123 lines
4.8 KiB
Python
123 lines
4.8 KiB
Python
#!/usr/bin/env python3
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"""
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Test script for Step 5 with orchestrator context structure
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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 time
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# Add the backend directory to the path
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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async def test_step5_orchestrator_context():
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"""Test Step 5 with orchestrator context structure."""
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print("🧪 Testing Step 5 with orchestrator context structure")
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try:
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# Import Step 5
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from services.calendar_generation_datasource_framework.prompt_chaining.steps.phase2.step5_implementation import ContentPillarDistributionStep
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# Create context exactly as the orchestrator does
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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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"user_id": 1,
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"strategy_id": 1,
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"industry": "technology",
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"onboarding_data": {
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"posting_preferences": {
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"daily": 2,
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"weekly": 10,
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"monthly": 40
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},
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"posting_days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
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"optimal_times": ["09:00", "12:00", "15:00", "18:00", "20:00"]
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},
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"strategy_data": {
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"content_pillars": [
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"AI and Machine Learning",
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"Digital Transformation",
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"Innovation and Technology Trends",
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"Business Strategy and Growth"
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],
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"business_objectives": [
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"Increase brand awareness by 40%",
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"Generate 500 qualified leads per month",
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"Establish thought leadership in AI/ML space"
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]
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}
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},
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"step_results": {
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"step_04": {
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"stepNumber": 4,
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"stepName": "Calendar Framework & Timeline",
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"results": {
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"calendarStructure": {
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"type": "monthly",
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"total_weeks": 4,
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"posting_days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
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"posting_frequency": {
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"daily": 2,
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"weekly": 10,
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"monthly": 40
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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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},
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"qualityScore": 1.0,
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"executionTime": "2.9s"
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}
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},
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"quality_scores": {},
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"current_step": 5,
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"phase": "phase_2_structure"
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}
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# Create Step 5 instance
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print("✅ Creating Step 5 instance...")
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step5 = ContentPillarDistributionStep()
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print("✅ Step 5 instance created successfully")
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# Test Step 5 execution with timing
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print("🔄 Executing Step 5...")
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start_time = time.time()
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result = await step5.run(context)
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execution_time = time.time() - start_time
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print(f"⏱️ Step 5 execution time: {execution_time:.2f} seconds")
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if result:
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print("✅ Step 5 executed successfully!")
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print(f"Status: {result.get('status', 'unknown')}")
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print(f"Quality Score: {result.get('quality_score', 0)}")
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print(f"Execution Time: {result.get('execution_time', 'unknown')}")
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if result.get('status') == 'error':
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print(f"❌ Step 5 Error: {result.get('error_message', 'Unknown error')}")
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else:
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print("📊 Step 5 Results:")
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results = result.get('results', {})
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print(f" - Pillar Mapping: {results.get('pillarMapping', {}).get('distribution_balance', 0):.1%} balance")
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print(f" - Theme Development: {results.get('themeDevelopment', {}).get('variety_score', 0):.1%} variety")
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print(f" - Strategic Validation: {results.get('strategicValidation', {}).get('alignment_score', 0):.1%} alignment")
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print(f" - Diversity Assurance: {results.get('diversityAssurance', {}).get('diversity_score', 0):.1%} diversity")
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else:
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print("❌ Step 5 returned None")
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except Exception as e:
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print(f"❌ Error testing Step 5: {e}")
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import traceback
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print(f"📋 Traceback: {traceback.format_exc()}")
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if __name__ == "__main__":
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asyncio.run(test_step5_orchestrator_context())
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