本文通过逆向工程实验,揭示了ChatGPT记忆系统的四层架构:会话元数据实时适应环境(如设备、位置),用户记忆长期存储用户事实(如姓名、职业偏好),最近对话摘要轻量级提供跨对话连续性,当前会话消息滑动窗口保持对话连贯性。研究发现,ChatGPT未使用向量数据库或RAG技术,而是通过预计算摘要和直接注入实现高效记忆。该方法牺牲详细历史上下文,但换取速度和效率,使系统感觉个性化且响应迅速。研究为AI记忆系统设计提供了实用启示,展示了简单方法在控制整个流程时的优势。
原文链接:Linux.do
本文通过逆向工程实验,揭示了ChatGPT记忆系统的四层架构:会话元数据实时适应环境(如设备、位置),用户记忆长期存储用户事实(如姓名、职业偏好),最近对话摘要轻量级提供跨对话连续性,当前会话消息滑动窗口保持对话连贯性。研究发现,ChatGPT未使用向量数据库或RAG技术,而是通过预计算摘要和直接注入实现高效记忆。该方法牺牲详细历史上下文,但换取速度和效率,使系统感觉个性化且响应迅速。研究为AI记忆系统设计提供了实用启示,展示了简单方法在控制整个流程时的优势。
原文链接:Linux.do
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