智能会话机器人：SaaS 平台的设计与思考

{"type":"doc","content":[{"type":"heading","attrs":{"align":null,"level":1},"content":[{"type":"text","text":"1. 前言","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"随着自然语言处理和智能语音识别技术的发展，智能会话机器人开始部分替代人工客服。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"网上关于NLP算法的文章有很多，但关于 Chatbot 架构的却很少，关于 Chatbot SaaS 平台架构的则更少。我是一名对机器学习感兴趣的程序员，更关注如何设计实现一个架构良好的 Chatbot SaaS 平台，因此写下了这篇文章。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":1},"content":[{"type":"text","text":"2. 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智能会话机器人的分类","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/ab/ab666bf6a151d09bbbef9d4ee5aea8b8.png","alt":null,"title":"智能会话机器人类型","style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":"","fromPaste":false,"pastePass":false}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"智能会话机器人按照对话轮次来划分，可以分为单轮对话机器人和多轮对话机器人；按照知识领域来划分，可以分为限定域机器人和开放域机器人；按照任务类型来划分，可以分为任务型机器人、问答型机器人、闲聊型机器人和融合型机器人。","attrs":{}}]},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"任务型","attrs":{}},{"type":"text","text":"：根据用户给出的信息完成指定的任务。一般限定于某个垂直领域，常采用多轮对话的形式。如订餐、订票等服务。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"问答型","attrs":{}},{"type":"text","text":"：为用户提出的事实型、布尔型、计算型、推理型等类型的问题自动给出答案，进一步还可以再划分为常见问题解答（FAQ）和知识图谱问答（KBQA），一般限定于某个特定知识领域。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"闲聊型","attrs":{}},{"type":"text","text":"：与用户进行开放式聊天，满足用户的情感陪护需求。一般不限定话题范围，但有可能偏向于某个领域。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"融合型","attrs":{}},{"type":"text","text":"：一般以任务型或问答型为主，融合闲聊功能。如对于电商客服机器人来说，能完成商品推荐购买任务，能回答保修政策问题，还能陪客户闲聊天，融合型是应用场景越来越复杂的产物。","attrs":{}}]}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"从应用的发展趋势来看：单轮对话——>多轮对话，以获得更多更完整的信息；单领域——>多领域，以满足用户更多层面的需求。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"2.2. 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自然语言生成（NLG）","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"自然语言生成主要有两种方法：基于模板生成和基于模型生成。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"基于模板生成：调用设计人员预先设计的回复模板，根据前置阶段获取到的状态和关键信息生成自然语言回复给用户。优点是响应速度较快，缺点是语言表达不够丰富，需要人工定义模板规则。因此比较适合在响应动作较少时使用，或是在系统冷启动时使用。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"基于模型生成：通常使用seq2seq模型学习大量交互语料数据，根据用户输入直接生成相关自然语言回复给用户。优点是不需要人工定义模板规则，语言表达较为丰富；缺点是回复结果不太可控，响应速度较慢，需要学习大量的交互语料数据。因此比较适合系统资源充足且有大量交互数据时采用。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"2.3. 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Policy，由其直接将消息分发到对应的系统。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":2,"align":null,"origin":null},"content":[{"type":"text","text":"这里有一个明显的需求冲突：当设定为仅机器人响应时，如果最终用户要求人工客服响应，该如何来处理？","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":3,"align":null,"origin":null},"content":[{"type":"text","text":"一个方案是 ActionServing 获取相关的分发策略设定，如果判断为仅机器人响应，则不通知人工坐席系统且给出预设的回复；另一个方案是ActionServing 无需知晓全局分发策略设定，始终通知人工坐席系统且给出预设的回复，是否切换完全由人工客服决定。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":4,"align":null,"origin":null},"content":[{"type":"text","text":"这个需求冲突需由产品设计层面决定，无论哪种方式，都只是与租户相关的ActionServing 的改动，不影响整体架构。","attrs":{}}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":" 2.个性化设定","attrs":{}}]},{"type":"numberedlist","attrs":{"start":1,"normalizeStart":1},"content":[{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":1,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"italic","attrs":{}},{"type":"strong","attrs":{}}],"text":"意图检测","attrs":{}},{"type":"text","text":"、","attrs":{}},{"type":"text","marks":[{"type":"italic","attrs":{}},{"type":"strong","attrs":{}}],"text":"情感分析","attrs":{}},{"type":"text","text":"……等需根据对话内容来判断是否需要切换到人工坐席的情形，这个可以归类到个性化设定，其本质也是Chatbot的对话流编排。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":2,"align":null,"origin":null},"content":[{"type":"text","text":"因为需要利用Chatbot 的自然语言理解能力，因此需先将消息发送给 Chatbot，由 Chatbot 的对话管理模块根据对话流状态通知人工客服进行后续处理。人工客服一旦开始应答，Router则开始将消息转发到人工坐席系统（自动切换）。","attrs":{}}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"基本业务流程如下图所示，Router 负责全局设定，Chatbot负责个性化的设定。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/59/59bf4beac5ae13c20e5b5688571bac15.png","alt":null,"title":"人工客服支持-业务流程","style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":"","fromPaste":false,"pastePass":false}},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"备注","attrs":{}},{"type":"text","text":"：","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如果有更加复杂的个性化需求，可以在 Router 之后增加一个租户特有的独立的分发组件，无需修改 Router 和 Chatbot。","attrs":{}}]}],"attrs":{}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"3.4. 技术选型","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在前面的架构设计中，我一直避免限定具体的技术框架和开发语言。这是因为选择何种技术其实是非常个性化的，不仅仅与具体业务有关，也跟开发团队熟悉的技术有关。接下来，也仅仅聊一些大的方向选择，或者说是我个人的选择倾向。","attrs":{}}]},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"异步、长链接和双向通信","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Chatbot 和人工客服的消息天然就是异步响应的，一次对话过程会有多轮对话（多次通信），也可能会连续发送多条消息而无需等待对方回复。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"因此，外部接入的会话网关应该能支持长链接、双向通信和异步响应。WebSocket、RSocket、MQTT等都可以支持，综合来看，WebSocket 最为通用成熟，因此首先支持 WebSocket 协议，其它协议再根据业务需求做进一步开发。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"另外，如 ","attrs":{}},{"type":"text","marks":[{"type":"italic","attrs":{}},{"type":"strong","attrs":{}}],"text":"3.2.3. 组件交互","attrs":{}},{"type":"text","text":" 所描述的，内部服务组件之间的通信也应该是异步的，我更倾向于采用 MQ 来作为 Router 和 Chatbot & 人工坐席系统 之间的异步通信机制。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"但这也会带来一些其它的复杂度：譬如服务端响应消息时如何才能知道客户端连接到了哪个会话网关的哪个节点？如何来判断是否响应超时？消息是否允许丢失？如何保证消息不丢失？如何保证消息不重复响应？","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"业界对于这些问题已有一些不错的实践，但具体而言还是要根据业务场景去选择落地方案，这里不再继续深入探讨。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"语言与框架","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"对于较大型的项目，通常都会使用多个语言。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"Java","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Java的企业应用生态最丰富完整，但有一个问题是编译后的镜像体积大启动慢。当前，Spring、Quarkus 等正与 GraalVM 密切合作以构建资源占用低启动速度快的云原生应用，但依然有很多项目不能编译成native-image。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"因此，如果选用Java 作为主语言，SaaS平台的管理部分可以用Spring，而对于需要频繁创建、快速扩容且相对简单的Chatbot 和 ActionServing，可以使用Spring Native 编译成 native-image。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"Go","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Go 编译后的容器镜像较小，并发支持也不错，虽然相比 Java 而言应用生态并没有那么强大，泛型也是硬伤，但在DevOps 领域却是绝对的主流。SaaS平台的管理调度部分其实也是偏运维，所以其实可以用 Go 来开发。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"Python","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"AI 领域的开发大多使用Python，当前主流的深度学习框架有TensorFlow 和 PyTorch，很多开源的模型也是使用这两个框架开发。相比较而言，TensorFlow 的模型部署更加强大方便。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"综上：SaaS平台的会话网关、管理端、领域服务层用 Java（Spring），Chatbot 和 ActionServing 用 Java（Spring Native）；SaaS平台的调度端可以用Go，不过更倾向于也用 Java；机器学习的模型开发主要用 Python（具体框架取决于开源模型采用哪种框架）；模型部署则与具体的深度学习框架相关，部署方式的选择需要考虑并发性能和时延，但总之最后一定要打包成 Docker 镜像，便于自动化运维。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"微服务与容器化","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":"none"},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"SaaS 平台要实现自动化运维，K8s + Istio + Docker 是一套不错的组合。但K8s 和 Istio 比较复杂，这需要技术团队对其有足够深刻的了解，还需要有一定的定制化开发能力。当然不一定非要用 K8s + Istio，也可以用 Spring Cloud + Go + Docker 的方式来开发，优点是方案会更加简单可控，缺点是需要自己实现一整套运维管理监控机制，到最后可能其实就是 K8s + Istio 功能的一个子集。","attrs":{}}]}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"关于技术选型，最重要的其实是在保证功能需求和特性需求的基础上能够做到简单和统一，有利于后续的开发和维护。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"3.5. 小结","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"本节首先对SaaS平台的需求进行了分析，然后介绍了SaaS 平台的架构设定，最后简单探讨了技术选型。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":1},"content":[{"type":"text","text":"4. 结束语","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"这篇文章描述了 SaaS 平台架构的基本设计，融入了一些我的思考过程，更详细的如模块设计、核心业务流程设计……等限于篇幅不再一一详述，后续有时间再聊。未来的规划中，我会基于当前的设计，先实现 Chatbot，然后再实现 SaaS 平台，欢迎继续关注。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"最后，感谢各位朋友的阅读，如有任何建议，请给我留言。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":1},"content":[{"type":"text","text":"5. 参考资料","attrs":{}}]},{"type":"numberedlist","attrs":{"start":1,"normalizeStart":1},"content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":1,"align":null,"origin":null},"content":[{"type":"text","text":"杜振东, 涂铭. 会话式AI：自然语言处理于人机交互[M]. 北京:机械工业出版社, 2020","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":2,"align":null,"origin":null},"content":[{"type":"text","text":"段楠, 周明. 智能问答[M]. 北京:高等教育出版社, 2018","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":3,"align":null,"origin":null},"content":[{"type":"text","text":"夕小瑶. 多轮对话之对话管理(Dialog Management)[EB/OL]. [2018-12-24]. https://www.jiqizhixin.com/articles/2018-12-24-23","attrs":{}}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":4,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/16/163c7f16649700506a2ff355a1f15673.png","alt":null,"title":"文章版本记录","style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":"","fromPaste":false,"pastePass":false}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}}]}