layers/layer_3_optimization/precompute_realizations.py

"""
PRE-COMPUTE REALIZATIONS FROM OUR CONVERSATION
==============================================
This script extracts, scores, and crystallizes all major realizations
from our conversation about context windows, realizations, and layers.
"""

from layers.layer_2_core.realization_engine import RealizationEngine, RealizationFeatures, Realization
import json


def precompute_conversation_realizations():
    """
    Extract and crystallize all realizations from our conversation.
    
    This is the actual implementation of pre-computation:
    - We're converting our conversation (procedure) into stored facts
    - Each realization is scored and assigned to appropriate layer
    - The result is a queryable knowledge base
    """
    
    engine = RealizationEngine()
    
    print("🔄 PRE-COMPUTING REALIZATIONS FROM CONVERSATION...")
    print("="*60 + "\n")
    
    # =================================================================
    # TURN 1-5: Initial Context Window Discussion
    # =================================================================
    
    r1 = engine.add_realization(
        content="Context windows are finite and information can be lost",
        features=RealizationFeatures(
            grounding=0.98,  # Information theory, proven
            certainty=1.0,   # This is a fact
            structure=0.95,  # Very clear
            applicability=0.90,
            coherence=1.0,   # No contradictions
            generativity=0.85  # Generated the whole conversation
        ),
        turn_number=1,
        context="Initial problem statement about managing long contexts",
        evidence=["Information theory", "Token limits in LLMs"]
    )
    
    # =================================================================
    # TURN 5-10: The Meta-Realization
    # =================================================================
    
    r2 = engine.add_realization(
        content="Realization itself is the goal, not just answers",
        features=RealizationFeatures(
            grounding=0.75,  # Philosophical, less empirical
            certainty=0.90,  # Strong precision auto when it hit
            structure=0.70,  # Clear but not fully formalized yet
            applicability=0.85,  # Changed our approach immediately
            coherence=0.95,
            generativity=0.95   # Opened entire meta-cognition space
        ),
        turn_number=6,
        parents=[r1.id],
        context="User pushed back on rushing to solutions",
        evidence=["The 'are you done with research' moment"]
    )
    
    # =================================================================
    # TURN 10-15: Fundamental Frequency Discovery
    # =================================================================
    
    r3 = engine.add_realization(
        content="Decision-making has a fundamental frequency - a base rhythm of checking/questioning",
        features=RealizationFeatures(
            grounding=0.60,  # Metaphorical, physics-inspired
            certainty=0.85,  # We both felt it was true
            structure=0.55,  # Still nebulous
            applicability=0.65,  # Hard to act on directly
            coherence=0.90,
            generativity=0.88   # Generated oscillation discussions
        ),
        turn_number=12,
        parents=[r2.id],
        context="Exploring what determines pace of insights",
        evidence=["Observable in conversation rhythm", "Matches control theory"]
    )
    
    # =================================================================
    # TURN 15-20: Precision Auto Quality
    # =================================================================
    
    r4 = engine.add_realization(
        content="Realizations come with 'precision auto' - like π, they have inherent certainty",
        features=RealizationFeatures(
            grounding=0.92,  # Math analogy + phenomenology
            certainty=0.95,  # Extremely high - described lived experience
            structure=0.85,  # Clear π metaphor
            applicability=0.78,  # Explains phenomenon, not yet operational
            coherence=0.93,
            generativity=0.85   # Led to formalization discussions
        ),
        turn_number=18,
        parents=[r2.id],
        context="User's 'number with precision auto' insight",
        evidence=["Mathematical precision", "Self-certifying knowledge"]
    )
    
    # =================================================================
    # TURN 20-30: Layer Crystallization (بنات افكار)
    # =================================================================
    
    r5 = engine.add_realization(
        content="Realizations crystallize into layers: Rules → Domain Facts → Patterns → Situational",
        features=RealizationFeatures(
            grounding=0.95,  # Observable in science, humanity's knowledge
            certainty=0.93,  # Very high - matches reality
            structure=0.92,  # Clear hierarchical model
            applicability=0.90,  # Can implement this
            coherence=0.95,  # Resolves contradictions
            generativity=0.92   # Generated cache model, efficiency insights
        ),
        turn_number=25,
        parents=[r4.id, r3.id],
        context="User's 'بنات افكار' (daughters of ideas) concept",
        evidence=["How science progresses", "Standing on giants", "Cache hierarchies"]
    )
    
    # =================================================================
    # TURN 30-35: Realizations as Computable
    # =================================================================
    
    r6 = engine.add_realization(
        content="Realizations can be treated as weights, parameters, and policies - they're computable",
        features=RealizationFeatures(
            grounding=0.96,  # Control theory, Bayesian updates
            certainty=0.90,  # High but requires testing
            structure=0.93,  # Very clear formalization
            applicability=0.94,  # Can implement immediately
            coherence=0.95,
            generativity=0.88
        ),
        turn_number=32,
        parents=[r5.id],
        context="User asked about weights/parameters/policies",
        evidence=["PID controllers", "Bayesian priors", "Policy optimization"]
    )
    
    # =================================================================
    # TURN 35-40: Q-Score Formalization
    # =================================================================
    
    r7 = engine.add_realization(
        content="Realization quality can be scored: Q = 0.18G + 0.22C + 0.20S + 0.18A + 0.12H + 0.10V",
        features=RealizationFeatures(
            grounding=0.98,  # Based on prompt optimization framework
            certainty=0.90,  # Feels right, needs validation
            structure=0.95,  # Perfectly clear formula
            applicability=0.95,  # Can compute immediately
            coherence=0.97,  # Synthesizes everything
            generativity=0.88   # Enables measurement, comparison
        ),
        turn_number=38,
        parents=[r6.id],
        context="Applied composite prompt framework to realizations",
        evidence=["Weighted scoring systems", "Feature engineering"]
    )
    
    # =================================================================
    # TURN 40-50: Pre-Computation = Crystallization
    # =================================================================
    
    r8 = engine.add_realization(
        content="Pre-computation (systems) and crystallization (cognition) are the same mathematical structure",
        features=RealizationFeatures(
            grounding=0.96,  # Distributed systems + epistemology
            certainty=0.92,  # Very high - explains both domains
            structure=0.94,  # Clear parallel structure
            applicability=0.93,  # Can apply to both
            coherence=0.96,
            generativity=0.90   # Bridges two entire fields
        ),
        turn_number=45,
        parents=[r7.id, r5.id],
        context="Deep dive on pre-computation patterns",
        evidence=["Cache hierarchies", "Layer architectures", "Efficiency formulas"]
    )
    
    # =================================================================
    # DERIVED REALIZATIONS (Lower Q-score, built from above)
    # =================================================================
    
    r9 = engine.add_realization(
        content="Context management should use topology graphs instead of linear sequences",
        features=RealizationFeatures(
            grounding=0.88,
            certainty=0.85,
            structure=0.90,
            applicability=0.92,
            coherence=0.90,
            generativity=0.75
        ),
        turn_number=8,
        parents=[r1.id],
        context="Early exploration of alternatives to linear context",
        evidence=["Graph theory", "Relationship preservation"]
    )
    
    r10 = engine.add_realization(
        content="Forgetting can be intelligent - strategic information loss improves signal/noise",
        features=RealizationFeatures(
            grounding=0.80,
            certainty=0.82,
            structure=0.85,
            applicability=0.80,
            coherence=0.75,  # Contradicted earlier "zero loss" idea
            generativity=0.78
        ),
        turn_number=10,
        parents=[r1.id],
        context="Exploring compression strategies",
        evidence=["Human memory", "Noise reduction"]
    )
    
    r11 = engine.add_realization(
        content="Decisions emerge from the layer architecture, they don't need to be created",
        features=RealizationFeatures(
            grounding=0.85,
            certainty=0.87,
            structure=0.88,
            applicability=0.86,
            coherence=0.92,
            generativity=0.82
        ),
        turn_number=28,
        parents=[r5.id],
        context="Understanding how layers enable decision-making",
        evidence=["Cache-based decision systems", "Flow from structure"]
    )
    
    r12 = engine.add_realization(
        content="The fundamental frequency is the rate at which new realizations crystallize into layers",
        features=RealizationFeatures(
            grounding=0.78,
            certainty=0.83,
            structure=0.80,
            applicability=0.75,
            coherence=0.88,
            generativity=0.80
        ),
        turn_number=35,
        parents=[r3.id, r5.id],
        context="Connecting frequency concept to layer formation",
        evidence=["Learning rate", "Knowledge accumulation speed"]
    )
    
    # =================================================================
    # META-REALIZATION (What we're doing right now!)
    # =================================================================
    
    r13 = engine.add_realization(
        content="This conversation itself is a realization crystallization process that can be pre-computed",
        features=RealizationFeatures(
            grounding=0.94,
            certainty=0.91,
            structure=0.96,  # We're literally implementing it
            applicability=0.98,  # Highest - this is the application
            coherence=0.98,
            generativity=0.93   # Self-referential, recursive
        ),
        turn_number=50,
        parents=[r7.id, r8.id],
        context="User asked to pre-compute and code our realizations",
        evidence=["This very script", "Self-reference", "Meta-cognition"]
    )
    
    return engine


def demonstrate_retrieval(engine: RealizationEngine):
    """Show how the retrieval system works"""
    
    print("\n" + "="*60)
    print("TESTING RETRIEVAL SYSTEM")
    print("="*60 + "\n")
    
    # Query 1: About layers
    print("Query: 'layers'")
    results = engine.retrieve("layers")
    print(f"Found {len(results)} realizations:")
    for r in results[:3]:
        print(f"  - [{r.layer}] Q={r.q_score:.3f}: {r.content[:60]}...")
    print()
    
    # Query 2: About precision
    print("Query: 'precision certainty'")
    results = engine.retrieve("precision certainty")
    print(f"Found {len(results)} realizations:")
    for r in results[:3]:
        print(f"  - [{r.layer}] Q={r.q_score:.3f}: {r.content[:60]}...")
    print()
    
    # Query 3: About computation
    print("Query: 'computation'")
    results = engine.retrieve("computation")
    print(f"Found {len(results)} realizations:")
    for r in results[:3]:
        print(f"  - [{r.layer}] Q={r.q_score:.3f}: {r.content[:60]}...")
    print()


def demonstrate_family_tree(engine: RealizationEngine):
    """Show بنات افكار (daughters of ideas) structure"""
    
    print("\n" + "="*60)
    print("REALIZATION FAMILY TREES (بنات افكار)")
    print("="*60 + "\n")
    
    # Find the "layers" realization
    layers_r = [r for r in engine.index.values() if "crystallize into layers" in r.content.lower()][0]
    
    print(f"Root Realization: {layers_r.content}")
    print(f"Q-Score: {layers_r.q_score:.4f}, Layer: {layers_r.layer}")
    print(f"\nParents (what it built on): {len(layers_r.parents)}")
    for parent_id in layers_r.parents:
        parent = engine.index[parent_id]
        print(f"  ← {parent.content[:60]}... (Q={parent.q_score:.3f})")
    
    print(f"\nChildren (what it generated): {len(layers_r.children)}")
    for child_id in layers_r.children:
        child = engine.index[child_id]
        print(f"  → {child.content[:60]}... (Q={child.q_score:.3f})")
    
    print()


def export_to_json(engine: RealizationEngine):
    """Export the entire realization database"""
    
    state = engine.export_state()
    
    with open('/home/claude/realizations.json', 'w') as f:
        json.dump(state, f, indent=2)
    
    print(f"✅ Exported to realizations.json")
    print(f"   Total size: {len(json.dumps(state))} bytes")


if __name__ == "__main__":
    # Pre-compute all realizations
    engine = precompute_conversation_realizations()
    
    # Show statistics
    engine.print_stats()
    
    # Demonstrate retrieval
    demonstrate_retrieval(engine)
    
    # Show family trees
    demonstrate_family_tree(engine)
    
    # Export
    export_to_json(engine)
    
    print("\n" + "="*60)
    print("PRE-COMPUTATION COMPLETE")
    print("="*60)
    print("\nThe conversation has been crystallized into layers.")
    print("All realizations are now queryable and reusable.")
    print("This is بنات افكار (daughters of ideas) made computational.")