[{"data":1,"prerenderedAt":840},["ShallowReactive",2],{"\u002Fcn\u002Fopen_source\u002Fmodules\u002Fmemories\u002Foverview":3,"surround-\u002Fcn\u002Fopen_source\u002Fmodules\u002Fmemories\u002Foverview":824},{"id":4,"title":5,"avatar":6,"banner":6,"body":7,"category":6,"desc":817,"description":17,"extension":818,"links":6,"meta":819,"navigation":6,"path":820,"seo":821,"stem":822,"__hash__":823},"docs\u002Fcn\u002Fopen_source\u002Fmodules\u002Fmemories\u002Foverview.md","记忆模块总览",null,{"type":8,"value":9,"toc":798},"minimark",[10,14,18,23,90,93,97,102,105,110,161,165,208,212,258,262,320,322,326,329,333,382,386,431,433,437,440,444,476,480,502,506,528,530,534,538,546,651,655,661,672,676,682,693,697,703,714,718,724,735,737,741,745,748,762,769,773,776,787,792,794],[11,12,13],"h1",{"id":13},"为什么需要不同的记忆模块",[15,16,17],"p",{},"记忆模块是赋予Agent“长期记忆”能力的核心组件。它不只是像数据库一样死板地存取数据，而是能够像人类一样，对信息进行自动化地提取、分类、关联和动态更新。通过选择不同的记忆模块，你可以让 Agent拥有不同能力。",[19,20,22],"h2",{"id":21},"快速选择指南","🎯 快速选择指南",[24,25,27,34],"alert",{"type":26},"info",[15,28,29,33],{},[30,31,32],"strong",{},"不确定选哪个？"," 跟随这个决策树：",[35,36,37,50,60,70,80],"ul",{},[38,39,40,41,44,45],"li",{},"🚀 ",[30,42,43],{},"快速测试\u002F演示：简单上手，无需额外软件"," → ",[46,47,49],"a",{"href":48},"#naivetextmemory-%E7%AE%80%E5%8D%95%E6%98%8E%E6%96%87%E8%AE%B0%E5%BF%86","NaiveTextMemory",[38,51,52,53,44,56],{},"📝 ",[30,54,55],{},"通用文本记忆：记住聊天内容或大量文档，并能根据语义搜索",[46,57,59],{"href":58},"#generaltextmemory-%E9%80%9A%E7%94%A8%E6%96%87%E6%9C%AC%E8%AE%B0%E5%BF%86","GeneralTextMemory",[38,61,62,63,44,66],{},"👤 ",[30,64,65],{},"用户偏好管理：专门针对用户画像设计",[46,67,69],{"href":68},"#preferencetextmemory-%E5%81%8F%E5%A5%BD%E8%AE%B0%E5%BF%86","PreferenceTextMemory",[38,71,72,73,44,76],{},"🌳 ",[30,74,75],{},"结构化知识图谱：数据之间有复杂的逻辑关联",[46,77,79],{"href":78},"#treetextmemory-%E5%88%86%E5%B1%82%E7%BB%93%E6%9E%84%E8%AE%B0%E5%BF%86","TreeTextMemory",[38,81,82,83,44,86],{},"⚡ ",[30,84,85],{},"推理加速：访问量很大，希望回复能更平稳、响应更快",[46,87,89],{"href":88},"#kvcachememory-%E6%BF%80%E6%B4%BB%E8%AE%B0%E5%BF%86","KVCacheMemory",[91,92],"hr",{},[19,94,96],{"id":95},"记忆模块分类","📚 记忆模块分类",[98,99,101],"h3",{"id":100},"一文本记忆系列","一、文本记忆系列",[15,103,104],{},"专注于存储和检索文本形式的记忆，适用于绝大多数应用场景。",[106,107,109],"h4",{"id":108},"naivetextmemory-简单明文记忆","NaiveTextMemory: 简单明文记忆",[111,112,113,119,124,138,143,154],"card",{},[15,114,115,118],{},[30,116,117],{},"适用场景："," 快速原型、演示、教学、小规模应用",[15,120,121],{},[30,122,123],{},"核心特性：",[35,125,126,129,132,135],{},[38,127,128],{},"✅ 零依赖，纯内存存储",[38,130,131],{},"✅ 关键词匹配检索",[38,133,134],{},"✅ 极简 API，5 分钟上手",[38,136,137],{},"✅ 支持文件持久化",[15,139,140],{},[30,141,142],{},"局限性：",[35,144,145,148,151],{},[38,146,147],{},"❌ 无向量语义搜索",[38,149,150],{},"❌ 不适合大规模数据",[38,152,153],{},"❌ 检索精度有限",[15,155,156,157],{},"📖 ",[46,158,160],{"href":159},".\u002Fnaive_textual_memory","查看文档",[106,162,164],{"id":163},"generaltextmemory-通用文本记忆","GeneralTextMemory: 通用文本记忆",[111,166,167,172,176,190,195,203],{},[15,168,169,171],{},[30,170,117],{}," 会话代理、个人助理、知识管理系统",[15,173,174],{},[30,175,123],{},[35,177,178,181,184,187],{},[38,179,180],{},"✅ 基于向量的语义搜索",[38,182,183],{},"✅ 丰富的元数据支持（类型、时间、来源等）",[38,185,186],{},"✅ 灵活的过滤和查询",[38,188,189],{},"✅ 适合中大规模应用",[15,191,192],{},[30,193,194],{},"技术要求：",[35,196,197,200],{},[38,198,199],{},"需要向量数据库（Qdrant 等）",[38,201,202],{},"需要 Embedding 模型",[15,204,156,205],{},[46,206,160],{"href":207},".\u002Fgeneral_textual_memory",[106,209,211],{"id":210},"preferencetextmemory-偏好记忆","PreferenceTextMemory: 偏好记忆",[111,213,214,219,223,237,242,253],{},[15,215,216,218],{},[30,217,117],{}," 个性化推荐、用户画像、智能助理",[15,220,221],{},[30,222,123],{},[35,224,225,228,231,234],{},[38,226,227],{},"✅ 自动识别显式和隐式偏好",[38,229,230],{},"✅ 偏好去重与冲突检测",[38,232,233],{},"✅ 按偏好类型、强度筛选",[38,235,236],{},"✅ 向量语义检索",[15,238,239],{},[30,240,241],{},"专用功能：",[35,243,244,247,250],{},[38,245,246],{},"双重偏好提取（explicit\u002Fimplicit）",[38,248,249],{},"偏好强度评分",[38,251,252],{},"时间衰减支持",[15,254,156,255],{},[46,256,160],{"href":257},".\u002Fpreference_textual_memory",[106,259,261],{"id":260},"treetextmemory-分层结构记忆","TreeTextMemory: 分层结构记忆",[111,263,264,269,273,287,292,303,307,315],{},[15,265,266,268],{},[30,267,117],{}," 知识图谱、复杂关系推理、多跳查询",[15,270,271],{},[30,272,123],{},[35,274,275,278,281,284],{},[38,276,277],{},"✅ 基于图数据库的结构化存储",[38,279,280],{},"✅ 支持层次关系和因果链",[38,282,283],{},"✅ 多跳推理能力",[38,285,286],{},"✅ 去重、冲突检测、记忆调度",[15,288,289],{},[30,290,291],{},"高级功能：",[35,293,294,297,300],{},[38,295,296],{},"支持 MultiModal Reader（图片、URL、文件）",[38,298,299],{},"支持互联网检索（BochaAI、Google、Bing）",[38,301,302],{},"工作记忆替换机制",[15,304,305],{},[30,306,194],{},[35,308,309,312],{},[38,310,311],{},"需要图数据库（Neo4j 等）",[38,313,314],{},"需要向量数据库和 Embedding 模型",[15,316,156,317],{},[46,318,160],{"href":319},".\u002Ftree_textual_memory",[91,321],{},[98,323,325],{"id":324},"二专用记忆模块","二、专用记忆模块",[15,327,328],{},"针对特定场景优化的记忆系统。",[106,330,332],{"id":331},"kvcachememory-激活记忆","KVCacheMemory: 激活记忆",[111,334,335,340,344,355,360,371,377],{},[15,336,337,339],{},[30,338,117],{}," LLM 推理加速、高频背景知识复用",[15,341,342],{},[30,343,123],{},[35,345,346,349,352],{},[38,347,348],{},"⚡ 预计算 KV Cache，跳过重复编码",[38,350,351],{},"⚡ 大幅减少预填充阶段计算",[38,353,354],{},"⚡ 适合高吞吐量场景",[15,356,357],{},[30,358,359],{},"典型用例：",[35,361,362,365,368],{},[38,363,364],{},"常见问题（FAQ）缓存",[38,366,367],{},"对话历史复用",[38,369,370],{},"领域知识预加载",[15,372,373,376],{},[30,374,375],{},"工作原理：","\n稳定的文本记忆 → 预转换为 KV Cache → 推理时直接注入",[15,378,156,379],{},[46,380,160],{"href":381},".\u002Fkv_cache_memory",[106,383,385],{"id":384},"parametricmemory-参数化记忆","ParametricMemory: 参数化记忆",[111,387,388,394,399,410,415,426],{},[15,389,390,393],{},[30,391,392],{},"状态："," 🚧 正在开发中",[15,395,396],{},[30,397,398],{},"设计目标：",[35,400,401,404,407],{},[38,402,403],{},"将知识编码到模型权重（LoRA、专家模块）",[38,405,406],{},"动态加载\u002F卸载能力模块",[38,408,409],{},"支持多任务、多角色架构",[15,411,412],{},[30,413,414],{},"未来功能：",[35,416,417,420,423],{},[38,418,419],{},"参数模块生成与压缩",[38,421,422],{},"版本控制与回滚",[38,424,425],{},"热插拔能力模块",[15,427,156,428],{},[46,429,160],{"href":430},".\u002Fparametric_memory",[91,432],{},[98,434,436],{"id":435},"三图数据库后端","三、图数据库后端",[15,438,439],{},"为 TreeTextMemory 提供图存储能力。",[106,441,443],{"id":442},"neo4j-graph-db","Neo4j Graph DB",[111,445,446,452,457,471],{},[15,447,448,451],{},[30,449,450],{},"推荐度："," ⭐⭐⭐⭐⭐",[15,453,454],{},[30,455,456],{},"特性：",[35,458,459,462,465,468],{},[38,460,461],{},"完整的图数据库功能",[38,463,464],{},"支持向量增强检索",[38,466,467],{},"多租户架构（v0.2.1+）",[38,469,470],{},"兼容社区版",[15,472,156,473],{},[46,474,160],{"href":475},".\u002Fneo4j_graph_db",[106,477,479],{"id":478},"nebula-graph-db","Nebula Graph 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NaiveTextMemory",[557,596,597],{"class":571},"()\n",[557,599,601,604,606,609,612,615,619,621],{"class":559,"line":600},3,[557,602,603],{"class":567},"memory",[557,605,572],{"class":571},[557,607,608],{"class":593},"add",[557,610,611],{"class":571},"(",[557,613,614],{"class":571},"\"",[557,616,618],{"class":617},"sfazB","用户喜欢喝咖啡",[557,620,614],{"class":571},[557,622,623],{"class":571},")\n",[557,625,627,630,632,635,637,640,642,644,647,649],{"class":559,"line":626},4,[557,628,629],{"class":567},"results ",[557,631,590],{"class":571},[557,633,634],{"class":567}," memory",[557,636,572],{"class":571},[557,638,639],{"class":593},"search",[557,641,611],{"class":571},[557,643,614],{"class":571},[557,645,646],{"class":617},"咖啡",[557,648,614],{"class":571},[557,650,623],{"class":571},[98,652,654],{"id":653},"场景-2-聊天机器人记忆","场景 2: 聊天机器人记忆",[15,656,657,543,659],{},[30,658,542],{},[46,660,59],{"href":207},[35,662,663,666,669],{},[38,664,665],{},"支持语义搜索",[38,667,668],{},"按时间、类型、来源过滤",[38,670,671],{},"适合对话历史管理",[98,673,675],{"id":674},"场景-3-个性化推荐系统","场景 3: 个性化推荐系统",[15,677,678,543,680],{},[30,679,542],{},[46,681,69],{"href":257},[35,683,684,687,690],{},[38,685,686],{},"自动提取用户偏好",[38,688,689],{},"偏好冲突检测",[38,691,692],{},"强度评分与筛选",[98,694,696],{"id":695},"场景-4-知识图谱应用","场景 4: 知识图谱应用",[15,698,699,543,701],{},[30,700,542],{},[46,702,79],{"href":319},[35,704,705,708,711],{},[38,706,707],{},"多跳关系查询",[38,709,710],{},"层次结构管理",[38,712,713],{},"复杂推理场景",[98,715,717],{"id":716},"场景-5-高性能-llm-服务","场景 5: 高性能 LLM 服务",[15,719,720,543,722],{},[30,721,542],{},[46,723,89],{"href":381},[35,725,726,729,732],{},[38,727,728],{},"FAQ 系统",[38,730,731],{},"客服机器人",[38,733,734],{},"大批量请求处理",[91,736],{},[19,738,740],{"id":739},"高级功能","🔗 高级功能",[98,742,744],{"id":743},"multimodal-reader多模态读取","MultiModal Reader（多模态读取）",[15,746,747],{},"在 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