Improving Flow of Chatbots with RASA Events

The conversational skills of artificial intelligence are breaking new grounds with rapid algorithmic advancements and sophisticated frameworks. One such open-source chatbot framework, RASA, is gaining momentum with its contextual and human-like interaction abilities. Development of chatbots with RASA’s natural language understanding (NLU) and dialogue engine, RASA Core, enable businesses to resolve complex user queries. Today, global businesses are exploring chatbot development services backed by RASA’s machine learning algorithms for enhancing user experiences across digital platforms.

This blog post guides developers and businesses through RASA events, a functionality that sequences conversation to streamline user conversations.

Understanding RASA events

Every conversation in RASA is represented as a sequence of events. These events cover important user tasks and queries that are categorized as-

1) General Purpose events- It envelopes setting up of slot, restarting, pausing, and resuming a conversation, forcing a follow-up action, and more.

2) Automatically Tracked Events- It entails a user’s conversation with the interface, with the functionality to undo a message and log an executed action.

To create a chatbot with human-like capabilities, it is very important to have the availability of context and flexibility to manage certain events. This functionality is assured while building chatbots with RASA events and hence improves the power of a chatbot.

Also, RASA’s underlying Python language enables real-time modification and bug detection supported by rich libraries of tools. Its pre-defined environments enable developers to analyze data and build natural language processing (NLP) solutions and chatbots with RASA efficiently.

General Purpose Events

General-purpose events are related to the context (which can be managed with the help of slots) and the flow of the conversation.

In RASA, the context of a conversation can be handled by using slot values and it helps to decide the flow of chat. Slot values can be used in the user stories to provide context to the conversation and decide the next action.

Events provide flexibility to control the context by manually setting or resetting the slot values from custom actions.

Automatically Tracked Events

Automatically tracked events are related to the actions and utterances. In some situations, the bot needs to assume some default inputs from the user to automatically call an intent to run a function. Consider a case in which there are two custom actions ActionShowList and ActionUpdateList which are called independently when corresponding two different intents are classified.

Developing Intelligent Chatbots with RASA and Oodles AI

We, at Oodles, have experiential knowledge in using Python for building chatbots with RASA, virtual assistants, and conversation AI models. Our AI development team deploys comprehensive neural networks to build machine learning models for manufacturing, retail, eCommerce, healthcare, and other global businesses. We combine AI’s NLP and computer vision technologies to build intelligent chatbot interfaces that fulfill the customer service requirements of businesses.

Talk to our AI team to know more about our Chatbot and artificial intelligence services

Learn more: Improving Flow of Chatbots with RASA Events


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