refactor: engine.py

entities.py

@@ -0,0 +1,88 @@
+import logging
+
+from langchain_core.messages import HumanMessage, SystemMessage
+
+from llm_runtime import _format_prompt, _normalize_llm_output, llm
+from memory import EntityMemory, MemoryEntry
+from time_utils import WorldClock
+
+logger = logging.getLogger(__name__)
+
+
+class Entity:
+    def __init__(
+        self,
+        name,
+        traits,
+        stats,
+        voice_sample,
+        current_mood="Neutral",
+        entity_id=None,
+    ):
+        self.name = name
+        self.traits = traits
+        self.stats = stats
+        self.current_mood = current_mood
+        self.memory = EntityMemory()
+        # TIER 1: The Short-Term Buffer (Verbatim)
+        self.chat_buffer = []
+        self.voice_sample = voice_sample
+        self.entity_id = entity_id
+
+    def perceive(self, entry: MemoryEntry):
+        self.memory.save(entry)
+
+    def reflect_and_summarize(self, world_clock: WorldClock, location: str):
+        """Converts Tier 1 (Buffer) into Tier 2 (Long-term Subjective Memory)."""
+        if not self.chat_buffer:
+            return
+
+        dialogue_text = "\n".join(
+            [f"{m['role_name']}: {m['content']}" for m in self.chat_buffer]
+        )
+
+        # The Subjective Filter Prompt
+        summary_prompt = [
+            SystemMessage(
+                content=f"""
+You are the private inner thoughts of {self.name}.
+Traits: {", ".join(self.traits)}.
+Mood: {self.current_mood}.
+Voice Reference: {self.voice_sample}
+                
+Think about what just happened.
+- No META-TALK, Do not use 'player', 'interaction', 'entity', or 'dialogue'
+- BE SUBJECTIVE. If you hated the talk or loved it, then express that.
+- USE YOUR VOICE. Match the style of your Voice Reference
+- Focus only on facts learned or feelings toward the person"""
+            ),
+            HumanMessage(
+                content=f"""
+What just happened? Context:\n{dialogue_text}"""
+            ),
+        ]
+
+        logger.info("LLM prompt (reflection):\n%s", _format_prompt(summary_prompt))
+        summary = _normalize_llm_output(llm.invoke(summary_prompt).content)
+        logger.info("SYSTEM: %s reflected on the talk: '%s'", self.name, summary)
+
+        chat_entities = sorted(
+            {
+                m["role_id"]
+                for m in self.chat_buffer
+                if m.get("role_id") and m.get("role_id") != self.entity_id
+            }
+        )
+        reflection = MemoryEntry(
+            content=f"Past Conversation Summary: {summary}",
+            event_type="reflection",
+            timestamp_str=world_clock.get_time_str(),
+            location=location,
+            entities=chat_entities,
+        )
+        self.perceive(reflection)
+        self.chat_buffer = []  # Clear buffer after archiving
+
+
+class Player(Entity):
+    pass