Rasa Core和Rasa nlu之间的区别 [英] Difference between Rasa core and Rasa nlu

问题描述

我试图从 Rasa core 和

I tried to understand about rasa from official documentation of Rasa core and Rasa nlu but not able to deduce much. What I am able to understand is

Rasa核心用于指导对话流程，而Rasa nlu则用于理解和处理文本以提取信息(实体)

第二件事，在 Rasa core 和 Rasa nlu 都可以用来构建聊天机器人，但无法理解两种方法以及何时遵循的区别哪个.

Second thing, there are examples to build chatbot in Rasa core as well as Rasa nlu both can be used to build chatbot but couldn't understand what's the difference in two approaches and when to follow which one.

能否请您帮我更好地理解.

Could you please help me to understand in a better way.

推荐答案

您说对了.两者一起工作，但有各自不同的目标.简单来说，Rasa Core处理对话流程，话语，动作和Rasa NLU提取实体和意图.

You got it right. Both work together but they have distinct goals. In simple terms, Rasa Core handles the conversation flow, utterances, actions and Rasa NLU extract entities and intents.

关于第二个问题:

第一个示例显示了创建bot的整个工作流程，它显示了如何设置域和故事.这些是Rasa Core而非Rasa NLU的功能.在该示例的第2项(称为定义解释器")上，作者明确表示他正在使用Rasa NLU作为解释器(但您甚至可以使用其他实体提取器框架).

The first example shows the entire workflow to create the bot, it shows how to setup the domain and the stories. Those are features from Rasa Core, not Rasa NLU. At item 2 on this example (called Define an interpreter) the author explicitly said he is making use of Rasa NLU as the interpreter (but you could be even using another entity extractor framework).

第二个示例(Rasa NLU之一)展示了如何仅训练实体和意图提取器.您没有有关域和故事的任何信息，也没有有关对话流程的任何信息，这是一个纯粹的NLU示例(即使他使用Rasa Core的默认运行方法来运行该机器人).

The second example (the Rasa NLU one) shows how to train the entity and intent extractor only. You don't have any information about domains and stories, no information about the conversational flow, it is a pure NLU example (even though he is using the default run method from Rasa Core to run the bot).

当我开始学习Rasa时，很难理解开发机器人的概念.但是随着您开始编码，它变得很清晰.无论您使用哪种平台，NLU都将处理实体和意图，而对话流程则将是另一回事.

When I started studying Rasa was a bit hard to understand the concepts to develop the bots. But as you start coding it got clear. No matter which platforms you use, NLU will be handling entity and intents while the conversational flow will be something else.

甚至可以使用一个库来处理机器人的核心，而使用另一个库来处理NLU.

It is even possible to use one library to handle the core of your bot and another one to handle NLU.

我想指出，与您可以用来构建bot核心的大多数工具不同，Rasa Core使用机器学习来更好地概括对话流程.您可以使用可能的对话路径的数据集并训练核心以对其进行概括，而不必为对话中的每个可能的节点编写代码.这是一个非常酷而强大的功能:)

I would like to note that different from the most tools you can use to build the core of your bot, Rasa Core use machine learning to better generalize the dialogue flow. Instead of write code for each possible node on your conversation, you can use a dataset of possible conversational paths and train the core to generalize it. This is a very cool and powerful feature :)

希望有帮助.

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