Fix/memory bug fix (#162)
* 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 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段
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lixinyue11
@@ -18,7 +18,7 @@ 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 EmotionSubject, EmotionType, type_mapping
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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_base_service import MemoryBaseService, MemoryTransService, Translation_English
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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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@@ -360,7 +360,9 @@ class UserMemoryService:
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async def get_cached_memory_insight(
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self,
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db: Session,
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end_user_id: str
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end_user_id: str,
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model_id: str,
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language_type: str
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) -> Dict[str, Any]:
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"""
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从数据库获取缓存的记忆洞察（四个维度）
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@@ -419,11 +421,18 @@ class UserMemoryService:
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key_findings_array = []
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logger.info(f"成功获取 end_user_id {end_user_id} 的缓存记忆洞察（四维度）")
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memory_insight=end_user.memory_insight
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behavior_pattern=end_user.behavior_pattern
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growth_trajectory=end_user.growth_trajectory
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if language_type!='zh':
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memory_insight=await Translation_English(model_id,memory_insight)
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behavior_pattern=await Translation_English(model_id,behavior_pattern)
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growth_trajectory=await Translation_English(model_id,growth_trajectory)
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return {
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"memory_insight": end_user.memory_insight, # 总体概述存储在 memory_insight
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"behavior_pattern": end_user.behavior_pattern,
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"memory_insight":memory_insight, # 总体概述存储在 memory_insight
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"behavior_pattern":behavior_pattern,
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"key_findings": key_findings_array, # 返回数组
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"growth_trajectory": end_user.growth_trajectory,
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"growth_trajectory": growth_trajectory,
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"updated_at": self._datetime_to_timestamp(end_user.memory_insight_updated_at),
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"is_cached": True
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}
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@@ -457,7 +466,9 @@ class UserMemoryService:
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async def get_cached_user_summary(
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self,
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db: Session,
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end_user_id: str
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end_user_id: str,
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model_id:str,
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language_type:str="zh"
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) -> Dict[str, Any]:
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"""
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从数据库获取缓存的用户摘要（四个部分）
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@@ -481,7 +492,6 @@ class UserMemoryService:
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user_uuid = uuid.UUID(end_user_id)
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(user_uuid)
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if not end_user:
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logger.warning(f"未找到 end_user_id 为 {end_user_id} 的用户")
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return {
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@@ -495,20 +505,29 @@ class UserMemoryService:
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}
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# 检查是否有缓存数据（至少有一个字段不为空）
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user_summary=end_user.user_summary
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personality_traits=end_user.personality_traits
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core_values=end_user.core_values
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one_sentence_summary=end_user.one_sentence_summary
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if language_type!='zh':
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user_summary=await Translation_English(model_id, user_summary)
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personality_traits = await Translation_English(model_id, personality_traits)
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core_values = await Translation_English(model_id, core_values)
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one_sentence_summary = await Translation_English(model_id, one_sentence_summary)
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has_cache = any([
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end_user.user_summary,
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end_user.personality_traits,
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end_user.core_values,
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end_user.one_sentence_summary
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user_summary,
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personality_traits,
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core_values,
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one_sentence_summary
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])
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if has_cache:
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logger.info(f"成功获取 end_user_id {end_user_id} 的缓存用户摘要")
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return {
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"user_summary": end_user.user_summary,
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"personality": end_user.personality_traits,
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"core_values": end_user.core_values,
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"one_sentence": end_user.one_sentence_summary,
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"user_summary": user_summary,
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"personality": personality_traits,
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"core_values":core_values,
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"one_sentence": one_sentence_summary,
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"updated_at": self._datetime_to_timestamp(end_user.user_summary_updated_at),
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"is_cached": True
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}
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@@ -1367,7 +1386,6 @@ async def analytics_memory_types(
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return memory_types
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async def analytics_graph_data(
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db: Session,
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end_user_id: str,
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@@ -1557,7 +1575,7 @@ async def analytics_graph_data(
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f"成功获取图数据: end_user_id={end_user_id}, "
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f"nodes={len(nodes)}, edges={len(edges)}"
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)
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return {
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"nodes": nodes,
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"edges": edges,
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@@ -1606,11 +1624,7 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_
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# 获取该节点类型的白名单字段
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allowed_fields = field_whitelist.get(label, [])
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# 如果没有定义白名单，返回空字典（或者可以返回所有字段）
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# if not allowed_fields:
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# # 对于未定义的节点类型，只返回基本字段
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# allowed_fields = ["name", "created_at", "caption"]
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count_neo4j=f"""MATCH (n)-[r]-(m) WHERE elementId(n) ="{node_id}" RETURN count(r) AS rel_count;"""
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node_results = await (_neo4j_connector.execute_query(count_neo4j))
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# 提取白名单中的字段
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@@ -1618,13 +1632,12 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_
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for field in allowed_fields:
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if field in properties:
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value = properties[field]
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if str(field) == 'entity_type':
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if str(field) == 'entity_type':
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value=type_mapping.get(value,'')
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if str(field)=="emotion_type":
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value=EmotionType.EMOTION_MAPPING.get(value)
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if str(field)=="emotion_subject":
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if str(field)=="emotion_subject":
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value=EmotionSubject.SUBJECT_MAPPING.get(value)
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# 清理 Neo4j 特殊类型
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filtered_props[field] = _clean_neo4j_value(value)
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filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0]
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return filtered_props
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