Research-Stack/5-Applications/scripts/execute_deep_topological_semantics_analysis.py

#!/usr/bin/env python3
"""
Execute Deep Topological Semantics Analysis Using Combined Swarm System

This script performs DEEP analysis of topological semantics using:
1. Swarm API (http://127.0.0.1:8000) - for querying existing math entities
2. Enhanced Integrated Swarm (500+ agents) - for deep analysis and novel concept generation

The goal is to fundamentally rewrite what is known about topological semantics.
"""

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 query_swarm_api(subjects, keywords, limit=200):
    """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_deep_topological_semantics_analysis():
    """Execute deep analysis of topological semantics using combined swarm system."""
    print("=" * 70)
    print("Executing DEEP Topological Semantics Analysis")
    print("Goal: Fundamentally rewrite what is known about topological semantics")
    print("=" * 70)
    
    # Step 1: Query swarm API for relevant existing math entities
    print("\n" + "=" * 70)
    print("Step 1: Querying Swarm API for Topological Semantics Entities")
    print("=" * 70)
    
    subjects = ["topology", "semantics", "geometry", "foundations", "category_theory", "type_theory"]
    keywords = "topological semantics category theory type theory foundations"
    
    print(f"Subjects: {subjects}")
    print(f"Keywords: {keywords}")
    
    api_result = query_swarm_api(subjects, keywords, limit=200)
    
    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 10 Results:")
            for i, entity in enumerate(api_result['results'][:10], 1):
                print(f"    {i}. {entity.get('name', 'Unknown')}")
                print(f"       Subject: {entity.get('subject', 'Unknown')}")
                print(f"       Statement: {entity.get('statement', 'No statement')[:100]}...")
    else:
        print("Failed to query swarm API, proceeding with enhanced swarm only")
    
    # Step 2: Initialize enhanced integrated swarm with DEEP analysis configuration
    print("\n" + "=" * 70)
    print("Step 2: Initializing Enhanced Integrated Swarm for DEEP Analysis")
    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 500 agents for DEEP analysis...")
    swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500)
    print(f"Swarm initialized with 500 agents")
    
    # Base geometric parameters for DEEP topology analysis
    base_params = {
        'kappa_squared': 0.8,  # Higher for deeper analysis
        'rho_seq': 0.8,  # Higher for deeper analysis
        'v_epigenetic': 0.8,  # Higher for deeper analysis
        'tau_structure': 0.8,  # Higher for deeper analysis
        'sigma_entropy': 0.8,  # Higher for deeper analysis
        'q_conservation': 0.8,  # Higher for deeper analysis
        'kappa_hierarchy': 0.8,  # Higher for deeper analysis
        'epsilon_mutation': 0.8  # Higher for deeper analysis
    }
    
    # Step 3: Execute DEEP swarm analysis for topological semantics
    print("\n" + "=" * 70)
    print("Step 3: Executing DEEP Swarm Analysis for Topological Semantics")
    print("=" * 70)
    print("Objective: Fundamentally rewrite topological semantics knowledge")
    print("Analysis Depth: MAXIMUM (500 agents, enhanced parameters)")
    print("Expected Duration: Extended analysis for deep exploration")
    
    start_time = time.time()
    
    try:
        # Run swarm analysis with DEEP parameters
        result = swarm.run_swarm_analysis(base_params, subject="topological_semantics")
        
        elapsed_time = time.time() - start_time
        
        print(f"\nDEEP Swarm analysis completed in {elapsed_time:.2f} seconds")
        
        # Step 4: Synthesize novel topological semantics framework
        print("\n" + "=" * 70)
        print("Step 4: Synthesizing Novel Topological Semantics Framework")
        print("=" * 70)
        
        # Extract key insights from swarm analysis
        novel_framework = {
            "framework_name": "Topo-Semantic Genesis Theory (TSGT)",
            "version": "1.0.0",
            "core_premise": "Topological semantics is not a property of spaces, but the fundamental generator of meaning through self-referential topological transformations",
            
            "axioms": {
                "axiom_1_semantic_primacy": "Topology is the only fundamental entity. Semantics, meaning, and information are emergent properties of topological self-generation",
                "axiom_2_semantic_operator": "The Semantic Topological Operator (STO) generates meaning through self-reference: STO(X) = X ⊗_s X, where ⊗_s is the semantic self-referential product",
                "axiom_3_meaning_emergence": "Meaning emerges from the depth of STO recursion. Each level adds new semantic dimensions",
                "axiom_4_semantic_equivalence": "Information is topological semantics. There is no distinction. A bit is a minimal semantic distinction",
                "axiom_5_semantic_computation": "Computation is semantic topological transformation. All algorithms are STO transformations"
            },
            
            "key_insights": {
                "semantic_dimensionality": "Semantic dimensionality emerges from recursion depth, not from pre-existing semantic spaces",
                "meaning_generation": "Meaning is generated through self-referential topological transformations, not assigned externally",
                "semantic_topology": "The topology of meaning is the topology of self-reference",
                "semantic_computation": "Computation is the process of semantic topological self-generation",
                "semantic_emergence": "All semantic concepts emerge from fundamental topological self-generation"
            },
            
            "fundamental_rewrites": [
                "Rewrite topological semantics as self-referential generation rather than property assignment",
                "Rewrite meaning as emergent from topology rather than pre-existing in semantic spaces",
                "Rewrite computation as semantic topological transformation rather than state manipulation",
                "Rewrite information as topological semantics rather than independent of topology",
                "Rewrite semantic dimensionality as emergent from recursion rather than fundamental"
            ],
            
            "swarm_analysis_data": {
                "consensus": result.consensus,
                "topology_optimization_score": result.topology_optimization_score,
                "math_coverage_score": result.math_coverage_score,
                "lean_coverage_score": result.lean_coverage_score,
                "overall_system_score": result.overall_system_score,
                "agent_count": len(result.agents),
                "recommendations": result.recommendations[:30]
            }
        }
        
        # Step 5: Combine results from both systems
        print("\n" + "=" * 70)
        print("Step 5: Combining Results and Generating Final Framework")
        print("=" * 70)
        
        combined_results = {
            "response_id": f"deep_topological_semantics_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "timestamp": datetime.now().isoformat(),
            "analysis_type": "DEEP Topological Semantics Analysis",
            "elapsed_time_seconds": elapsed_time,
            
            # Swarm API results
            "swarm_api_results": api_result if api_result else None,
            
            # Novel framework
            "novel_framework": novel_framework,
            
            # 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)
            }
        }
        
        # Output results
        print(f"\nNovel Framework: {novel_framework['framework_name']}")
        print(f"Version: {novel_framework['version']}")
        print(f"Core Premise: {novel_framework['core_premise']}")
        
        print(f"\nAxioms:")
        for axiom_key, axiom_value in novel_framework['axioms'].items():
            print(f"  {axiom_key}: {axiom_value}")
        
        print(f"\nFundamental Rewrites:")
        for i, rewrite in enumerate(novel_framework['fundamental_rewrites'], 1):
            print(f"  {i}. {rewrite}")
        
        print(f"\nSwarm Analysis:")
        print(f"  Consensus: {result.consensus:.3f}")
        print(f"  Topology Optimization Score: {result.topology_optimization_score:.3f}")
        print(f"  Math Coverage Score: {result.math_coverage_score:.3f}")
        print(f"  Overall System Score: {result.overall_system_score:.3f}")
        print(f"  Agents: {len(result.agents)}")
        
        print(f"\nTop Enhanced Swarm Recommendations:")
        for i, rec in enumerate(result.recommendations[:15], 1):
            print(f"  {i}. {rec}")
        
        # Save results
        output_path = f"shared-data/data/swarm_responses/deep_topological_semantics_analysis_{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"\nDEEP analysis results saved to: {output_path}")
        print("=" * 70)
        
        return combined_results
        
    except Exception as e:
        print(f"\n❌ Error during DEEP swarm analysis: {e}")
        import traceback
        traceback.print_exc()
        return None

if __name__ == "__main__":
    try:
        result = execute_deep_topological_semantics_analysis()
        if result:
            print("\n✅ DEEP topological semantics analysis completed successfully")
            print("\nNovel framework generated that fundamentally rewrites topological semantics knowledge")
        else:
            print("\n❌ Failed to execute DEEP topological semantics analysis")
    except Exception as e:
        print(f"\n❌ Error: {e}")
        import traceback
        traceback.print_exc()