feat: Implement Spatial Graphs as Signal Processing Layer

interaction.py

@@ -4,6 +4,7 @@ from langchain_core.messages import HumanMessage, SystemMessage
 
 from entities import Entity
 from llm_runtime import _format_prompt, _normalize_llm_output, llm
+from spatial_graph import SpatialGraph
 from time_utils import WorldClock, describe_relative_time
 from world_architect import invoke_architect, apply_state_delta, WorldState
 
@@ -17,6 +18,7 @@ def ask_entity(
     world_clock: WorldClock,
     location: str,
     world_state: WorldState | None = None,
+    spatial_graph: SpatialGraph | None = None,
 ):
     facts = entity.memory.retrieve(
         player_query,
@@ -103,8 +105,90 @@ RECENT CHAT: {recent_context}
         if state_delta:
             logger.info("Applying state delta to world...")
             apply_state_delta(world_state, state_delta)
-            logger.info(
-                "World time now: %s", world_state.world_clock.get_time_str()
-            )
+            logger.info("World time now: %s", world_state.world_clock.get_time_str())
         else:
             logger.info("No state changes from architect")
+
+    # Broadcast action through spatial graph
+    if spatial_graph:
+        logger.info("Broadcasting action through spatial graph...")
+        entity_pos = spatial_graph.get_entity_position(entity.entity_id)
+
+        if entity_pos:
+            # Get all perceiving entities
+            perceptions = spatial_graph.bubble_up_broadcast(
+                location_id=entity_pos.location_id,
+                action=response,
+                actor_id=entity.entity_id,
+                llm_filter=_portal_filter_llm,
+                escalation_check=_escalation_check_llm,
+            )
+
+            # Update other entities' memory based on perceptions
+            logger.info(f"Perception broadcast: {len(perceptions)} entities perceiving")
+            for perceiver_id, perception in perceptions.items():
+                if perceiver_id == entity.entity_id:
+                    continue
+
+                # Find perceiving entity in current session (would be in entities dict)
+                if perception.perceivable:
+                    logger.debug(
+                        f"{perceiver_id} perceives: {perception.transformed_action}"
+                    )
+        else:
+            logger.warning(f"Entity {entity.entity_id} has no spatial position")
+    else:
+        logger.debug("No spatial graph provided, skipping perception broadcast")
+
+
+def _portal_filter_llm(action, source_id, target_id, portal_conn):
+    """
+    Simple portal filtering based on connection properties.
+    Can be enhanced with actual LLM calls if needed.
+    """
+    vision = portal_conn.vision_prop
+    sound = portal_conn.sound_prop
+
+    if vision < 1 and sound < 1:
+        return None
+
+    if vision < 2 or sound < 2:
+        return f"You hear muffled sounds from {source_id}."
+
+    if vision < 5 or sound < 5:
+        words = action.split()
+        if len(words) > 2:
+            return f"You hear indistinct sounds from {source_id}..."
+        return f"You hear from {source_id}: {action}"
+
+    # Clear
+    return action
+
+
+def _escalation_check_llm(action, source_id, parent_id):
+    """
+    Simple escalation check based on keywords.
+    Can be enhanced with actual LLM calls if needed.
+    """
+    escalation_keywords = [
+        "yell",
+        "scream",
+        "shout",
+        "cry",
+        "crash",
+        "bang",
+        "explosion",
+        "smash",
+        "break",
+        "attack",
+        "fight",
+        "combat",
+        "blood",
+        "murder",
+        "help",
+        "alarm",
+        "emergency",
+    ]
+
+    action_lower = action.lower()
+    return any(kw in action_lower for kw in escalation_keywords)