ALwrity version 0.5.6
This commit is contained in:
ajaysi
491
backend/services/test_real_services_integration.py
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491
backend/services/test_real_services_integration.py
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"""
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Real Services Integration Test for 12-Step Prompt Chaining Framework
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This script tests the complete integration using real AI services and database connections.
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This test should be run from the backend/services directory or with proper PYTHONPATH setup.
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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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import json
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from datetime import datetime
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from typing import Dict, Any, Optional
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# Add the backend directory to Python path for proper imports
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backend_dir = os.path.dirname(os.path.dirname(__file__))
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if backend_dir not in sys.path:
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sys.path.insert(0, backend_dir)
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services_dir = os.path.dirname(__file__)
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if services_dir not in sys.path:
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sys.path.insert(0, services_dir)
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async def test_real_ai_engine_service():
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"""Test real AI Engine Service with proper error handling."""
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print("🤖 Testing Real AI Engine Service")
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print("=" * 40)
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try:
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from content_gap_analyzer.ai_engine_service import AIEngineService
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ai_engine = AIEngineService()
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# Test strategic insights generation
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print("🎯 Testing strategic insights generation...")
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try:
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result = await ai_engine.generate_strategic_insights(
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strategy_data={
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"content_pillars": ["AI", "Technology", "Innovation"],
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"target_audience": {"demographics": {"age": "25-35", "industry": "technology"}},
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"business_goals": ["Increase brand awareness", "Generate leads"]
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},
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onboarding_data={
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"website_analysis": {
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"industry": "technology",
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"target_audience": ["developers", "tech enthusiasts"],
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"content_focus": ["tutorials", "industry insights"]
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}
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},
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industry="technology",
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business_size="sme"
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)
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if result and isinstance(result, dict):
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print(f"✅ Strategic insights generation: SUCCESS")
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print(f" - Result keys: {list(result.keys())}")
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if "strategic_insights" in result:
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print(f" - Insights count: {len(result['strategic_insights'])}")
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return True, result
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else:
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print(f"⚠️ Strategic insights generation: Empty result")
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return False, None
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except Exception as e:
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print(f"❌ Strategic insights generation failed: {str(e)}")
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return False, None
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except ImportError as e:
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print(f"❌ AI Engine Service not available: {e}")
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return False, None
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async def test_real_keyword_researcher():
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"""Test real Keyword Researcher service."""
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print("\n🔍 Testing Real Keyword Researcher")
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print("=" * 40)
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try:
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from content_gap_analyzer.keyword_researcher import KeywordResearcher
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keyword_researcher = KeywordResearcher()
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# Test keyword analysis
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print("🎯 Testing keyword analysis...")
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try:
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result = await keyword_researcher.analyze_keywords(
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target_keywords=["artificial intelligence", "machine learning", "automation", "AI tools"],
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industry="technology"
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)
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if result and isinstance(result, dict):
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print(f"✅ Keyword analysis: SUCCESS")
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print(f" - Result keys: {list(result.keys())}")
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if "high_value_keywords" in result:
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print(f" - High-value keywords: {len(result['high_value_keywords'])}")
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return True, result
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else:
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print(f"⚠️ Keyword analysis: Empty result")
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return False, None
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except Exception as e:
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print(f"❌ Keyword analysis failed: {str(e)}")
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return False, None
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except ImportError as e:
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print(f"❌ Keyword Researcher not available: {e}")
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return False, None
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async def test_real_onboarding_service():
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"""Test real Onboarding Data Service."""
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print("\n👤 Testing Real Onboarding Data Service")
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print("=" * 40)
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try:
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from onboarding_data_service import OnboardingDataService
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onboarding_service = OnboardingDataService()
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# Test get personalized inputs
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print("🎯 Testing get personalized AI inputs...")
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try:
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result = onboarding_service.get_personalized_ai_inputs(1)
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if result and isinstance(result, dict):
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print(f"✅ Get personalized AI inputs: SUCCESS")
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print(f" - Result keys: {list(result.keys())}")
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if "website_analysis" in result:
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print(f" - Website analysis available")
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if "keyword_analysis" in result:
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print(f" - Keyword analysis available")
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return True, result
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else:
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print(f"⚠️ Get personalized AI inputs: Empty result")
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return False, None
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except Exception as e:
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print(f"❌ Get personalized AI inputs failed: {str(e)}")
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return False, None
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except ImportError as e:
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print(f"❌ Onboarding Data Service not available: {e}")
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return False, None
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async def test_real_data_processing():
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"""Test real data processing modules."""
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print("\n💾 Testing Real Data Processing Modules")
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print("=" * 40)
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try:
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from calendar_generation_datasource_framework.data_processing import (
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ComprehensiveUserDataProcessor,
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StrategyDataProcessor,
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GapAnalysisDataProcessor
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)
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# Test comprehensive user data processor
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print("🎯 Testing ComprehensiveUserDataProcessor...")
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try:
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processor = ComprehensiveUserDataProcessor()
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result = await processor.get_comprehensive_user_data(1, 1)
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if result and isinstance(result, dict):
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print(f"✅ ComprehensiveUserDataProcessor: SUCCESS")
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print(f" - Result keys: {list(result.keys())}")
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return True, result
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else:
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print(f"⚠️ ComprehensiveUserDataProcessor: Empty result")
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return False, None
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except Exception as e:
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print(f"❌ ComprehensiveUserDataProcessor failed: {str(e)}")
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return False, None
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except ImportError as e:
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print(f"❌ Data Processing modules not available: {e}")
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return False, None
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async def test_phase1_with_real_services():
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"""Test Phase 1 steps with real service integration."""
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print("\n🎯 Testing Phase 1 Steps with Real Services")
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print("=" * 50)
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try:
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from calendar_generation_datasource_framework.prompt_chaining.steps.phase1_steps import (
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ContentStrategyAnalysisStep,
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GapAnalysisStep,
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AudiencePlatformStrategyStep
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)
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# Get real data
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real_context = {
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"user_id": 1,
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"strategy_id": 1,
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"calendar_type": "monthly",
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"industry": "technology",
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"business_size": "sme",
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"user_data": {
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"strategy_data": {
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"content_pillars": ["AI", "Technology", "Innovation", "Tutorials"],
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"target_audience": {
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"demographics": {"age": "25-35", "location": "US", "industry": "technology"},
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"interests": ["AI", "machine learning", "programming", "tech trends"]
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},
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"business_goals": ["Increase brand awareness", "Generate leads", "Establish thought leadership"],
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"success_metrics": ["Website traffic", "Social engagement", "Lead generation"]
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},
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"onboarding_data": {
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"website_analysis": {
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"industry": "technology",
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"target_audience": ["developers", "tech enthusiasts", "AI researchers"],
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"content_focus": ["tutorials", "industry insights", "product reviews"],
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"competitive_landscape": ["competitor1.com", "competitor2.com"]
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},
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"competitor_analysis": {
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"top_performers": ["OpenAI Blog", "Google AI Blog", "MIT Technology Review"],
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"content_types": ["research papers", "tutorials", "industry news"]
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},
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"keyword_analysis": {
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"high_value_keywords": ["artificial intelligence", "machine learning", "AI tools", "automation"],
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"search_volume": {"artificial intelligence": 100000, "machine learning": 80000}
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}
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},
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"gap_analysis": {
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"content_gaps": ["Video tutorials", "Interactive demos", "Case studies", "Beginner guides"],
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"keyword_opportunities": ["AI for beginners", "machine learning tutorial", "AI tools comparison"],
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"implementation_priority": {"high": ["Video tutorials"], "medium": ["Case studies"]}
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},
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"performance_data": {
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"engagement_metrics": {"average_engagement": 0.05, "peak_engagement_time": "9am-11am"},
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"best_performing_content": ["How-to guides", "Industry insights", "Product comparisons"],
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"platform_performance": {"linkedin": 0.08, "twitter": 0.03, "blog": 0.12}
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},
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"competitor_data": {
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"competitor_urls": ["https://openai.com/blog", "https://ai.googleblog.com"],
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"analysis_date": datetime.now().isoformat()
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}
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},
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"step_results": {},
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"quality_scores": {},
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"current_step": 0,
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"phase": "initialization"
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}
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phase1_results = {}
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total_execution_time = 0
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# Test Step 1: Content Strategy Analysis with real services
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print("🎯 Testing Step 1: Content Strategy Analysis with Real Services")
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try:
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step1 = ContentStrategyAnalysisStep()
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result1 = await step1.run(real_context)
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phase1_results["step_01"] = result1
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total_execution_time += result1.get('execution_time', 0.0)
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print(f"✅ Step 1 Status: {result1.get('status', 'unknown')}")
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print(f"📊 Step 1 Quality: {result1.get('quality_score', 0.0):.2f}")
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print(f"⏱️ Step 1 Time: {result1.get('execution_time', 0.0):.2f}s")
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# Check if real services were used
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step_result = result1.get('result', {})
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strategy_summary = step_result.get('content_strategy_summary', {})
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if strategy_summary.get('content_pillars'):
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print(f" ✅ Real strategy data processed: {len(strategy_summary['content_pillars'])} pillars")
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except Exception as e:
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print(f"❌ Step 1 failed: {str(e)}")
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# Test Step 2: Gap Analysis with real services
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print("\n🎯 Testing Step 2: Gap Analysis & Opportunity Identification with Real Services")
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try:
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step2 = GapAnalysisStep()
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result2 = await step2.run(real_context)
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phase1_results["step_02"] = result2
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total_execution_time += result2.get('execution_time', 0.0)
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print(f"✅ Step 2 Status: {result2.get('status', 'unknown')}")
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print(f"📊 Step 2 Quality: {result2.get('quality_score', 0.0):.2f}")
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print(f"⏱️ Step 2 Time: {result2.get('execution_time', 0.0):.2f}s")
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# Check if real services were used
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step_result = result2.get('result', {})
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gap_analysis = step_result.get('prioritized_gaps', {})
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if gap_analysis.get('content_gaps'):
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print(f" ✅ Real gap data processed: {len(gap_analysis['content_gaps'])} gaps")
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except Exception as e:
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print(f"❌ Step 2 failed: {str(e)}")
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# Test Step 3: Audience & Platform Strategy with real services
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print("\n🎯 Testing Step 3: Audience & Platform Strategy with Real Services")
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try:
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step3 = AudiencePlatformStrategyStep()
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result3 = await step3.run(real_context)
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phase1_results["step_03"] = result3
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total_execution_time += result3.get('execution_time', 0.0)
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print(f"✅ Step 3 Status: {result3.get('status', 'unknown')}")
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print(f"📊 Step 3 Quality: {result3.get('quality_score', 0.0):.2f}")
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print(f"⏱️ Step 3 Time: {result3.get('execution_time', 0.0):.2f}s")
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# Check if real services were used
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step_result = result3.get('result', {})
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audience_personas = step_result.get('audience_personas', {})
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if audience_personas.get('demographics'):
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print(f" ✅ Real audience data processed")
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except Exception as e:
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print(f"❌ Step 3 failed: {str(e)}")
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# Calculate overall metrics
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completed_steps = len([r for r in phase1_results.values() if r.get('status') == 'completed'])
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total_quality = sum(r.get('quality_score', 0.0) for r in phase1_results.values())
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avg_quality = total_quality / len(phase1_results) if phase1_results else 0.0
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print(f"\n📋 Phase 1 Real Services Integration Summary")
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print("=" * 50)
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print(f"✅ Completed Steps: {completed_steps}/3")
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print(f"📊 Average Quality: {avg_quality:.2f}")
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print(f"⏱️ Total Time: {total_execution_time:.2f}s")
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return completed_steps == 3, phase1_results
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except ImportError as e:
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print(f"❌ Phase 1 steps not available: {e}")
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return False, {}
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async def test_end_to_end_calendar_generation():
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"""Test complete end-to-end calendar generation with real services."""
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print("\n🚀 Testing End-to-End Calendar Generation with Real Services")
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print("=" * 60)
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try:
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from calendar_generation_datasource_framework.prompt_chaining import PromptChainOrchestrator
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# Initialize orchestrator
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print("📋 Initializing Prompt Chain Orchestrator...")
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orchestrator = PromptChainOrchestrator()
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# Test full calendar generation
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print("🎯 Testing complete calendar generation...")
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try:
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result = await orchestrator.generate_calendar(
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user_id=1,
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strategy_id=1,
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calendar_type="monthly",
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industry="technology",
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business_size="sme"
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)
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print("✅ End-to-end calendar generation completed!")
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# Analyze result quality
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quality_score = result.get('quality_score', 0.0)
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ai_confidence = result.get('ai_confidence', 0.0)
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processing_time = result.get('processing_time', 0.0)
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print(f"📊 Quality Score: {quality_score:.2f}")
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print(f"🤖 AI Confidence: {ai_confidence:.2f}")
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print(f"⏱️ Processing Time: {processing_time:.2f}s")
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print(f"🎯 Framework Version: {result.get('framework_version', 'unknown')}")
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# Check calendar content completeness
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calendar_fields = [
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'daily_schedule', 'weekly_themes', 'content_recommendations',
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'optimal_timing', 'performance_predictions', 'trending_topics',
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'content_pillars', 'platform_strategies', 'gap_analysis_insights'
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]
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present_fields = [field for field in calendar_fields if field in result and result[field]]
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completeness_score = len(present_fields) / len(calendar_fields) * 100
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print(f"📋 Content Completeness: {completeness_score:.1f}% ({len(present_fields)}/{len(calendar_fields)} fields)")
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# Check step results
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step_results = result.get('step_results_summary', {})
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completed_steps = len([s for s in step_results.values() if s.get('status') == 'completed'])
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print(f"🎯 Steps Completed: {completed_steps}/12")
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return True, {
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'quality_score': quality_score,
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'ai_confidence': ai_confidence,
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'processing_time': processing_time,
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'completeness_score': completeness_score,
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'completed_steps': completed_steps
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}
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except Exception as e:
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print(f"❌ End-to-end calendar generation failed: {str(e)}")
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return False, None
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except ImportError as e:
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print(f"❌ Prompt Chain Orchestrator not available: {e}")
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return False, None
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async def generate_real_services_report(test_results: Dict[str, Any]):
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"""Generate comprehensive real services integration report."""
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print("\n📋 Real Services Integration Report")
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print("=" * 60)
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# Service connectivity
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services_tested = 0
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services_working = 0
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for test_name, (success, data) in test_results.items():
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services_tested += 1
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if success:
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services_working += 1
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print(f"✅ {test_name}: SUCCESS")
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else:
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print(f"❌ {test_name}: FAILED")
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connectivity_score = services_working / services_tested * 100 if services_tested > 0 else 0
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print(f"\n🔧 Service Connectivity: {services_working}/{services_tested} ({connectivity_score:.1f}%)")
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# Phase 1 integration analysis
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if 'phase1_real_services' in test_results:
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phase1_success, phase1_data = test_results['phase1_real_services']
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if phase1_success:
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avg_quality = sum(r.get('quality_score', 0.0) for r in phase1_data.values()) / len(phase1_data)
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total_time = sum(r.get('execution_time', 0.0) for r in phase1_data.values())
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print(f"🎯 Phase 1 Quality: {avg_quality:.2f}")
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print(f"⏱️ Phase 1 Time: {total_time:.2f}s")
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# End-to-end analysis
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if 'e2e_calendar_generation' in test_results:
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e2e_success, e2e_data = test_results['e2e_calendar_generation']
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if e2e_success and e2e_data:
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print(f"🚀 E2E Quality: {e2e_data['quality_score']:.2f}")
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print(f"🤖 E2E Confidence: {e2e_data['ai_confidence']:.2f}")
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print(f"📋 E2E Completeness: {e2e_data['completeness_score']:.1f}%")
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||||
# Overall assessment
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||||
if connectivity_score >= 80:
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print(f"\n🎉 EXCELLENT: Real services integration ready for production!")
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elif connectivity_score >= 60:
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print(f"\n✅ GOOD: Most services working, minor issues to resolve")
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||||
elif connectivity_score >= 40:
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print(f"\n⚠️ FAIR: Some services working, significant improvements needed")
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else:
|
||||
print(f"\n❌ POOR: Major service integration issues, requires attention")
|
||||
|
||||
# Save detailed report
|
||||
report = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"service_connectivity": {
|
||||
"working": services_working,
|
||||
"tested": services_tested,
|
||||
"percentage": connectivity_score
|
||||
},
|
||||
"test_results": test_results,
|
||||
"overall_status": "excellent" if connectivity_score >= 80 else "good" if connectivity_score >= 60 else "fair" if connectivity_score >= 40 else "poor"
|
||||
}
|
||||
|
||||
with open("real_services_integration_report.json", "w") as f:
|
||||
json.dump(report, f, indent=2, default=str)
|
||||
|
||||
print(f"\n💾 Detailed report saved to: real_services_integration_report.json")
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main real services integration test function."""
|
||||
print("🧪 Real Services Integration Test Suite")
|
||||
print("=" * 60)
|
||||
print(f"🕒 Started at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
|
||||
test_results = {}
|
||||
|
||||
# Test individual real services
|
||||
test_results['ai_engine'] = await test_real_ai_engine_service()
|
||||
test_results['keyword_researcher'] = await test_real_keyword_researcher()
|
||||
test_results['onboarding_service'] = await test_real_onboarding_service()
|
||||
test_results['data_processing'] = await test_real_data_processing()
|
||||
|
||||
# Test Phase 1 with real services
|
||||
test_results['phase1_real_services'] = await test_phase1_with_real_services()
|
||||
|
||||
# Test end-to-end calendar generation
|
||||
test_results['e2e_calendar_generation'] = await test_end_to_end_calendar_generation()
|
||||
|
||||
# Generate comprehensive report
|
||||
await generate_real_services_report(test_results)
|
||||
|
||||
print(f"\n🏁 Real services integration test completed at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user