Merge #47 into develop from fix/othername-name

[fix]Fix the bug that affects user memory. * fix/othername-name: (11 commits squashed) - [fix]Fix the issue with the display of the user's memory list - [fix]Ensure the six dimensions of emotional expression - [fix]Fix the issue with the display of the user's memory list - [fix]Ensure the six dimensions of emotional expression - Merge branch 'fix/othername-name' of codeup.aliyun.com:redbearai/python/redbear-mem-open into fix/othername-name - [fix]Restore the display of memory types - [fix]Fix the issue with the display of the user's memory list - [fix]Ensure the six dimensions of emotional expression - [fix]Restore the display of memory types - Merge branch 'fix/othername-name' of codeup.aliyun.com:redbearai/python/redbear-mem-open into fix/othername-name - [updated]Update the title of the "analytics/node_statistics" log Signed-off-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com> Reviewed-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com> Merged-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com> CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/47
2025-12-24 10:11:31 +00:00
parent 6338edda11
commit 9cf8d5cb0a
6 changed files with 129 additions and 33 deletions
--- a/api/app/controllers/user_memory_controllers.py
+++ b/api/app/controllers/user_memory_controllers.py
@@ -14,6 +14,7 @@ from app.core.error_codes import BizCode
 from app.services.user_memory_service import (
    UserMemoryService,
    analytics_node_statistics,
+    analytics_memory_types,
    analytics_graph_data,
 )
 from app.schemas.response_schema import ApiResponse
@@ -185,21 +186,17 @@ async def get_node_statistics_api(
        api_logger.warning(f"用户 {current_user.username} 尝试查询节点统计但未选择工作空间")
        return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
    
-    api_logger.info(f"节点统计请求: end_user_id={end_user_id}, user={current_user.username}, workspace={workspace_id}")
+    api_logger.info(f"记忆类型统计请求: end_user_id={end_user_id}, user={current_user.username}, workspace={workspace_id}")
    
    try:
-        result = await analytics_node_statistics(db, end_user_id)
+        # 调用新的记忆类型统计函数
+        result = await analytics_memory_types(db, end_user_id)
        
-        # 检查是否有错误消息
-        if "message" in result and result["total"] == 0:
-            api_logger.warning(f"节点统计查询返回空结果: {result.get('message')}")
-            return success(data=result, msg=result.get("message", "查询成功"))
-        
-        api_logger.info(f"成功获取节点统计: end_user_id={end_user_id}, total={result['total']}")
+        api_logger.info(f"成功获取记忆类型统计: end_user_id={end_user_id}, 感知记忆={result.get('感知记忆', 0)}")
        return success(data=result, msg="查询成功")
    except Exception as e:
-        api_logger.error(f"节点统计查询失败: end_user_id={end_user_id}, error={str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "节点统计查询失败", str(e))
+        api_logger.error(f"记忆类型查询失败: end_user_id={end_user_id}, error={str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "记忆类型查询失败", str(e))

@router.get("/analytics/graph_data", response_model=ApiResponse)
 async def get_graph_data_api(
@@ -293,7 +290,7 @@ async def get_end_user_profile(
        # 构建响应数据
        profile_data = EndUserProfileResponse(
            id=end_user.id,
-            name=end_user.name,
+            other_name=end_user.other_name,
            position=end_user.position,
            department=end_user.department,
            contact=end_user.contact,
@@ -364,7 +361,7 @@ async def update_end_user_profile(
        # 构建响应数据
        profile_data = EndUserProfileResponse(
            id=end_user.id,
-            name=end_user.name,
+            other_name=end_user.other_name,
            position=end_user.position,
            department=end_user.department,
            contact=end_user.contact,
--- a/api/app/models/end_user_model.py
+++ b/api/app/models/end_user_model.py
@@ -19,7 +19,6 @@ class EndUser(Base):
    updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now)
    
    # 用户基本信息字段
-    name = Column(String, nullable=True, comment="姓名")
    position = Column(String, nullable=True, comment="职位")
    department = Column(String, nullable=True, comment="部门")
    contact = Column(String, nullable=True, comment="联系方式")
--- a/api/app/schemas/end_user_schema.py
+++ b/api/app/schemas/end_user_schema.py
@@ -18,7 +18,6 @@ class EndUser(BaseModel):
    updated_at: datetime.datetime = Field(description="更新时间", default_factory=datetime.datetime.now)
    
    # 用户基本信息字段
-    name: Optional[str] = Field(description="姓名", default=None)
    position: Optional[str] = Field(description="职位", default=None)
    department: Optional[str] = Field(description="部门", default=None)
    contact: Optional[str] = Field(description="联系方式", default=None)
@@ -32,7 +31,7 @@ class EndUserProfileResponse(BaseModel):
    model_config = ConfigDict(from_attributes=True)
    
    id: uuid.UUID = Field(description="终端用户ID")
-    name: Optional[str] = Field(description="姓名", default=None)
+    other_name: Optional[str] = Field(description="其他名称", default="")
    position: Optional[str] = Field(description="职位", default=None)
    department: Optional[str] = Field(description="部门", default=None)
    contact: Optional[str] = Field(description="联系方式", default=None)
@@ -44,7 +43,7 @@ class EndUserProfileResponse(BaseModel):
 class EndUserProfileUpdate(BaseModel):
    """终端用户基本信息更新请求模型"""
    end_user_id: str = Field(description="终端用户ID")
-    name: Optional[str] = Field(description="姓名", default=None)
+    other_name: Optional[str] = Field(description="其他名称", default="")
    position: Optional[str] = Field(description="职位", default=None)
    department: Optional[str] = Field(description="部门", default=None)
    contact: Optional[str] = Field(description="联系方式", default=None)
--- a/api/app/services/emotion_analytics_service.py
+++ b/api/app/services/emotion_analytics_service.py
@@ -65,19 +65,9 @@ class EmotionAnalyticsService:
        """获取情绪标签统计
        
        查询指定用户的情绪类型分布，包括计数、百分比和平均强度。
-        
-        Args:
-            end_user_id: 宿主ID（用户组ID）
-            emotion_type: 可选的情绪类型过滤
-            start_date: 可选的开始日期（ISO格式）
-            end_date: 可选的结束日期（ISO格式）
-            limit: 返回结果的最大数量
-            
-        Returns:
-            Dict: 包含情绪标签统计的响应数据：
-                - tags: 情绪标签列表
-                - total_count: 总情绪数量
-                - time_range: 时间范围信息
+        确保返回所有6个情绪维度（joy、sadness、anger、fear、surprise、neutral），
+        即使某些维度没有数据也会返回count=0的记录。
+
        """
        try:
            logger.info(f"获取情绪标签统计: user={end_user_id}, type={emotion_type}, "
@@ -92,8 +82,34 @@ class EmotionAnalyticsService:
                limit=limit
            )
            
+            # 定义所有6个情绪维度
+            all_emotion_types = ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral']
+            
+            # 将查询结果转换为字典，方便查找
+            tags_dict = {tag["emotion_type"]: tag for tag in tags}
+            
+            # 补全缺失的情绪维度
+            complete_tags = []
+            for emotion in all_emotion_types:
+                if emotion in tags_dict:
+                    complete_tags.append(tags_dict[emotion])
+                else:
+                    # 如果该情绪类型不存在，添加默认值
+                    complete_tags.append({
+                        "emotion_type": emotion,
+                        "count": 0,
+                        "percentage": 0.0,
+                        "avg_intensity": 0.0
+                    })
+            
            # 计算总数
-            total_count = sum(tag["count"] for tag in tags)
+            total_count = sum(tag["count"] for tag in complete_tags)
+            
+            # 如果有数据，重新计算百分比（因为补全了0值项）
+            if total_count > 0:
+                for tag in complete_tags:
+                    if tag["count"] > 0:
+                        tag["percentage"] = round((tag["count"] / total_count) * 100, 2)
            
            # 构建时间范围信息
            time_range = {}
@@ -104,12 +120,12 @@ class EmotionAnalyticsService:
            
            # 格式化响应
            response = {
-                "tags": tags,
+                "tags": complete_tags,
                "total_count": total_count,
                "time_range": time_range if time_range else None
            }
            
-            logger.info(f"情绪标签统计完成: total_count={total_count}, tags_count={len(tags)}")
+            logger.info(f"情绪标签统计完成: total_count={total_count}, tags_count={len(complete_tags)}")
            return response
            
        except Exception as e:
--- a/api/app/services/memory_dashboard_service.py
+++ b/api/app/services/memory_dashboard_service.py
@@ -272,7 +272,7 @@ async def get_workspace_total_memory_count(
            from app.repositories.end_user_repository import EndUserRepository
            repo = EndUserRepository(db)
            end_user = repo.get_by_id(uuid.UUID(end_user_id))
-            user_name = end_user.name if end_user else None
+            user_name = end_user.other_name if end_user else None
            
            return {
                "total_memory_count": search_result.get("total", 0),
--- a/api/app/services/user_memory_service.py
+++ b/api/app/services/user_memory_service.py
@@ -534,6 +534,91 @@ async def analytics_node_statistics(
    return data


+async def analytics_memory_types(
+    db: Session,
+    end_user_id: Optional[str] = None
+) -> Dict[str, Any]:
+    """
+    统计8种记忆类型的数量
+    
+    计算规则：
+    1. 感知记忆 = statement + entity
+    2. 工作记忆 = chunk + entity
+    3. 短期记忆 = chunk
+    4. 长期记忆 = entity
+    5. 显性记忆 = 1/2 * entity
+    6. 隐形记忆 = 1/3 * entity
+    7. 情绪记忆 = statement
+    8. 情景记忆 = memory_summary
+    
+    Args:
+        db: 数据库会话
+        end_user_id: 可选的终端用户ID (UUID)，用于过滤特定用户的节点
+        
+    Returns:
+        {
+            "感知记忆": int,
+            "工作记忆": int,
+            "短期记忆": int,
+            "长期记忆": int,
+            "显性记忆": int,
+            "隐形记忆": int,
+            "情绪记忆": int,
+            "情景记忆": int
+        }
+    """
+    # 定义需要查询的节点类型
+    node_types = {
+        "Statement": "Statement",
+        "Entity": "ExtractedEntity",
+        "Chunk": "Chunk",
+        "MemorySummary": "MemorySummary"
+    }
+    
+    # 存储每种节点类型的计数
+    node_counts = {}
+    
+    # 查询每种节点类型的数量
+    for key, node_type in node_types.items():
+        if end_user_id:
+            query = f"""
+            MATCH (n:{node_type})
+            WHERE n.group_id = $group_id
+            RETURN count(n) as count
+            """
+            result = await _neo4j_connector.execute_query(query, group_id=end_user_id)
+        else:
+            query = f"""
+            MATCH (n:{node_type})
+            RETURN count(n) as count
+            """
+            result = await _neo4j_connector.execute_query(query)
+        
+        # 提取计数结果
+        count = result[0]["count"] if result and len(result) > 0 else 0
+        node_counts[key] = count
+    
+    # 获取各节点类型的数量
+    statement_count = node_counts.get("Statement", 0)
+    entity_count = node_counts.get("Entity", 0)
+    chunk_count = node_counts.get("Chunk", 0)
+    memory_summary_count = node_counts.get("MemorySummary", 0)
+    
+    # 按规则计算8种记忆类型
+    memory_types = {
+        "感知记忆": statement_count + entity_count,
+        "工作记忆": chunk_count + entity_count,
+        "短期记忆": chunk_count,
+        "长期记忆": entity_count,
+        "显性记忆": entity_count // 2,  # 1/2 entity，使用整除
+        "隐形记忆": entity_count // 3,  # 1/3 entity，使用整除
+        "情绪记忆": statement_count,
+        "情景记忆": memory_summary_count
+    }
+    
+    return memory_types
+
+
 async def analytics_graph_data(
    db: Session,
    end_user_id: str,