Fix/memory bug fix (#111)

* 图谱数据量限制数量去掉

* 图谱数据量限制数量去掉

* 图谱数据量限制数量去掉

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 用户详情优化

api/app/schemas/memory_episodic_schema.py

@@ -1,9 +1,51 @@
 """
 情景记忆的请求和响应模型
 """
+from abc import ABC
 from pydantic import BaseModel, Field
 from typing import Optional
 
+type_mapping = {
+            "Person": "人物实体节点",
+            "Organization": "组织实体节点",
+            "ORG": "组织实体节点",
+            "Location": "地点实体节点",
+            "LOC": "地点实体节点",
+            "Event": "事件实体节点",
+            "Concept": "概念实体节点",
+            "Time": "时间实体节点",
+            "Position": "职位实体节点",
+            "WorkRole": "职业实体节点",
+            "System": "系统实体节点",
+            "Policy": "政策实体节点",
+            "HistoricalPeriod": "历史时期实体节点",
+            "HistoricalState": "历史国家实体节点",
+            "HistoricalEvent": "历史事件实体节点",
+            "EconomicFactor": "经济因素实体节点",
+            "Condition": "条件实体节点",
+            "Numeric": "数值实体节点"
+        }
+class EmotionType(ABC):
+    JOY_TYPE = "joy"
+    SURPRISE_TYPE = "surprise"
+    SANDROWNESS_TYPE = "sadness"
+    FEAR_TYPE = "fear"
+    ANGET_TYPE="anger"
+    NEUTRAL_TYPE="neutral"
+    EMOTION_MAPPING={
+        "joy":"愉快",
+        "surprise":"惊喜",
+        "sadness":"悲伤",
+        "fear":"恐惧",
+        "anger":"生气",
+        "neutral":"中性"
+    }
+class EmotionSubject(ABC):
+    SUBJECT_MAPPING={
+        "self":"自己",
+        "other":"别人",
+        "object":"事物对象"
+    }
 
 class EpisodicMemoryOverviewRequest(BaseModel):
     """情景记忆总览查询请求"""

api/app/services/memory_entity_relationship_service.py

@@ -15,6 +15,8 @@ from neo4j.time import DateTime as Neo4jDateTime
 import json
 from datetime import datetime
 
+from app.schemas.memory_episodic_schema import EmotionType
+
 logger = logging.getLogger(__name__)
 
 class MemoryEntityService:
@@ -123,7 +125,7 @@ class MemoryEntityService:
         extracted_entity_list = self._deduplicate_dict_list(extracted_entity_list)
         
         # 合并所有数据并处理相同text的合并
-        all_timeline_data = memory_summary_list + statement_list + extracted_entity_list
+        all_timeline_data = memory_summary_list + statement_list
         all_timeline_data = self._merge_same_text_items(all_timeline_data)
         
         result = {
@@ -496,11 +498,11 @@ class MemoryEmotion:
                 length_data.append(emotion_intensity)
             if emotion_type is not None and emotion_intensity is not None and formatted_created_at is not None:
                 # 使用(emotion_type, created_at)作为分组键
-                if emotion_type in {"joy", "surprise"}:
+                if emotion_type in {EmotionType.JOY_TYPE, EmotionType.SURPRISE_TYPE}:
                     emotion_type='positive'
-                elif emotion_type in {"sadness", "fear", "anger"}:
+                elif emotion_type in {EmotionType.SANDROWNESS_TYPE, EmotionType.FEAR_TYPE, EmotionType.ANGET_TYPE}:
                     emotion_type='negative'
-                elif emotion_type=='neutral':
+                elif emotion_type==EmotionType.NEUTRAL_TYPE:
                     emotion_type='neutral'
                 group_key = (emotion_type, formatted_created_at)
                 # 累加emotion_intensity

api/app/services/user_memory_service.py

@@ -15,6 +15,8 @@ from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
 from app.repositories.end_user_repository import EndUserRepository
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+from app.schemas.memory_episodic_schema import type_mapping, EmotionType, EmotionSubject
+
 from app.services.memory_base_service import MemoryBaseService
 from app.services.memory_config_service import MemoryConfigService
 from pydantic import BaseModel, Field
@@ -1332,7 +1334,7 @@ async def analytics_graph_data(
     db: Session,
     end_user_id: str,
     node_types: Optional[List[str]] = None,
-    limit: int = 100,
+    limit: int = 130,
     depth: int = 1,
     center_node_id: Optional[str] = None
 ) -> Dict[str, Any]:
@@ -1416,12 +1418,14 @@ async def analytics_graph_data(
                 elementId(n) as id,
                 labels(n)[0] as label,
                 properties(n) as properties
+            LIMIT $limit
             """
             node_params = {
                 "group_id": end_user_id,
-                # "limit": limit
+                "limit": limit
             }
 
+
         # 执行节点查询
         node_results = await _neo4j_connector.execute_query(node_query, **node_params)
         
@@ -1576,10 +1580,15 @@ async  def _extract_node_properties(label: str, properties: Dict[str, Any],node_
     for field in allowed_fields:
         if field in properties:
             value = properties[field]
+            if str(field) == 'entity_type':
+                value=type_mapping.get(value,'')
+            if str(field)=="emotion_type":
+                value=EmotionType.EMOTION_MAPPING.get(value)
+            if str(field)=="emotion_subject":
+                value=EmotionSubject.SUBJECT_MAPPING.get(value)
             # 清理 Neo4j 特殊类型
             filtered_props[field] = _clean_neo4j_value(value)
     filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0]
-    print(filtered_props)
     return filtered_props