Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions. Existing memory approaches often rely on simplistic extraction methods that lead to incomplete memories or use rigid, single-purpose memory extraction prompts tailored to a single use case, such as chatbots. Consequently, they lack generalizability and perform poorly across diverse downstream tasks. To bridge this gap, we introduce the Memory Base, a novel data management paradigm for managing the persistent state of long-term interactions. It is characterized by three core principles: selective extraction of high-value memories from raw information streams; inherent statefulness and evolution, where memory content is progressively summarized, corrected, and temporally weighted to prioritize recent interactions; and a generalizable abstraction paradigm designed for robust transferability across diverse applications, including education, recommendation, and agent memory. Building on this foundation, we present VikingMem, an end-to-end Memory Base Management System implemented on the VikingDB vector engine. VikingMem materializes this paradigm through interconnected event and entity abstractions. It features event-centric memory extraction to selectively handle complex information streams, while entities are dynamically updated by events to achieve stateful evolution. Using temporal compression via a topic-wise timeline and time-weighted recall, the system progressively produces high-level summary memories, prioritizes recent items, and compresses and fades older ones. Extensive evaluations on long-term memory benchmarks demonstrate that VikingMem outperformes baselines by up to 30% in memory retrieval effectiveness while maintaining the low latency essential for interactive applications.

翻译：大语言模型彻底改变了交互式应用；然而，其有限的上下文窗口为维持有状态的长程交互带来了关键的数据管理挑战。现有内存方法通常依赖简单的提取策略导致记忆不完整，或采用僵化且仅针对单一用例（如聊天机器人）的单用途内存提取提示。因此，这些方法缺乏泛化能力，在多样化的下游任务中表现不佳。为填补这一空白，我们提出了内存库（Memory Base）——一种用于管理长程交互持久状态的新型数据管理范式。该范式具有三大核心原则：从原始信息流中高价值记忆的选择性提取；内在的有状态性与演化性，即通过逐步总结、修正和时序加权优先处理近期交互来更新记忆内容；以及面向多样化应用（包括教育、推荐和智能体记忆）实现鲁棒迁移的通用化抽象范式。基于此，我们提出了VikingMem——一个部署在VikingDB向量引擎上的端到端内存库管理系统。VikingMem通过互连的事件与实体抽象实现了该范式。其以事件为中心的内存提取机制可选择性处理复杂信息流，而实体则由事件动态更新以实现有状态演化。通过基于主题时间线的时序压缩与时间加权召回，系统逐步生成高层级摘要记忆，优先保留近期条目，并对旧条目进行压缩与衰减。在长程记忆基准上的大量评估表明，VikingMem在记忆检索有效性上相较于基线方法提升高达30%，同时维持了交互式应用所必需的低延迟。