#!/usr/bin/env python3 """ Execute Fundamental Topology Reinvention Using Combined Swarm System This script combines: 1. Swarm API (http://127.0.0.1:8000) - for querying existing math entities 2. Enhanced Integrated Swarm (200 agents) - for deep analysis and novel concept generation The combined system can both query existing knowledge and generate novel frameworks. """ import sys import json import time import requests from pathlib import Path from datetime import datetime # Add scripts directory to path sys.path.insert(0, str(Path(__file__).parent)) from enhanced_integrated_swarm import ( EnhancedIntegratedSwarm, create_demo_topology, MathDatabase ) SWARM_API_URL = "http://127.0.0.1:8000" def load_swarm_request(request_path): """Load the swarm request from file.""" with open(request_path, 'r') as f: return json.load(f) def query_swarm_api(subjects, keywords, limit=100): """Query the swarm API for existing math entities.""" try: response = requests.post( f"{SWARM_API_URL}/query", json={ "subjects": subjects, "keywords": keywords, "formalStatus": "unknown", "requireLeanFormalization": False, "limit": limit, "includeMetadata": True }, timeout=60 ) response.raise_for_status() return response.json() except Exception as e: print(f"Error querying swarm API: {e}") return None def execute_fundamental_reinvention_combined(): """Execute fundamental topology reinvention using combined swarm system.""" print("=" * 70) print("Executing Fundamental Topology Reinvention Using Combined Swarm System") print("=" * 70) # Load the swarm request request_path = "shared-data/data/swarm_requests/swarm_fundamental_reinvention.json" print(f"\nLoading request from: {request_path}") request = load_swarm_request(request_path) print(f"Request ID: {request['request_id']}") print(f"Time Allocation: {request['time_allocation']}") print(f"Priority: {request['priority']}") # Step 1: Query swarm API for relevant existing math entities print("\n" + "=" * 70) print("Step 1: Querying Swarm API for Existing Math Entities") print("=" * 70) subjects = ["topology", "mathematics", "foundations", "geometry"] keywords = "topology mathematics foundations geometry" print(f"Subjects: {subjects}") print(f"Keywords: {keywords}") api_result = query_swarm_api(subjects, keywords, limit=50) if api_result: print(f"\nSwarm API Results:") print(f" Success: {api_result.get('success', False)}") print(f" Count: {api_result.get('count', 0)}") print(f" Confidence: {api_result.get('confidence', 0)}") if api_result.get('results'): print(f"\n Top 5 Results:") for i, entity in enumerate(api_result['results'][:5], 1): print(f" {i}. {entity.get('name', 'Unknown')}") print(f" Subject: {entity.get('subject', 'Unknown')}") print(f" Statement: {entity.get('statement', 'No statement')[:80]}...") else: print("Failed to query swarm API, proceeding with enhanced swarm only") # Step 2: Initialize enhanced integrated swarm print("\n" + "=" * 70) print("Step 2: Initializing Enhanced Integrated Swarm") print("=" * 70) # Create demo topology print("Creating topology...") topology = create_demo_topology() print(f"Created topology with {len(topology.nodes)} nodes, {len(topology.edges)} edges") # Initialize math database print("Initializing math database...") math_db = MathDatabase() # Create enhanced integrated swarm with many agents for deep reasoning print(f"Initializing enhanced integrated swarm with 200 agents...") swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=200) print(f"Swarm initialized with 200 agents") # Base geometric parameters for topology analysis base_params = { 'kappa_squared': 0.5, 'rho_seq': 0.5, 'v_epigenetic': 0.5, 'tau_structure': 0.5, 'sigma_entropy': 0.5, 'q_conservation': 0.5, 'kappa_hierarchy': 0.5, 'epsilon_mutation': 0.5 } # Step 3: Execute swarm analysis for topology print("\n" + "=" * 70) print("Step 3: Executing Swarm Analysis for Fundamental Topology Reinvention") print("=" * 70) print(f"Subject: {request['context']['scope']}") print(f"Objective: {request['context']['objective']}") print(f"Time allocation: {request['time_allocation']}") start_time = time.time() try: # Run swarm analysis result = swarm.run_swarm_analysis(base_params, subject="topology") elapsed_time = time.time() - start_time print(f"\nSwarm analysis completed in {elapsed_time:.2f} seconds") # Step 4: Combine results from both systems print("\n" + "=" * 70) print("Step 4: Combining Results from Both Swarm Systems") print("=" * 70) combined_results = { "response_id": f"swarm_response_combined_{request['request_id'].replace('swarm_', '')}", "timestamp": datetime.now().isoformat(), "request_id": request['request_id'], "elapsed_time_seconds": elapsed_time, "time_allocation": request['time_allocation'], # Swarm API results "swarm_api_results": api_result if api_result else None, # Enhanced swarm results "enhanced_swarm_results": { "consensus": result.consensus, "topology_optimization_score": result.topology_optimization_score, "math_coverage_score": result.math_coverage_score, "lean_coverage_score": result.lean_coverage_score, "gpu_computing_score": result.gpu_computing_score, "ssd_storage_score": result.ssd_storage_score, "genetic_compression_score": result.genetic_compression_score, "homeostasis_score": result.homeostasis_score, "patterns_learned": result.patterns_learned, "metatyping_score": result.metatyping_score, "remote_nodes_count": result.remote_nodes_count, "dag_events_count": result.dag_events_count, "optimization_ratio": result.optimization_ratio, "substrate_potential": result.substrate_potential, "optimization_cycles": result.optimization_cycles, "overall_system_score": result.overall_system_score, "agent_count": len(result.agents), "nii_core_count": len(result.nii_cores), "recommendation_count": len(result.recommendations) }, # Combined analysis "combined_analysis": { "total_entities_considered": api_result.get('count', 0) if api_result else 0, "swarm_consensus": result.consensus, "enhanced_swarm_recommendations": result.recommendations[:20], "swarm_api_suggestions": api_result.get('suggestions', []) if api_result else [] } } # Output combined results print(f"\nCombined Analysis Results:") print(f" Total Entities Considered: {combined_results['combined_analysis']['total_entities_considered']}") print(f" Swarm Consensus: {combined_results['enhanced_swarm_results']['consensus']:.3f}") print(f" Topology Optimization Score: {combined_results['enhanced_swarm_results']['topology_optimization_score']:.3f}") print(f" Math Coverage Score: {combined_results['enhanced_swarm_results']['math_coverage_score']:.3f}") print(f" Overall System Score: {combined_results['enhanced_swarm_results']['overall_system_score']:.3f}") print(f"\nEnhanced Swarm Recommendations:") for i, rec in enumerate(result.recommendations[:10], 1): print(f" {i}. {rec}") if api_result and api_result.get('suggestions'): print(f"\nSwarm API Suggestions:") for suggestion in api_result['suggestions']: print(f" - {suggestion}") # Save combined results output_path = f"shared-data/data/swarm_responses/swarm_response_fundamental_reinvention_combined_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" Path(output_path).parent.mkdir(parents=True, exist_ok=True) with open(output_path, 'w') as f: json.dump(combined_results, f, indent=2) print(f"\nCombined results saved to: {output_path}") print("=" * 70) return combined_results except Exception as e: print(f"\n❌ Error during swarm analysis: {e}") import traceback traceback.print_exc() return None if __name__ == "__main__": try: result = execute_fundamental_reinvention_combined() if result: print("\n✅ Fundamental topology reinvention executed successfully using combined swarm system") else: print("\n❌ Failed to execute fundamental topology reinvention using combined swarm system") except Exception as e: print(f"\n❌ Error: {e}") import traceback traceback.print_exc()