In this exciting age of artificial intelligence and data science that we are living in, it is important to automate as many things as possible. I’m sure you will agree that chatbots are a smart and cool way to do it. They are an application of Natural Language Processing (NLP) that bridges the gap between human and machine interaction. By building conversation scenarios using Machine Learning, an AI chatbot can spring into action and learn, understand questions, inquiries, demands, and give appropriate answers by itself. Rasa is one of the Python open-source frameworks to build assistants. Do note that in this blog, I am using the words ‘chatbot’, ‘bot’, and ‘assistant’ to refer to the same idea, so please don’t be confused.
Allow me to take you into the world of Rasa first, and then explain what Rasa X is all about.
Rasa
In the programming realm, Rasa is a framework that is designed to help you build Task-Oriented Dialogue Systems. ‘Task Oriented’ indicates that the user wants to accomplish something, and a Dialogue System helps them to achieve that task by bringing about an automated two-way conversation. Rasa is a platform that makes use of Machine Learning as well as a heuristic approach to build custom chatbots. The core data required to build an assistant is all the examples that you want your system to learn from. Rasa combines the different aspects of the history of NLP which includes rules-based, statistical methods, neural networks, and transformers.
There are two main parts in any kind of chatbot assistant and they are:
1. Natural Language Understanding (NLU) – The input is any text data that a human has generated and makes an output machine-readable information.
• Rule-Based – The machine understands the pattern – for instance, let’s say, identifying an account number upon giving a regular expression for it. This is a very fast approach but also has problems like not identifying if any different pattern is given by the user.
• Neural – It is a transformer-based approach that classifies the given input into different categories based on its previous learnings on the examples that the developer has given. Rasa uses Dual Intent and Entity Transformer (DIET). It generally identifies examples that it hasn’t seen before.
2. Decision Making – Based on the earlier conversation flow, the task here is to decide what should be the next step. The above two approaches can also be utilized here wherein ‘rule-based’ means certain rules are defined like when a certain intent occurs and then some tasks are performed, or specific replies are given whereas a ‘neural approach’ iterates through all training examples and then decides the next item. This is a more natural, conversation-driven approach.
Now that you are familiar with Rasa, let me introduce you to Rasa X.
Rasa X
Rasa X is built on top of Rasa. It is an interactive GUI-based environment that helps in developing the assistant interactively. Instead of writing all examples and training data in the folder manually, you can use Rasa X to write all of it. It needs its own installation in the virtual environment.
Voice Integration:
Do keep in mind that you cannot directly add voice channels while developing a chatbot but once all the logic is completed with the help of integration of some different frameworks, it can be achieved. For example, if you want to add the assistant to your own website, then a React-based component is needed. It uses HTML and JavaScript as frontend, and for different channels, the approaches vary a lot. It depends on the development framework of the channel. Take a look at this list of some of the channel integrations:
Now let’s focus on some of the useful applications of a chatbot.
Customer support: When a customer’s behavior is abnormal while browsing through a website, it is more likely that they need customer support. In such scenarios, a chatbot can come and help them to solve their queries.
Product suggestions: When a customer does not buy a particular thing but is browsing through it, an assistant can come into the picture and say, “Hey! Please buy this product instead of that one.” When a customer purchases a product, then it is a good idea that a bot acts like a friend and recommends a similar product in such a way that the customer feels satisfied.
Banking alerts: Most chatbots are capable of informing users about their account balances, recent transactions, credit card payment dates, and so on.
Order tracking: This is a perfect simplistic example of a task-oriented dialogue system wherein the user will just give the order ID and they can track it. Not just that, they can also complain here about any delays, cancellations, etc.
Making bookings and reservations: It is the convenient automation of booking a taxi, restaurant, event, etc. wherein a user does not have to browse a website to do so. Once the intent is conveyed to the chatbot, it becomes the task of the chatbot to capture the details and make the reservation.
Product alerts: Once a chatbot is live, it can give alerts regarding new products launched, new offers and discounts, rewards, and scratch cards. As you must have seen, new-age consumers are very responsive to alerts.
Comparison of Rasa X with Other Tools
You might have read that there are many tools like Google Dialog Flow, AWS Lex and so on, and they do a similar job. Let’s compare them and examine how they are different.
There you have it! In this blog, I have introduced you to Rasa X and shared some details connected to the integrations and applications of Rasa X that are possible thanks to the magic of machine learning algorithms. I hope its comparison with other tools is also useful for you.
Stay tuned for my upcoming blog on the installation, folder structure, controls, and features of Rasa and Rasa X that help develop an assistant.
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