Complete step 14-1

npc_social_network/npc/npc_base.py

@@ -3,9 +3,11 @@ from .npc_memory import Memory, MemoryStore
 from .npc_emotion import EmotionManager
 from .npc_behavior import BehaviorManager
 from .emotion_config import EMOTION_LIST, EMOTION_CATEGORY_MAP, EMOTION_DECAY_RATE, PERSONALITY_TEMPLATE
-from .emotion_config import POSITIVE_RELATION_EMOTIONS, NEGATIVE_RELATION_EMOTIONS, COGNITIVE_RELATION_EMOTIONS, AGE_PROFILE
+from .emotion_config import POSITIVE_RELATION_EMOTIONS, NEGATIVE_RELATION_EMOTIONS, COGNITIVE_RELATION_EMOTIONS
+from .personality_config import AGE_PROFILE, PERSONALITY_PROFILE
 from .npc_relationship import RelationshipManager
 from ..models.llm_helper import query_llm_for_emotion, query_llm_for_response, query_llm_for_response_with_context
+from datetime import datetime
 import random
 import copy
 
@@ -62,29 +64,9 @@ class NPC:
         # baseline buffer를 EmotionManager에 등록
         self.emotion.set_baseline(baseline_buffer)
 
-        # Personality 성격 프로필 기반
-        # profile: stable / emotional / logical (추후 더 추가)
-        personality_profile = {
-            "stable": {
-                "sensitive": (0.002, 0.004),
-                "stoic": (0.002, 0.004),
-                "cognitive_bias": (0.002, 0.004)
-            },
-            "emotional":{
-                "sensitive": (0.006, 0.01),
-                "stoic": (0.002, 0.004),
-                "cognitive_bias": (0.004, 0.006)
-            },
-            "logical":{
-                "sensitive": (0.002, 0.004),
-                "stoic": (0.004, 0.006),
-                "cognitive_bias":(0.006, 0.01)
-            },
-        }
-
         # 프로필 지정 → 기본은 "stable", 나중에 NPC마다 다르게 부여
         profile_type = "stable"
-        profile = personality_profile[profile_type]
+        profile = PERSONALITY_PROFILE[profile_type]
         
         # Personality 변화 속도 개별 설정
         self.personality_change_rate = {
@@ -96,8 +78,13 @@ class NPC:
         # Personality baseline 저장 (초기값 복사)
         self.personality_baseline = copy.deepcopy(self.personality)
 
-        # 인생 단계 profile 추가
-        self.personality_stage = "adolescence"    # 기본 "adolescence", NPC마다 다르게 설정 가능
+        # 나이 추가 (0 ~ 80세 기본 랜덤 초기화 → 나중에 생성 시 age 지정 가능)
+        self.age = random.randint(0, 80)
+
+        # 초기 personality_stage는 update_personality_stage()에서 자동 설정
+        self.personality_stage = None
+        self.update_personality_stage()
+
 
     def move(self):
         """
@@ -469,9 +456,13 @@ class NPC:
 
             # 인생 단계별 변화율 적용
             stage_factor = AGE_PROFILE.get(self.personality_stage, 1.0)
+            # memory_age_in_days 계산 (현재 시점 - memory timestamp)
+            memory_age_in_days = max(0, (datetime.now() - m.timestamp).days)
+            # importance scaling factor 계산
+            importance_scaling = self.get_memory_importance_scaling(memory_age_in_days)
 
             # 최종 weight 계산
-            weight = stage_factor * (m.importance / 10.0) * type_weight * emotion_weight * emotion_bias_factor    # importance 1~10 → 0.1 ~ 1.0
+            weight = stage_factor * (m.importance / 10.0) * type_weight * emotion_weight * emotion_bias_factor * importance_scaling    # importance 1~10 → 0.1 ~ 1.0
             if m.emotion in POSITIVE_RELATION_EMOTIONS:
                 self.personality["sensitive"] = min(1.0, self.personality["sensitive"] + self.personality_change_rate["sensitive"] * weight)
                 self.personality["stoic"] = max(0.0, self.personality["stoic"] - self.personality_change_rate["stoic"] * weight)
@@ -500,4 +491,43 @@ class NPC:
         """
         시간이 지남에 따라 기억 importance 감소
         """
-        self.memory_store.decay_memories()
+        self.memory_store.decay_memories()
+
+    def update_personality_stage(self):
+        """
+        현재 나이에 따라 personality_stage 자동 업데이트
+        """
+        if self.age < 3:
+            self.personality_stage = "infancy_and_toddlerhood"
+        elif self.age < 6:
+            self.personality_stage = "early_childhood"
+        elif self.age < 12:
+            self.personality_stage = "middle_childhood"
+        elif self.age < 18:
+            self.personality_stage = "adolescence"
+        elif self.age < 30:
+            self.personality_stage = "young_adulthood"
+        elif self.age < 65:
+            self.personality_stage = "middle_adulthood"
+        else:
+            self.personality_stage = "older_adulthood"
+
+    def get_memory_importance_scaling(self, memory_age_in_days: float) -> float:
+        """
+        NPC의 나이에 따라 memory_age_in_days 기반 scaling factor 반환
+        - 젊은 NPC → 최근 기억 더 강조
+        - 노년 NPC → 오래된 기억 더 강조
+        """
+        if self.personality_stage in [
+            "infancy_and_toddlerhood", "early_choldhood", "middle_childhood",
+            "adolescence", "young_adulthood"
+            ]:
+            # 최근 기억 강조 → 오래된 기억 영향 약화
+            return max(0.5, 1.5 - 0.05 * memory_age_in_days)    # 최대 1.5, 점점 감소
+        elif self.personality_stage in [
+            "middle_adulthood", "older_adulthood"
+        ]:
+            return min(1.5, 0.5 + 0.05 * memory_age_in_days)    # 최소 0.5, 점점 증가
+        else:
+            # fallback → 정상화
+            return 1.0

npc_social_network/npc/npc_memory_embedder.py

@@ -0,0 +1,51 @@
+# portfolio/npc_social_network/npc/npc_memory_embedder.py
+
+from sentence_transformers import SentenceTransformer
+import numpy as np
+import faiss
+import os
+from .npc_memory import MemoryStore
+from .npc_base import NPC
+
+# 사전 훈련된 문장 임베딩 모델
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# 문장 임베딩 저장 장소
+VECTOR_DIR = "portfolio/npc_social_network/data/vectorstores"
+
+def memory_to_text(memory):
+    """
+    Memoty 객체를 임베딩 가능한 텍스트 형태로 변환
+    """
+    lines = [f"Content: {memory.content}"]
+    if memory.emotion:
+        emotion_str = ", ".join([f"{k}:{v:.2f}" for k, v in memory.emotion.items()])
+        lines.append(f"Emotion: {emotion_str}")
+    lines.append(f"Importance: {memory.importance}")
+    return " | ".join(lines)
+
+def embed_memory(memory):
+    """
+    Memory 객체 1개를 벡터로 임베딩
+    """
+    text = memory_to_text(memory)
+    embedding = model.encode(text)
+    return embedding
+
+def embed_npc_memories(npc: NPC):
+    """
+    특정 NPC의 기억 전체를 임베딩하고 FAISS index로 저장
+    """
+    if not os.path.exists(VECTOR_DIR):
+        os.makedirs(VECTOR_DIR)
+
+    memories = npc.memory.get_all()
+    texts = [memory_to_text(mem) for mem in memories]
+    embeddings = model.encode(texts)
+
+    index = faiss.IndexFlatL2(len(embeddings[0]))
+    index.add(np.array(embeddings, dtype=np.float32))
+
+    faiss.write_index(index, f"{VECTOR_DIR}/{npc.name}")
+
+    print(f"[임베딩 완료] {npc.name}의 기억 {len(memories)}개를 벡터화하여 저장했습니다.")

npc_social_network/npc/personality_config.py

@@ -0,0 +1,32 @@
+# portfolio/npc_social_network/npc/personality_config.py
+
+# 인생 단계별 Personality 변화율 정의
+AGE_PROFILE = {
+    "infancy_and_toddlerhood": 3.0,  # (0-3 세) Temperament & attachment foundation — change rate very high
+    "early_childhood": 2.0,          # (3-6 세) Initial personality structure emergence — high change rate
+    "middle_childhood": 1.5,         # (6-12 세) Personality structure solidification — still high change rate
+    "adolescence": 1.0,              # (12-18 세) Identity formation — moderate to high change rate
+    "young_adulthood": 0.5,          # (18-30 세) Personality stabilization — moderate change rate
+    "middle_adulthood": 0.2,         # (30-65 세) Personality highly stable — low change rate
+    "older_adulthood": 0.02          # (65- 세) Personality largely fixed — minimal change
+}
+
+# Personality 성격 프로필 기반
+# profile: stable / emotional / logical (추후 더 추가)
+PERSONALITY_PROFILE = {
+    "stable": {
+        "sensitive": (0.002, 0.004),
+        "stoic": (0.002, 0.004),
+        "cognitive_bias": (0.002, 0.004)
+    },
+    "emotional":{
+        "sensitive": (0.006, 0.01),
+        "stoic": (0.002, 0.004),
+        "cognitive_bias": (0.004, 0.006)
+    },
+    "logical":{
+        "sensitive": (0.002, 0.004),
+        "stoic": (0.004, 0.006),
+        "cognitive_bias":(0.006, 0.01)
+    },
+}