refactor(memory): use MemorySummary node count for implicit memory metrics
- Replace Statement-based implicit memory count (count/3) with actual MemorySummary node count filtered by DERIVED_FROM_STATEMENT relationship - Add minimum threshold of 5 MemorySummary nodes before reporting data - Add _build_empty_profile() to return structured empty profile when insufficient data exists, skipping unnecessary LLM calls
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lanceyq
@@ -379,12 +379,59 @@ class ImplicitMemoryService:
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raise
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def _build_empty_profile(self) -> dict:
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"""构建 MemorySummary 不足时返回的固定空白画像数据"""
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now_ms = int(datetime.now().timestamp() * 1000)
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insufficient = "Insufficient data for analysis"
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def _empty_dimension(name: str) -> dict:
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return {
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"evidence": [insufficient],
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"reasoning": f"No clear evidence found for {name} dimension",
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"percentage": 0.0,
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"dimension_name": name,
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"confidence_level": 20,
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}
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def _empty_category(name: str) -> dict:
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return {
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"evidence": [insufficient],
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"percentage": 25.0,
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"category_name": name,
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"trending_direction": None,
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}
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return {
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"habits": [],
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"portrait": {
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"aesthetic": _empty_dimension("aesthetic"),
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"creativity": _empty_dimension("creativity"),
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"literature": _empty_dimension("literature"),
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"technology": _empty_dimension("technology"),
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"historical_trends": None,
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"analysis_timestamp": now_ms,
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"total_summaries_analyzed": 0,
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},
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"preferences": [],
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"interest_areas": {
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"art": _empty_category("art"),
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"tech": _empty_category("tech"),
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"music": _empty_category("music"),
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"lifestyle": _empty_category("lifestyle"),
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"analysis_timestamp": now_ms,
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"total_summaries_analyzed": 0,
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},
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}
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async def generate_complete_profile(
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self,
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user_id: str
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) -> dict:
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"""生成完整的用户画像（包含所有4个模块）
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需要该用户的 MemorySummary 节点数量 >= 5 才会真正调用 LLM 生成画像，
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否则返回固定的空白画像数据。
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Args:
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user_id: 用户ID
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@@ -394,6 +441,20 @@ class ImplicitMemoryService:
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logger.info(f"生成完整用户画像: user={user_id}")
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try:
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# 前置检查：查询该用户有效的 MemorySummary 节点数量（排除孤立节点）
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query = """
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MATCH (n:MemorySummary)-[:DERIVED_FROM_STATEMENT]->(:Statement)
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WHERE n.end_user_id = $end_user_id
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RETURN count(DISTINCT n) as count
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"""
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result = await self.neo4j_connector.execute_query(query, end_user_id=user_id)
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memory_summary_count = result[0]["count"] if result and len(result) > 0 else 0
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logger.info(f"用户 MemorySummary 节点数量: {memory_summary_count} (user={user_id})")
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if memory_summary_count < 5:
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logger.info(f"MemorySummary 数量不足 5（当前 {memory_summary_count}），返回空白画像: user={user_id}")
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return self._build_empty_profile()
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# 并行调用4个分析方法
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preferences, portrait, interest_areas, habits = await asyncio.gather(
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self.get_preference_tags(user_id=user_id),
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@@ -1500,7 +1500,7 @@ async def analytics_memory_types(
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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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5. 隐性记忆 (IMPLICIT_MEMORY) = MemorySummary 节点数量（需 >= 5 才显示，否则为 0）
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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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@@ -1557,23 +1557,23 @@ async def analytics_memory_types(
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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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# 获取隐性记忆数量（基于有关联关系的 MemorySummary 节点数量，需 >= 5 才计入）
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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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# 只统计有 DERIVED_FROM_STATEMENT 关系的 MemorySummary 节点，排除孤立节点
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query = """
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MATCH (n:Statement)
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MATCH (n:MemorySummary)-[:DERIVED_FROM_STATEMENT]->(:Statement)
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WHERE n.end_user_id = $end_user_id
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RETURN count(n) as count
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RETURN count(DISTINCT n) as count
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"""
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result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id)
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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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memory_summary_count = result[0]["count"] if result and len(result) > 0 else 0
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# 仅当 MemorySummary 节点数量 >= 5 时才显示数量，否则为 0
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implicit_count = memory_summary_count if memory_summary_count >= 5 else 0
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logger.debug(f"隐性记忆数量（有效MemorySummary节点数）: {implicit_count} (有效MemorySummary总数={memory_summary_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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logger.warning(f"获取MemorySummary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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# 原有的基于行为习惯的统计方式（已注释）
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@@ -1639,7 +1639,7 @@ async def analytics_memory_types(
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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": implicit_count, # 隐性记忆（Statement数量的1/3）
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"IMPLICIT_MEMORY": implicit_count, # 隐性记忆（MemorySummary节点数，需>=5）
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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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