Refactor/memory statistics (#112)
* [refactor]Reconstructing forgotten, emotional, situational, and explicit memory statistics * [refactor]Reconstructing forgotten, emotional, situational, and explicit memory statistics * [changes]Improve the code based on AI review * [changes]Statistics on work, perception, short-term, and implicit memory * [changes]Statistics on work, perception, short-term, and implicit memory * [changes]Replace the invisible memory calculation method * [changes]Statistics on work, perception, short-term, and implicit memory * [refactor]Reconstructing forgotten, emotional, situational, and explicit memory statistics * [changes]Statistics on work, perception, short-term, and implicit memory * [changes]Replace the invisible memory calculation method
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乐力齐
@@ -13,12 +13,14 @@ from typing import Any, Dict, List, Optional, Tuple
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from app.core.logging_config import get_logger
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from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
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from app.db import get_db_context
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from app.repositories.conversation_repository import ConversationRepository
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from app.repositories.end_user_repository import EndUserRepository
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.schemas.memory_episodic_schema import type_mapping, EmotionType, EmotionSubject
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from app.services.implicit_memory_service import ImplicitMemoryService
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from app.services.memory_base_service import MemoryBaseService
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from app.services.memory_config_service import MemoryConfigService
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from app.services.memory_perceptual_service import MemoryPerceptualService
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from app.services.memory_short_service import ShortService
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from pydantic import BaseModel, Field
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from sqlalchemy.orm import Session
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@@ -1198,18 +1200,17 @@ async def analytics_memory_types(
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end_user_id: Optional[str] = None
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) -> List[Dict[str, Any]]:
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"""
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统计9种记忆类型的数量和百分比
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统计8种记忆类型的数量和百分比
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计算规则：
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1. 感知记忆 (PERCEPTUAL_MEMORY) = statement + entity
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2. 工作记忆 (WORKING_MEMORY) = chunk + entity
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3. 短期记忆 (SHORT_TERM_MEMORY) = chunk
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4. 长期记忆 (LONG_TERM_MEMORY) = entity
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5. 显性记忆 (EXPLICIT_MEMORY) = 情景记忆 + 语义记忆（通过 MemoryBaseService.get_explicit_memory_count 获取）
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6. 隐性记忆 (IMPLICIT_MEMORY) = 1/3 * entity
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7. 情绪记忆 (EMOTIONAL_MEMORY) = 情绪标签统计总数（通过 MemoryBaseService.get_emotional_memory_count 获取）
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8. 情景记忆 (EPISODIC_MEMORY) = memory_summary（通过 MemoryBaseService.get_episodic_memory_count 获取）
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9. 遗忘记忆 (FORGET_MEMORY) = 激活值低于阈值的节点数（通过 MemoryBaseService.get_forget_memory_count 获取）
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1. 感知记忆 (PERCEPTUAL_MEMORY) = 通过 MemoryPerceptualService.get_memory_count 获取的 total_count
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2. 工作记忆 (WORKING_MEMORY) = 会话数量（通过 ConversationRepository.get_conversation_by_user_id 获取）
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3. 短期记忆 (SHORT_TERM_MEMORY) = /short_term 接口返回的问答对数量
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4. 显性记忆 (EXPLICIT_MEMORY) = 情景记忆 + 语义记忆（通过 MemoryBaseService.get_explicit_memory_count 获取）
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5. 隐性记忆 (IMPLICIT_MEMORY) = Statement 节点数量的三分之一
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6. 情绪记忆 (EMOTIONAL_MEMORY) = 情绪标签统计总数（通过 MemoryBaseService.get_emotional_memory_count 获取）
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7. 情景记忆 (EPISODIC_MEMORY) = memory_summary（通过 MemoryBaseService.get_episodic_memory_count 获取）
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8. 遗忘记忆 (FORGET_MEMORY) = 激活值低于阈值的节点数（通过 MemoryBaseService.get_forget_memory_count 获取）
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Args:
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db: 数据库会话
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@@ -1229,7 +1230,6 @@ async def analytics_memory_types(
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- PERCEPTUAL_MEMORY: 感知记忆
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- WORKING_MEMORY: 工作记忆
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- SHORT_TERM_MEMORY: 短期记忆
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- LONG_TERM_MEMORY: 长期记忆
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- EXPLICIT_MEMORY: 显性记忆
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- IMPLICIT_MEMORY: 隐性记忆
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- EMOTIONAL_MEMORY: 情绪记忆
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@@ -1239,40 +1239,78 @@ async def analytics_memory_types(
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# 初始化基础服务
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base_service = MemoryBaseService()
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# 定义需要查询的基础节点类型
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node_types = {
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"Statement": "Statement",
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"Entity": "ExtractedEntity",
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"Chunk": "Chunk"
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}
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# 初始化感知记忆服务
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perceptual_service = MemoryPerceptualService(db)
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# 存储每种节点类型的计数
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node_counts = {}
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# 获取感知记忆数量
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if end_user_id:
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perceptual_stats = perceptual_service.get_memory_count(uuid.UUID(end_user_id))
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perceptual_count = perceptual_stats.get("total", 0)
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else:
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perceptual_count = 0
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# 查询每种节点类型的数量
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for key, node_type in node_types.items():
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if end_user_id:
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query = f"""
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MATCH (n:{node_type})
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# 获取工作记忆数量（基于会话数量）
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work_count = 0
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if end_user_id:
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try:
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conversation_repo = ConversationRepository(db)
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conversations = conversation_repo.get_conversation_by_user_id(
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user_id=uuid.UUID(end_user_id),
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limit=100, # 获取更多会话以准确统计
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is_activate=True
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)
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work_count = len(conversations)
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logger.debug(f"工作记忆数量（会话数）: {work_count} (end_user_id={end_user_id})")
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except Exception as e:
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logger.warning(f"获取会话数量失败，工作记忆数量设为0: {str(e)}")
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work_count = 0
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# 获取隐性记忆数量（基于 Statement 节点数量的三分之一）
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implicit_count = 0
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if end_user_id:
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try:
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# 查询 Statement 节点数量
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query = """
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MATCH (n:Statement)
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WHERE n.group_id = $group_id
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RETURN count(n) as count
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"""
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result = await _neo4j_connector.execute_query(query, group_id=end_user_id)
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else:
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query = f"""
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MATCH (n:{node_type})
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RETURN count(n) as count
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"""
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result = await _neo4j_connector.execute_query(query)
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# 提取计数结果
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count = result[0]["count"] if result and len(result) > 0 else 0
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node_counts[key] = count
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statement_count = result[0]["count"] if result and len(result) > 0 else 0
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# 取三分之一作为隐性记忆数量
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implicit_count = round(statement_count / 3)
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logger.debug(f"隐性记忆数量（Statement数量的1/3）: {implicit_count} (Statement总数={statement_count}, end_user_id={end_user_id})")
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except Exception as e:
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logger.warning(f"获取Statement数量失败，隐性记忆数量设为0: {str(e)}")
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implicit_count = 0
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# 获取各节点类型的数量
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statement_count = node_counts.get("Statement", 0)
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entity_count = node_counts.get("Entity", 0)
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chunk_count = node_counts.get("Chunk", 0)
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# 原有的基于行为习惯的统计方式（已注释）
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# implicit_count = 0
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# if end_user_id:
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# try:
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# implicit_service = ImplicitMemoryService(db, end_user_id)
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# behavior_habits = await implicit_service.get_behavior_habits(
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# user_id=end_user_id
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# )
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# implicit_count = len(behavior_habits)
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# logger.debug(f"隐性记忆数量（行为习惯数）: {implicit_count} (end_user_id={end_user_id})")
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# except Exception as e:
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# logger.warning(f"获取行为习惯数量失败，隐性记忆数量设为0: {str(e)}")
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# implicit_count = 0
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# 获取短期记忆数量（基于 /short_term 接口返回的问答对数量）
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short_term_count = 0
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if end_user_id:
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try:
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short_term_service = ShortService(end_user_id)
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short_term_data = short_term_service.get_short_databasets()
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# 统计 short_term 数组的长度
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if short_term_data:
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short_term_count = len(short_term_data)
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logger.debug(f"短期记忆数量（问答对数）: {short_term_count} (end_user_id={end_user_id})")
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except Exception as e:
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logger.warning(f"获取短期记忆数量失败，短期记忆数量设为0: {str(e)}")
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short_term_count = 0
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# 获取用户的遗忘阈值配置
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forgetting_threshold = 0.3 # 默认值
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@@ -1298,17 +1336,16 @@ async def analytics_memory_types(
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# 使用 MemoryBaseService 的共享方法获取特殊记忆类型的数量
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episodic_count = await base_service.get_episodic_memory_count(end_user_id)
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explicit_count = await base_service.get_explicit_memory_count(end_user_id)
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emotion_count = await base_service.get_emotional_memory_count(end_user_id, statement_count)
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emotion_count = await base_service.get_emotional_memory_count(end_user_id, perceptual_count)
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forget_count = await base_service.get_forget_memory_count(end_user_id, forgetting_threshold)
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# 按规则计算9种记忆类型的数量（使用英文枚举作为key）
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# 按规则计算8种记忆类型的数量（使用英文枚举作为key）
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memory_counts = {
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"PERCEPTUAL_MEMORY": statement_count + entity_count, # 感知记忆
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"WORKING_MEMORY": chunk_count + entity_count, # 工作记忆
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"SHORT_TERM_MEMORY": chunk_count, # 短期记忆
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"LONG_TERM_MEMORY": entity_count, # 长期记忆
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"PERCEPTUAL_MEMORY": perceptual_count, # 感知记忆
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"WORKING_MEMORY": work_count, # 工作记忆（基于会话数量）
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"SHORT_TERM_MEMORY": short_term_count, # 短期记忆（基于问答对数量）
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"EXPLICIT_MEMORY": explicit_count, # 显性记忆（情景记忆 + 语义记忆）
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"IMPLICIT_MEMORY": entity_count // 3, # 隐性记忆 (1/3 entity)
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"IMPLICIT_MEMORY": implicit_count, # 隐性记忆（Statement数量的1/3）
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"EMOTIONAL_MEMORY": emotion_count, # 情绪记忆（使用情绪标签统计）
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"EPISODIC_MEMORY": episodic_count, # 情景记忆
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"FORGET_MEMORY": forget_count # 遗忘记忆（激活值低于阈值）
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