fso_linguistic_parser.py

import hashlib
import json

class LinguisticParser:
    """
    Law XII Component: The Advanced Linguistic Ingestor
    Provides SVOC (Subject-Verb-Object-Context) shattering for complex reasoning traces.
    Maps thought processes to Torus trajectories.
    """
    def __init__(self, m=256, k=4):
        self.m = m
        self.k = k

    def _get_coord(self, text, fiber=2):
        h = hashlib.sha256(text.encode()).digest()
        coords = [h[i % len(h)] % self.m for i in range(self.k - 1)]
        w = (fiber - sum(coords)) % self.m
        return tuple(coords + [w])

    def generate_reasoning_trace(self, goal):
        """
        Decomposes a goal into a series of SVOC-structured thought atoms.
        Creates a 'Reasoning Trace' as a sequence of manifold coordinates.
        """
        print(f"\n--- [LINGUISTIC PARSER]: Generating Reasoning Trace for '{goal}' ---")
        
        # In a production system, this would use a Dependency Parser (e.g., spaCy)
        # Here we simulate the structural shattering of a thought process
        trace = []
        
        # Thought 1: Perception (Subject: TGI, Verb: Identify, Object: Goal)
        t1 = f"TGI identifies goal: {goal}"
        # Thought 2: Retrieval (Subject: Manifold, Verb: Search, Object: Knowledge)
        t2 = f"Manifold searches Fiber 2 for context."
        # Thought 3: Action (Subject: Engine, Verb: Execute, Object: Solution)
        t3 = f"Engine executes Hamiltonian path to target."
        
        thoughts = [t1, t2, t3]
        for i, thought in enumerate(thoughts):
            coord = self._get_coord(thought)
            trace.append({
                "step": i + 1,
                "svoc": thought,
                "coord": coord,
                "fiber": 2
            })
            print(f"      [TRACE STEP {i+1}]: {thought} -> @ {coord}")
            
        return trace

    def ingest_language_spec(self, lang_name, spec_text):
        """Standard shattering of linguistic rules."""
        atoms = []
        units = spec_text.split(". ")
        for unit in units:
            if unit.strip():
                coord = self._get_coord(unit)
                atoms.append({"data": unit.strip(), "fiber": 2, "coord": coord, "type": "linguistic_atom"})
        return atoms

if __name__ == "__main__":
    parser = LinguisticParser()
    parser.generate_reasoning_trace("Optimize topological routing.")