263 lines
9.7 KiB
Python
263 lines
9.7 KiB
Python
#!/usr/bin/env python3
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"""
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Test script for Phase 3 AI Prompt Optimization
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Verifies that the AI Prompt Optimizer is working with advanced prompts and schemas.
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"""
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import asyncio
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import sys
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import os
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from pathlib import Path
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# Add the backend directory to the Python path
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sys.path.append(str(Path(__file__).parent / "backend"))
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from services.ai_prompt_optimizer import AIPromptOptimizer
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from services.content_gap_analyzer.ai_engine_service import AIEngineService
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from loguru import logger
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async def test_ai_prompt_optimizer():
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"""Test the AI Prompt Optimizer functionality."""
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print("🔧 Testing AI Prompt Optimizer...")
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# Initialize the AI Prompt Optimizer
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ai_optimizer = AIPromptOptimizer()
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# Test 1: Strategic Content Gap Analysis
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print("\n📊 Test 1: Strategic Content Gap Analysis")
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analysis_data = {
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'target_url': 'example.com',
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'industry': 'technology',
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'serp_opportunities': 25,
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'expanded_keywords_count': 150,
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'competitors_analyzed': 5,
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'content_quality_score': 8.5,
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'competition_level': 'high',
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'dominant_themes': {
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'artificial_intelligence': 0.3,
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'machine_learning': 0.25,
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'data_science': 0.2,
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'automation': 0.15,
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'innovation': 0.1
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},
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'competitive_landscape': {
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'market_leader': 'competitor1.com',
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'content_leader': 'competitor2.com',
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'quality_leader': 'competitor3.com'
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}
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}
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try:
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result = await ai_optimizer.generate_strategic_content_gap_analysis(analysis_data)
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print(f"✅ Strategic content gap analysis completed")
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print(f" - Strategic insights: {len(result.get('strategic_insights', []))}")
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print(f" - Content recommendations: {len(result.get('content_recommendations', []))}")
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print(f" - Keyword strategy: {bool(result.get('keyword_strategy'))}")
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except Exception as e:
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print(f"❌ Strategic content gap analysis failed: {str(e)}")
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return False
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# Test 2: Advanced Market Position Analysis
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print("\n🏢 Test 2: Advanced Market Position Analysis")
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market_data = {
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'industry': 'technology',
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'competitors': [
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{
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'url': 'competitor1.com',
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'content_score': 8.5,
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'quality_score': 9.0,
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'frequency': 'high'
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},
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{
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'url': 'competitor2.com',
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'content_score': 7.8,
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'quality_score': 8.2,
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'frequency': 'medium'
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}
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],
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'market_size': 'Large',
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'growth_rate': '15%',
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'key_trends': ['AI adoption', 'Cloud migration', 'Digital transformation']
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}
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try:
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result = await ai_optimizer.generate_advanced_market_position_analysis(market_data)
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print(f"✅ Advanced market position analysis completed")
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print(f" - Market leader: {result.get('market_leader', 'N/A')}")
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print(f" - Market gaps: {len(result.get('market_gaps', []))}")
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print(f" - Opportunities: {len(result.get('opportunities', []))}")
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print(f" - Strategic recommendations: {len(result.get('strategic_recommendations', []))}")
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except Exception as e:
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print(f"❌ Advanced market position analysis failed: {str(e)}")
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return False
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# Test 3: Advanced Keyword Analysis
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print("\n🔍 Test 3: Advanced Keyword Analysis")
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keyword_data = {
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'industry': 'technology',
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'target_keywords': ['artificial intelligence', 'machine learning', 'data science'],
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'search_volume_data': {
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'artificial intelligence': 50000,
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'machine learning': 35000,
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'data science': 25000
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},
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'competition_analysis': {
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'artificial intelligence': 'high',
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'machine learning': 'medium',
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'data science': 'low'
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},
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'trend_analysis': {
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'artificial intelligence': 'rising',
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'machine learning': 'stable',
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'data science': 'rising'
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}
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}
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try:
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result = await ai_optimizer.generate_advanced_keyword_analysis(keyword_data)
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print(f"✅ Advanced keyword analysis completed")
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print(f" - Keyword opportunities: {len(result.get('keyword_opportunities', []))}")
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print(f" - Keyword clusters: {len(result.get('keyword_clusters', []))}")
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except Exception as e:
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print(f"❌ Advanced keyword analysis failed: {str(e)}")
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return False
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# Test 4: Health Check
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print("\n🏥 Test 4: Health Check")
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try:
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health_status = await ai_optimizer.health_check()
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print(f"✅ Health check completed")
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print(f" - Service status: {health_status.get('status')}")
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print(f" - Prompts loaded: {health_status.get('prompts_loaded')}")
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print(f" - Schemas loaded: {health_status.get('schemas_loaded')}")
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print(f" - AI integration: {health_status.get('capabilities', {}).get('ai_integration')}")
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except Exception as e:
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print(f"❌ Health check failed: {str(e)}")
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return False
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return True
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async def test_ai_engine_integration():
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"""Test the AI Engine Service integration with prompt optimizer."""
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print("\n🤖 Testing AI Engine Service Integration...")
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# Initialize the AI Engine Service
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ai_engine = AIEngineService()
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# Test 1: Content Gap Analysis with Advanced Prompts
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print("\n📊 Test 1: Content Gap Analysis with Advanced Prompts")
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analysis_summary = {
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'target_url': 'example.com',
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'industry': 'technology',
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'serp_opportunities': 25,
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'expanded_keywords_count': 150,
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'competitors_analyzed': 5,
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'dominant_themes': {
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'artificial_intelligence': 0.3,
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'machine_learning': 0.25,
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'data_science': 0.2
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}
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}
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try:
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result = await ai_engine.analyze_content_gaps(analysis_summary)
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print(f"✅ Content gap analysis with advanced prompts completed")
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print(f" - Strategic insights: {len(result.get('strategic_insights', []))}")
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print(f" - Content recommendations: {len(result.get('content_recommendations', []))}")
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except Exception as e:
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print(f"❌ Content gap analysis failed: {str(e)}")
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return False
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# Test 2: Market Position Analysis with Advanced Prompts
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print("\n🏢 Test 2: Market Position Analysis with Advanced Prompts")
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market_data = {
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'industry': 'technology',
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'competitors': [
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{
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'url': 'competitor1.com',
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'content_score': 8.5,
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'quality_score': 9.0
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},
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{
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'url': 'competitor2.com',
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'content_score': 7.8,
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'quality_score': 8.2
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}
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]
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}
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try:
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result = await ai_engine.analyze_market_position(market_data)
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print(f"✅ Market position analysis with advanced prompts completed")
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print(f" - Market leader: {result.get('market_leader', 'N/A')}")
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print(f" - Market gaps: {len(result.get('market_gaps', []))}")
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print(f" - Strategic recommendations: {len(result.get('strategic_recommendations', []))}")
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except Exception as e:
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print(f"❌ Market position analysis failed: {str(e)}")
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return False
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return True
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async def test_ai_fallback_functionality():
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"""Test the fallback functionality when AI fails."""
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print("\n🛡️ Testing AI Fallback Functionality...")
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# Initialize the AI Prompt Optimizer
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ai_optimizer = AIPromptOptimizer()
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# Test with invalid data to trigger fallback
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print("\n📊 Test: Fallback for Strategic Content Gap Analysis")
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invalid_data = {
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'invalid_field': 'invalid_value'
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}
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try:
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result = await ai_optimizer.generate_strategic_content_gap_analysis(invalid_data)
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print(f"✅ Fallback functionality working")
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print(f" - Strategic insights: {len(result.get('strategic_insights', []))}")
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print(f" - Content recommendations: {len(result.get('content_recommendations', []))}")
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except Exception as e:
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print(f"❌ Fallback functionality failed: {str(e)}")
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return False
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return True
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async def main():
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"""Main test function."""
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print("🚀 Starting Phase 3 AI Prompt Optimization Tests...")
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print("=" * 60)
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# Test 1: AI Prompt Optimizer
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ai_optimizer_success = await test_ai_prompt_optimizer()
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# Test 2: AI Engine Integration
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ai_engine_success = await test_ai_engine_integration()
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# Test 3: Fallback Functionality
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fallback_success = await test_ai_fallback_functionality()
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print("\n" + "=" * 60)
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print("📊 Test Results Summary:")
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print(f"AI Prompt Optimizer: {'✅ PASSED' if ai_optimizer_success else '❌ FAILED'}")
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print(f"AI Engine Integration: {'✅ PASSED' if ai_engine_success else '❌ FAILED'}")
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print(f"Fallback Functionality: {'✅ PASSED' if fallback_success else '❌ FAILED'}")
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if ai_optimizer_success and ai_engine_success and fallback_success:
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print("\n🎉 All Phase 3 tests passed! AI Prompt Optimization is working correctly.")
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print("\n✅ Phase 3 Achievements:")
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print(" - Advanced AI prompts implemented")
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print(" - Comprehensive JSON schemas created")
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print(" - Expert-level AI instructions optimized")
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print(" - Robust error handling and fallbacks")
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print(" - AI engine service integration completed")
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return 0
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else:
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print("\n⚠️ Some Phase 3 tests failed. Please check the AI configuration.")
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return 1
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if __name__ == "__main__":
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exit_code = asyncio.run(main())
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sys.exit(exit_code) |