be present)
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allowed_couple_genders: (q as { allowed_couple_genders?: string[] | null }).allowed_couple_genders || null,
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target_user_genders: (q as { target_user_genders?: string[] | null }).target_user_genders || null,
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}));
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const toneDescription = TONE_LEVELS.find(t => t.level === packContext.tone)?.label || 'Unknown';
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const prompt = `You are a quality reviewer for a couples intimacy app. Review each generated question against our guidelines.
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const prompt = `<task>
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Review each generated question against our quality guidelines.
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</task>
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PACK CONTEXT:
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<pack_context>
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- Pack Name: "${packContext.name}"
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${packContext.description ? `- Pack Description: "${packContext.description}"` : ''}
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- Content Type: ${packContext.isExplicit ? 'EXPLICIT (adult content allowed)' : 'NON-EXPLICIT (clean/romantic only)'}
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- Tone Level: ${packContext.tone} (${toneDescription})
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</pack_context>
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<scoring_criteria>
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${REVIEW_GUIDELINES}
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</scoring_criteria>
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VERDICT RULES:
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<verdict_rules>
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- PASS: All scores >= 7, no major issues
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- FLAG: Any score 5-7, or minor issues worth noting (admin should review but can use)
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- REJECT: Any score < 5, or major violations (mixed anatomy, wrong intensity, severe guideline violation)
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</verdict_rules>
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QUESTIONS TO REVIEW:
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<questions_to_review>
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${JSON.stringify(questionsForReview, null, 2)}
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</questions_to_review>
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Return a JSON object with:
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<output_format>
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{
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"reviews": [
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{
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"index": 0,
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"verdict": "pass" | "flag" | "reject",
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"issues": ["Issue 1", "Issue 2"],
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"suggestions": "Optional improvement suggestion",
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"index": number, // 0-based index of the question
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"verdict": "pass"|"flag"|"reject",
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"issues": string[], // List of issues found, empty if none
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"suggestions": string|null, // Optional improvement suggestion
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"scores": {
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"guidelineCompliance": 8,
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"creativity": 7,
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"clarity": 9,
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"intensityAccuracy": 8
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}
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"guidelineCompliance": 1-10, // Criterion 1
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"creativity": 1-10, // Criterion 2
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"clarity": 1-10, // Criterion 3
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"intensityAccuracy": 1-10, // Criterion 4
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"anatomicalConsistency": 1-10, // Criterion 5
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"partnerTextQuality": 1-10, // Criterion 6 (10 if no partner_text needed)
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"coupleTargeting": 1-10, // Criterion 7
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"initiatorTargeting": 1-10 // Criterion 8 (10 if symmetric)
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},
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"intensitySuggestion": number|null, // Suggested intensity (1-5) if current seems wrong, null if correct
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"targetingSuggestions": { // Only include if targeting needs changes
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"suggestedCoupleTargets": string[]|null, // ["male+male", "female+male", "female+female"] or subset
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"suggestedInitiator": string[]|null, // ["male", "female"] or null for any
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"reason": string // Why targeting should change
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}|null
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}
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]
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}
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Review ALL questions. Be thorough but fair - only flag/reject for genuine issues.`;
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Review ALL questions against ALL 8 criteria. Be thorough but fair - only flag/reject for genuine issues.
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</output_format>`;
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const response = await openai.chat.completions.create({
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model: config.reviewerModel,
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@@ -91,7 +112,7 @@ Review ALL questions. Be thorough but fair - only flag/reject for genuine issues
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{ role: 'user', content: prompt },
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],
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response_format: { type: 'json_object' },
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temperature: 0.3,
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temperature: config.reviewerTemperature ?? 0.3,
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});
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const content = response.choices[0].message.content;
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@@ -111,19 +132,31 @@ Review ALL questions. Be thorough but fair - only flag/reject for genuine issues
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const flagged = reviews.filter(r => r.verdict === 'flag').length;
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const rejected = reviews.filter(r => r.verdict === 'reject').length;
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// Calculate overall quality as average of all scores
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let totalScore = 0;
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let scoreCount = 0;
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// Calculate overall quality as weighted average of all 8 scores
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// Weights: guidelineCompliance 20%, creativity 15%, clarity 15%, intensityAccuracy 15%,
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// anatomicalConsistency 15%, partnerTextQuality 10%, coupleTargeting 5%, initiatorTargeting 5%
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let totalWeightedScore = 0;
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let totalWeight = 0;
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const weights = {
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guidelineCompliance: 0.20,
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creativity: 0.15,
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clarity: 0.15,
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intensityAccuracy: 0.15,
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anatomicalConsistency: 0.15,
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partnerTextQuality: 0.10,
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coupleTargeting: 0.05,
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initiatorTargeting: 0.05,
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};
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for (const review of reviews) {
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if (review.scores) {
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totalScore += review.scores.guidelineCompliance || 0;
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totalScore += review.scores.creativity || 0;
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totalScore += review.scores.clarity || 0;
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totalScore += review.scores.intensityAccuracy || 0;
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scoreCount += 4;
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for (const [key, weight] of Object.entries(weights)) {
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const score = review.scores[key as keyof typeof review.scores] || 0;
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totalWeightedScore += score * weight;
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totalWeight += weight;
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}
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}
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}
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const overallQuality = scoreCount > 0 ? Math.round((totalScore / scoreCount) * 10) / 10 : 0;
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const overallQuality = totalWeight > 0 ? Math.round((totalWeightedScore / (totalWeight / Object.keys(weights).length)) * 10) / 10 : 0;
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return {
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reviews,
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@@ -168,54 +201,77 @@ export async function selectBestGeneration(
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text: q.text,
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partner_text: q.partner_text || null,
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intensity: q.intensity,
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allowed_couple_genders: (q as { allowed_couple_genders?: string[] | null }).allowed_couple_genders || null,
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target_user_genders: (q as { target_user_genders?: string[] | null }).target_user_genders || null,
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})),
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}));
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const prompt = `You are a quality reviewer for a couples intimacy app. You have received ${candidates.length} different sets of generated questions from different AI models. Your task is to:
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const prompt = `<task>
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Compare ${candidates.length} different sets of generated questions from different AI models. Select the best set and review it in detail.
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</task>
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<objectives>
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1. COMPARE all sets and SELECT the BEST one overall
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2. REVIEW the selected set in detail
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</objectives>
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PACK CONTEXT:
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<pack_context>
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- Pack Name: "${packContext.name}"
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${packContext.description ? `- Pack Description: "${packContext.description}"` : ''}
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- Content Type: ${packContext.isExplicit ? 'EXPLICIT (adult content allowed)' : 'NON-EXPLICIT (clean/romantic only)'}
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- Tone Level: ${packContext.tone} (${toneDescription})
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</pack_context>
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<scoring_criteria>
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${REVIEW_GUIDELINES}
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</scoring_criteria>
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CANDIDATE SETS:
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<candidate_sets>
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${JSON.stringify(candidatesForReview, null, 2)}
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</candidate_sets>
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SELECTION CRITERIA (in order of importance):
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1. Overall quality and creativity of questions
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2. Variety and uniqueness within the set
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3. Adherence to pack theme and tone
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4. Proper intensity grading
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5. Correct use of "your partner" language
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6. Quality of partner_text for asymmetric questions
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<selection_criteria>
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Ranked by importance (approximate weights):
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1. Overall quality and creativity of questions (30%)
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2. Variety and uniqueness within the set (20%)
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3. Adherence to pack theme and tone (20%)
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4. Proper intensity grading (15%)
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5. Correct use of "your partner" language (10%)
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6. Quality of partner_text for asymmetric questions (5%)
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</selection_criteria>
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Return a JSON object with:
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<output_format>
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{
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"selectedIndex": <0-based index of the best candidate set>,
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"reasoning": "<brief explanation of why this set was chosen>",
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"selectedIndex": number, // 0-based index of the best candidate set
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"reasoning": string, // Brief explanation of why this set was chosen
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"reviews": [
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{
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"index": <question index within selected set>,
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"verdict": "pass" | "flag" | "reject",
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"issues": ["Issue 1", "Issue 2"],
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"suggestions": "Optional improvement suggestion",
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"index": number, // Question index within selected set
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"verdict": "pass"|"flag"|"reject",
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"issues": string[],
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"suggestions": string|null,
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"scores": {
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"guidelineCompliance": 1-10,
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"creativity": 1-10,
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"clarity": 1-10,
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"intensityAccuracy": 1-10
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}
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"intensityAccuracy": 1-10,
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"anatomicalConsistency": 1-10,
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"partnerTextQuality": 1-10,
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"coupleTargeting": 1-10,
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"initiatorTargeting": 1-10
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},
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"intensitySuggestion": number|null,
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"targetingSuggestions": {
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"suggestedCoupleTargets": string[]|null,
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"suggestedInitiator": string[]|null,
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"reason": string
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}|null
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}
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]
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}
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Select the best set and review ALL questions in that set.`;
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Select the best set and review ALL questions in that set against ALL 8 criteria.
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</output_format>`;
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const response = await openai.chat.completions.create({
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model: config.reviewerModel,
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@@ -227,7 +283,7 @@ Select the best set and review ALL questions in that set.`;
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{ role: 'user', content: prompt },
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],
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response_format: { type: 'json_object' },
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temperature: 0.3,
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temperature: config.reviewerTemperature ?? 0.3,
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});
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const content = response.choices[0].message.content;
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@@ -244,23 +300,33 @@ Select the best set and review ALL questions in that set.`;
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const reviews: QuestionReview[] = parsed.reviews || [];
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const reasoning = parsed.reasoning || 'No reasoning provided';
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// Calculate summary
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// Calculate summary with weighted average of all 8 scores
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const passed = reviews.filter(r => r.verdict === 'pass').length;
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const flagged = reviews.filter(r => r.verdict === 'flag').length;
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const rejected = reviews.filter(r => r.verdict === 'reject').length;
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let totalScore = 0;
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let scoreCount = 0;
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let totalWeightedScore = 0;
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let totalWeight = 0;
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const weights = {
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guidelineCompliance: 0.20,
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creativity: 0.15,
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clarity: 0.15,
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intensityAccuracy: 0.15,
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anatomicalConsistency: 0.15,
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partnerTextQuality: 0.10,
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coupleTargeting: 0.05,
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initiatorTargeting: 0.05,
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};
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for (const review of reviews) {
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if (review.scores) {
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totalScore += review.scores.guidelineCompliance || 0;
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totalScore += review.scores.creativity || 0;
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totalScore += review.scores.clarity || 0;
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totalScore += review.scores.intensityAccuracy || 0;
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scoreCount += 4;
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for (const [key, weight] of Object.entries(weights)) {
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const score = review.scores[key as keyof typeof review.scores] || 0;
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totalWeightedScore += score * weight;
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totalWeight += weight;
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}
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}
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}
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const overallQuality = scoreCount > 0 ? Math.round((totalScore / scoreCount) * 10) / 10 : 0;
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const overallQuality = totalWeight > 0 ? Math.round((totalWeightedScore / (totalWeight / Object.keys(weights).length)) * 10) / 10 : 0;
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return {
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selectedIndex,
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