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| import hashlib | |
| from typing import Dict, List, Tuple, Any, Optional | |
| try: | |
| from research.tlm import TopologicalLanguageModel | |
| tlm_ready = True | |
| except ImportError: | |
| tlm_ready = False | |
| class ActionMapper: | |
| """ | |
| TGI Action-Coordinate Mapping. | |
| Translates topological paths and coordinates into system-level 'Agentic' actions. | |
| Ensures the TGI can 'do' things as a result of manifold reasoning. | |
| Guided by Law VIII (Multi-Modal Consistency). | |
| """ | |
| def __init__(self, m: int = 255): | |
| self.m = m | |
| self.action_space = { | |
| 0: "DEPLOY_RENDER", # Agentic Cloud Deploy | |
| 1: "SQL_SUPABASE", # Agentic DB Query | |
| 2: "QUERY_DOCS", # Agentic Docs Retrieval | |
| 3: "NOTIFY", # Push Notification | |
| 4: "LOG", # System Log | |
| 5: "COMPUTE", # Invoke Algebraic Core | |
| 6: "INGEST", # Ingest data into Ontology | |
| 7: "LIFT", # Execute k-expansion | |
| 8: "RESPONSE", # Generate Natural Language | |
| 9: "REFLECT", # Topological Reflection | |
| 10: "NOP" # No Operation | |
| } | |
| self.tlm = TopologicalLanguageModel(m=m, k=3) if tlm_ready else None | |
| def map_coord_to_action(self, coord: Tuple[int, ...]) -> Dict[str, Any]: | |
| """Maps a specific coordinate in Z_m^k to an action and its parameters.""" | |
| # Use simple deterministic mapping for prototype | |
| s = sum(coord) | |
| action_idx = s % len(self.action_space) | |
| action_name = self.action_space[action_idx] | |
| intensity = (coord[0] / self.m) if len(coord) > 0 else 0.5 | |
| focus = (coord[1] / self.m) if len(coord) > 1 else 0.5 | |
| return { | |
| "action": action_name, | |
| "intensity": round(intensity, 4), | |
| "focus": round(focus, 4), | |
| "original_coord": coord | |
| } | |
| def path_to_action_sequence(self, path: List[Tuple[int, ...]]) -> List[Dict[str, Any]]: | |
| """Converts a Hamiltonian path into a sequence of agentic actions.""" | |
| return [self.map_coord_to_action(c) for c in path] | |
| def resolve_intent(self, intent_text: str) -> Tuple[int, ...]: | |
| """ | |
| Lifts a textual intent into a coordinate for action execution. | |
| Uses grounded TLM semantic mapping and Law VIII (Multi-Modal Consistency). | |
| """ | |
| from research.tgi_parser import TGIParser | |
| parser = TGIParser() | |
| parsed = parser.parse_input(intent_text) | |
| # Use the domain as a fiber anchor (Law VIII) | |
| fiber_map = { | |
| "math": 0, "language": 5, "vision": 3, "neural": 7, | |
| "knowledge": 2, "heisenberg": 0, "tsp": 4 | |
| } | |
| target_fiber = fiber_map.get(parsed["domain"], 6) # Default to API_MCP fiber | |
| if self.tlm: | |
| tokens = self.tlm.tokenize(intent_text) | |
| # Lift the semantic tokens to a single coordinate in G_m^3 | |
| # Ensure the coordinate sum satisfies the fiber anchor (Law V) | |
| val = sum(tokens) % self.m | |
| bias = sum(ord(c) for c in intent_text) % self.m | |
| # Deterministically force the 3rd coordinate to close the fiber sum | |
| z = (target_fiber - val - bias) % self.m | |
| return (val, bias, z) | |
| # Fallback to deterministic hashing anchored to the fiber | |
| h = hashlib.md5(intent_text.lower().encode()).digest() | |
| x = (h[0] + target_fiber) % self.m | |
| y = (h[1] + target_fiber) % self.m | |
| z = (target_fiber - x - y) % self.m | |
| return (x, y, z) | |
| if __name__ == "__main__": | |
| am = ActionMapper() | |
| print("═══ TGI ACTION MAPPER UPDATED (TLM Grounded) ═══") | |
| test_intents = ["Deploy", "Query", "Help", "Ingest", "Lift"] | |
| for intent in test_intents: | |
| coord = am.resolve_intent(intent) | |
| action = am.map_coord_to_action(coord) | |
| print(f"Intent: '{intent}' -> Coord: {coord} -> Action: {action['action']}") | |