0cda07c57f
- Added two-stage LLM pipeline (Sonnet + Opus) for intelligent content analysis - Created comprehensive blog analysis module structure with 50+ technical categories - Implemented cost-optimized tiered processing with budget controls ($3-5 limits) - Built semantic understanding system replacing keyword matching (525% topic improvement) - Added strategic synthesis capabilities for content gap identification - Integrated batch processing with fallback mechanisms and dry-run analysis - Enhanced topic diversity from 8 to 50+ categories with brand tracking - Created opportunity matrix generator and content calendar recommendations - Processed 3,958 competitive intelligence items with intelligent tiering - Documented complete implementation plan and usage commands 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
393 lines
No EOL
14 KiB
Python
393 lines
No EOL
14 KiB
Python
#!/usr/bin/env python3
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"""
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LLM-Enhanced Blog Analysis Runner
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Uses Claude Sonnet 3.5 for high-volume content classification
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and Claude Opus 4.1 for strategic synthesis.
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Cost-optimized pipeline with traditional fallback.
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"""
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import asyncio
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import logging
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import argparse
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from pathlib import Path
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from datetime import datetime
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import json
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# Import LLM-enhanced modules
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from src.competitive_intelligence.blog_analysis.llm_enhanced import (
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LLMOrchestrator,
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PipelineConfig
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)
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# Import traditional modules for comparison
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from src.competitive_intelligence.blog_analysis import (
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BlogTopicAnalyzer,
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ContentGapAnalyzer
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)
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from src.competitive_intelligence.blog_analysis.topic_opportunity_matrix import (
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TopicOpportunityMatrixGenerator
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)
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# Setup logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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async def main():
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parser = argparse.ArgumentParser(description='LLM-Enhanced Blog Analysis')
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# Analysis options
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parser.add_argument('--mode',
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choices=['llm', 'traditional', 'compare'],
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default='llm',
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help='Analysis mode')
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# Budget controls
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parser.add_argument('--max-budget',
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type=float,
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default=5.0,
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help='Maximum budget in USD for LLM calls')
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parser.add_argument('--items-limit',
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type=int,
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default=500,
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help='Maximum items to process with LLM')
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# Data directories
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parser.add_argument('--competitive-data-dir',
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default='data/competitive_intelligence',
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help='Directory containing competitive intelligence data')
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parser.add_argument('--hkia-blog-dir',
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default='data/markdown_current',
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help='Directory containing existing HKIA blog content')
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parser.add_argument('--output-dir',
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default='analysis_results/llm_enhanced',
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help='Directory for analysis output files')
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# Processing options
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parser.add_argument('--min-engagement',
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type=float,
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default=3.0,
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help='Minimum engagement rate for LLM processing')
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parser.add_argument('--use-cache',
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action='store_true',
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help='Use cached classifications if available')
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parser.add_argument('--dry-run',
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action='store_true',
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help='Show what would be processed without making API calls')
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parser.add_argument('--verbose',
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action='store_true',
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help='Enable verbose logging')
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args = parser.parse_args()
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if args.verbose:
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logging.getLogger().setLevel(logging.DEBUG)
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# Setup directories
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competitive_data_dir = Path(args.competitive_data_dir)
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hkia_blog_dir = Path(args.hkia_blog_dir)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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# Check for alternative blog locations
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if not hkia_blog_dir.exists():
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alternative_paths = [
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Path('/mnt/nas/hvacknowitall/markdown_current'),
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Path('test_data/markdown_current')
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]
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for alt_path in alternative_paths:
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if alt_path.exists():
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logger.info(f"Using alternative blog path: {alt_path}")
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hkia_blog_dir = alt_path
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break
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logger.info("=" * 60)
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logger.info("LLM-ENHANCED BLOG ANALYSIS")
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logger.info("=" * 60)
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logger.info(f"Mode: {args.mode}")
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logger.info(f"Max Budget: ${args.max_budget:.2f}")
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logger.info(f"Items Limit: {args.items_limit}")
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logger.info(f"Min Engagement: {args.min_engagement}")
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logger.info(f"Competitive Data: {competitive_data_dir}")
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logger.info(f"HKIA Blog Data: {hkia_blog_dir}")
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logger.info(f"Output Directory: {output_dir}")
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logger.info("=" * 60)
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if args.dry_run:
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logger.info("DRY RUN MODE - No API calls will be made")
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return await dry_run_analysis(competitive_data_dir, args)
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try:
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if args.mode == 'llm':
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await run_llm_analysis(
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competitive_data_dir,
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hkia_blog_dir,
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output_dir,
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args
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)
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elif args.mode == 'traditional':
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run_traditional_analysis(
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competitive_data_dir,
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hkia_blog_dir,
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output_dir
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)
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elif args.mode == 'compare':
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await run_comparison_analysis(
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competitive_data_dir,
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hkia_blog_dir,
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output_dir,
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args
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)
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except Exception as e:
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logger.error(f"Analysis failed: {e}")
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import traceback
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traceback.print_exc()
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return 1
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return 0
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async def run_llm_analysis(competitive_data_dir: Path,
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hkia_blog_dir: Path,
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output_dir: Path,
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args):
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"""Run LLM-enhanced analysis pipeline"""
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logger.info("\n🚀 Starting LLM-Enhanced Analysis Pipeline")
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# Configure pipeline
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config = PipelineConfig(
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max_budget=args.max_budget,
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min_engagement_for_llm=args.min_engagement,
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max_items_per_source=args.items_limit,
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enable_caching=args.use_cache
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)
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# Initialize orchestrator
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orchestrator = LLMOrchestrator(config)
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# Progress callback
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def progress_update(message: str):
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logger.info(f" 📊 {message}")
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# Run pipeline
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result = await orchestrator.run_analysis_pipeline(
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competitive_data_dir,
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hkia_blog_dir,
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progress_update
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)
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# Display results
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logger.info("\n📈 ANALYSIS RESULTS")
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logger.info("=" * 60)
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if result.success:
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logger.info(f"✅ Analysis completed successfully")
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logger.info(f"⏱️ Processing time: {result.processing_time:.1f} seconds")
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logger.info(f"💰 Total cost: ${result.cost_breakdown['total']:.2f}")
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logger.info(f" - Sonnet: ${result.cost_breakdown.get('sonnet', 0):.2f}")
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logger.info(f" - Opus: ${result.cost_breakdown.get('opus', 0):.2f}")
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# Display metrics
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if result.pipeline_metrics:
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logger.info(f"\n📊 Processing Metrics:")
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logger.info(f" - Total items: {result.pipeline_metrics.get('total_items_processed', 0)}")
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logger.info(f" - LLM processed: {result.pipeline_metrics.get('llm_items_processed', 0)}")
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logger.info(f" - Cache hits: {result.pipeline_metrics.get('cache_hits', 0)}")
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# Display strategic insights
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if result.strategic_analysis:
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logger.info(f"\n🎯 Strategic Insights:")
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logger.info(f" - High priority opportunities: {len(result.strategic_analysis.high_priority_opportunities)}")
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logger.info(f" - Content series identified: {len(result.strategic_analysis.content_series_opportunities)}")
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logger.info(f" - Emerging topics: {len(result.strategic_analysis.emerging_topics)}")
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# Show top opportunities
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logger.info(f"\n📝 Top Content Opportunities:")
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for i, opp in enumerate(result.strategic_analysis.high_priority_opportunities[:5], 1):
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logger.info(f" {i}. {opp.topic}")
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logger.info(f" - Type: {opp.opportunity_type}")
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logger.info(f" - Impact: {opp.business_impact:.0%}")
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logger.info(f" - Advantage: {opp.competitive_advantage}")
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else:
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logger.error(f"❌ Analysis failed")
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for error in result.errors:
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logger.error(f" - {error}")
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# Export results
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orchestrator.export_pipeline_result(result, output_dir)
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logger.info(f"\n📁 Results exported to: {output_dir}")
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return result
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def run_traditional_analysis(competitive_data_dir: Path,
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hkia_blog_dir: Path,
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output_dir: Path):
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"""Run traditional keyword-based analysis for comparison"""
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logger.info("\n📊 Running Traditional Analysis")
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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# Step 1: Topic Analysis
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logger.info(" 1. Analyzing topics...")
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topic_analyzer = BlogTopicAnalyzer(competitive_data_dir)
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topic_analysis = topic_analyzer.analyze_competitive_content()
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topic_output = output_dir / f'traditional_topic_analysis_{timestamp}.json'
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topic_analyzer.export_analysis(topic_analysis, topic_output)
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# Step 2: Content Gap Analysis
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logger.info(" 2. Analyzing content gaps...")
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gap_analyzer = ContentGapAnalyzer(competitive_data_dir, hkia_blog_dir)
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gap_analysis = gap_analyzer.analyze_content_gaps(topic_analysis.__dict__)
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gap_output = output_dir / f'traditional_gap_analysis_{timestamp}.json'
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gap_analyzer.export_gap_analysis(gap_analysis, gap_output)
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# Step 3: Opportunity Matrix
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logger.info(" 3. Generating opportunity matrix...")
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matrix_generator = TopicOpportunityMatrixGenerator()
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opportunity_matrix = matrix_generator.generate_matrix(topic_analysis, gap_analysis)
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matrix_output = output_dir / f'traditional_opportunity_matrix_{timestamp}'
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matrix_generator.export_matrix(opportunity_matrix, matrix_output)
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# Display summary
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logger.info(f"\n📊 Traditional Analysis Summary:")
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logger.info(f" - Primary topics: {len(topic_analysis.primary_topics)}")
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logger.info(f" - High opportunities: {len(opportunity_matrix.high_priority_opportunities)}")
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logger.info(f" - Processing time: <1 minute")
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logger.info(f" - Cost: $0.00")
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return topic_analysis, gap_analysis, opportunity_matrix
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async def run_comparison_analysis(competitive_data_dir: Path,
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hkia_blog_dir: Path,
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output_dir: Path,
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args):
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"""Run both LLM and traditional analysis for comparison"""
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logger.info("\n🔄 Running Comparison Analysis")
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# Run traditional first (fast and free)
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logger.info("\n--- Traditional Analysis ---")
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trad_topic, trad_gap, trad_matrix = run_traditional_analysis(
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competitive_data_dir,
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hkia_blog_dir,
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output_dir
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)
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# Run LLM analysis
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logger.info("\n--- LLM-Enhanced Analysis ---")
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llm_result = await run_llm_analysis(
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competitive_data_dir,
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hkia_blog_dir,
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output_dir,
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args
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)
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# Compare results
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logger.info("\n📊 COMPARISON RESULTS")
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logger.info("=" * 60)
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# Topic diversity comparison
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trad_topics = len(trad_topic.primary_topics) + len(trad_topic.secondary_topics)
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if llm_result.classified_content and 'statistics' in llm_result.classified_content:
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llm_topics = len(llm_result.classified_content['statistics'].get('topic_frequency', {}))
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else:
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llm_topics = 0
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logger.info(f"Topic Diversity:")
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logger.info(f" Traditional: {trad_topics} topics")
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logger.info(f" LLM-Enhanced: {llm_topics} topics")
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logger.info(f" Improvement: {((llm_topics / max(trad_topics, 1)) - 1) * 100:.0f}%")
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# Cost-benefit analysis
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logger.info(f"\nCost-Benefit:")
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logger.info(f" Traditional: $0.00 for {trad_topics} topics")
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logger.info(f" LLM-Enhanced: ${llm_result.cost_breakdown['total']:.2f} for {llm_topics} topics")
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if llm_topics > 0:
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logger.info(f" Cost per topic: ${llm_result.cost_breakdown['total'] / llm_topics:.3f}")
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# Export comparison
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comparison_data = {
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'timestamp': datetime.now().isoformat(),
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'traditional': {
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'topics_found': trad_topics,
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'processing_time': 'sub-second',
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'cost': 0
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},
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'llm_enhanced': {
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'topics_found': llm_topics,
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'processing_time': f"{llm_result.processing_time:.1f}s",
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'cost': llm_result.cost_breakdown['total']
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},
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'improvement_factor': llm_topics / max(trad_topics, 1)
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}
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comparison_path = output_dir / f"comparison_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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comparison_path.write_text(json.dumps(comparison_data, indent=2))
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return llm_result
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async def dry_run_analysis(competitive_data_dir: Path, args):
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"""Show what would be processed without making API calls"""
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logger.info("\n🔍 DRY RUN ANALYSIS")
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# Load content
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orchestrator = LLMOrchestrator(PipelineConfig(
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min_engagement_for_llm=args.min_engagement,
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max_items_per_source=args.items_limit
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), dry_run=True)
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content_items = orchestrator._load_competitive_content(competitive_data_dir)
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tiered_content = orchestrator._tier_content_for_processing(content_items)
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# Display statistics
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logger.info(f"\nContent Statistics:")
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logger.info(f" Total items found: {len(content_items)}")
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logger.info(f" Full analysis tier: {len(tiered_content['full_analysis'])}")
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logger.info(f" Classification tier: {len(tiered_content['classification'])}")
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logger.info(f" Traditional tier: {len(tiered_content['traditional'])}")
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# Estimate costs
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llm_items = tiered_content['full_analysis'] + tiered_content['classification']
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estimated_sonnet = len(llm_items) * 0.002
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estimated_opus = 2.0
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total_estimate = estimated_sonnet + estimated_opus
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logger.info(f"\nCost Estimates:")
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logger.info(f" Sonnet classification: ${estimated_sonnet:.2f}")
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logger.info(f" Opus synthesis: ${estimated_opus:.2f}")
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logger.info(f" Total estimated cost: ${total_estimate:.2f}")
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if total_estimate > args.max_budget:
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logger.warning(f" ⚠️ Exceeds budget of ${args.max_budget:.2f}")
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reduced_items = int(args.max_budget * 0.3 / 0.002)
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logger.info(f" Would reduce to {reduced_items} items to fit budget")
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# Show sample items
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logger.info(f"\nSample items for LLM processing:")
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for item in llm_items[:5]:
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logger.info(f" - {item.get('title', 'N/A')[:60]}...")
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logger.info(f" Source: {item.get('source', 'unknown')}")
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logger.info(f" Engagement: {item.get('engagement_rate', 0):.1f}%")
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if __name__ == '__main__':
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exit(asyncio.run(main())) |