Hello everyone. Welcome to the firstly episode of the Rasa Masterclass.In this chapter you will learn: What contextual aides are and how theydiffer from other types of aides What is Rasa and how different componentsof the framework work together to enable you to build build enormous conversational AIHow to get started with building AI assistants consuming Rasa Lets dive claim in.So what are the contextual auxiliaries and how they differ from other types of deputies? To answer this question, lets take a look at how auxiliaries evolved over time. At Rasa, when we talk about the evolutionof communicative AI, we use the concept of 5 levels of auxiliaries: Level 1: Notification assistants.They arewhat you very likely have interacted with on your phone or laptop already. They sendyou simple notifications, but they show up as textbook on a messaging app like Whatsapp.Level 2: FAQ deputies. The most common type of deputies at the moment. They allow usersto ask a simple questions and get a response, which is a slight progress from commonlyknown FAQ sheets with a rummage barroom. The only inconsistency is that it is sometimes enhancedwith one or two related to the follow-up questions. Until this day, service industries standard to buildFAQ assistants is using a set of rules or a nation machine. It induces it easy to developsuch assistant, but at the same time it compiles them prone to mistakes formerly the conversationtakes a different turn from what is enforced by the characterized rules.Level 3: Contextual helpers. The framework troubles: what the user has said before, when/ where/ how she said here today, and so on should affect how those discussions goes. Consideringcontext likewise means being capable of understanding and responding to different and unexpectedinputs. This is how contextual aides differ from FAQ and notification assistants- they can keep the context of what has been said before, gracefully handle unexpectedconversation turns, drive the conversation when the user wanders from the regular conversationpath and be enhanced over time.Contextual assistants are a big step further from simple FAQ assistantsand the best part is that you can already structure them with Rasa.What can we expect in the future? Level 4: Personalised Auxiliaries. As you might expectfrom a human that gets to know you over meter, AI assistants has begun to do the same. Forexample, an AI assistant will learn when its a good time to get in touch and proactivelyreach out based on this context. It will remember your likings and give you the eventual, personalized boundary. Eventually, we will have Level 5: AutonomousOrganization of AssistantsThere will be a group of AI assistants that know every customerpersonally and eventually loped huge parts of company operationsfrom lead generationover sell, auctions, HR, or finance. This is a major leap forward that will make manyyears, but this is a vision we see as reality. In Rasa Masterclass we will be focusing onbuilding Level 3: Contextual AI assistants.So how does Rasa enable developers to go beyondsimple FAQ helpers? The reaction is Rasas machine learning – located approaching to learnfrom conversational data. To understand this better, first causes take a look at whatRasa is and how all of the components work together.Rasa is an open beginning machine learning framework for building contextual AI assistants andchatbots. It consists of two components: natural language understanding and dialogue management. NLU is a so-called ear of your auxiliary whichhelps your assistant to understand what is being said. It takes consumer input in an unstructuredhuman conversation and obtain organized data supplied by a constitute of intents and entities. Intentscan be understood as descriptions that represent the overall goal of the users input. Forexample, a content Hello could have a label greet because the meaning ofthis message is a greeting. Entities are pieces of information that an assistant may needin a certain context.For example, a sense My name is Juste has my honour in it.An aide should extract the name and remember it throughout the conversation to keep theinteraction natural. This is achieved by training a mentioned entity identification pattern to identifyand extract the entities( in this example reputation) for unstructured customer messages.Dialogue administration ingredient( so announced Core) is a so-called brain which sees the decisionon how an aide should respond based on the state of the conversation as well as thecontext. Core learns by observing the patterns from communicative data between the userand an deputy. How Core learns to keep the context? A machine learning model underthe hood observes what the conversation is currently about and likewise looks at what hasbeen said before.In addition to this, the example takes into account the details extractedby the NLU model – some entities can have a target force on how the dialogue managementmodel predicts the next activity. You will learn more details about this process in the upcomingepisodes of the Rasa masterclass. Last but not least, you need a mechanism thatallows your assistant to continue learning from real communicative data. This technologicallycumbersome task is attained easier with Rasa X – a implement specifically designed for allowingyou to easily share your assistant with real customers as soon as possible collect the conversationsthey have with the auxiliary, recall, correct and use them to improve your aide withoutinterrupting the creation. So to summarise, natural language understandingcomponent NLU enables your assistant to understand what the users says, exchange handling componentpredicts how an auxiliary should respond based on the specific state of the conversationand other details, and Rasa X enables your deputy to continue draw lessons from real conversationaldata.All these Rasa components work together to empower you to build huge contextual assistantsthat go beyond simple FAQ interactions. So how do you start developing an assistantwith Rasa? The quickest direction to do it is using the Rasa CLI command rasa init which automaticallycreates a Rasa project for you with some instance grooming data and all the most important projectfiles needed to build an assistant. Lets see how it all works in practice. After installing rasa on your machine, opena terminal and operate a command rasa init. This dominate will create a brand-new Rasa project ina local index which you can specify by providing the directory name.Lets callour directory rasabot. Once the index is initialised, Rasa will automatically fillthis directory with the essential project records as well as some speciman develop dataand qualify the NLU and exchange simulations for you to test. Rasa init sets a simple assistantcalled moodbot which will ask you how “youre feeling” and if you are unhappy it will try tocheer you up by sending you a picture of a cute vampire cub.If you like, you can testthis assistant immediately by having a conversation with it just after the modelings are trained.Lets cause it a shot: I can start a speech by saying hiAn deputies responds with hey how are you I am a bit sadAn assistant understood that I am hapless and mail me something to applaud me up. Letscheck it out. Oh thats cute. It definitely reached me feelbetter. Using Rasa init is a great way to get a testof how a Rasa powered auxiliary directs and what is required to build one. You can alsouse moodot as a basis of your own custom assistant. I would encourage you to play around withmoodbot a bit more – test it on different inputs and see how it behaves.I will see youin the next chapter of the Rasa Masterclass where we will dive even deeper in the developmentof the contextual AI assistants with Rasa.