Understanding Architecture Models of Chatbot and Response Generation Mechanisms

Most companies today have an online presence in the form of a website or social media channels. They must capitalize on this by utilizing custom chatbots to communicate with their target audience easily. Chatbots can now communicate with consumers in the same way humans do, thanks to advances in natural language processing. Businesses save resources, cost, and time by using a chatbot to get more done in less time. Intelligent chatbots are already able to understand users’ questions from a given context and react appropriately. Combining immediate response and round-the-clock connectivity makes them an enticing way for brands to connect with their customers.

As the backend integrations fetch data from a third-party application, the knowledge base is inherent to the chatbot. The conversations between chatbots and humans happen through channels. Below is the basic chatbot architecture diagram that depicts how the program processes a request.

Top 12 Live Chat Best Practices to Drive Superior Customer Experiences

HealthTap, a telehealth platform, integrated its chatbot with electronic health records (EHR) systems, allowing users to access their medical information and schedule appointments. This integration was made possible by a well-structured chatbot architecture. Modular architectures divide the chatbot system into distinct components, each responsible for specific tasks. For instance, there may be separate modules for NLU, dialogue management, and response generation. This modular approach promotes code reusability, scalability, and easier maintenance. This article focuses on what I call “Transactional Chatbots” — Bots that help users perform certain tasks based on user input.

This type of Chat app can’t be shared in other

Chat spaces or with other teams, and can’t be published to the

Google Workspace Marketplace. Incoming webhooks are recommended for

Chat apps to report alerts or status, or for some types of

Chat app prototyping. A data architecture can draw from popular enterprise architecture frameworks, including TOGAF, DAMA-DMBOK 2, and the Zachman Framework for Enterprise Architecture.

Conversational AI chat-bot — Architecture overview

Chatbots can also transfer the complex queries to a human executive through chatbot-to-human handover. The output from the chatbot can also be vice-versa, and with different inputs, the chatbot architecture also varies. Additionally, the dialog manager keeps track of and ensures the proper flow of communication between the user and the chatbot. Chatbot architecture represents the framework of the components/elements that make up a functioning chatbot and defines how they work depending on your business and customer requirements. The sole purpose to create a chatbot is to ensure smooth communication without annoying your customers. For this, you must train the program to appropriately respond to every incoming query.

At times, a user may not even detect a machine on the other side of the screen while talking to these chatbots. A knowledge base is a library of information that the chatbot relies on to fetch the data used to respond to users. In this article, we explore how chatbots work, their components, and the steps involved in chatbot architecture and development. ~50% of large enterprises are considering investing in chatbot development.

It involves processing and interpreting user input, understanding context, and extracting relevant information. NLU enables the chatbot to comprehend user intents and respond appropriately. In this architecture, the chatbot operates based on predefined rules and patterns. It follows a set of if-then rules to match user inputs and provide corresponding responses.

• These knowledge bases differ based on the business operations and the user needs.
• The similarity of the user’s query with a question is the question-question similarity.
• However, still, you cannot be sure what responses the model will generate.
• It is foundational to data processing operations and artificial intelligence (AI) applications.

Different frameworks and technologies may be employed to implement each component, allowing for customization and flexibility in the design of the chatbot architecture. Chatbot architecture refers to the basic structure and design of a chatbot system. chatbot architecture diagram It includes the components, modules and processes that work together to make a chatbot work. In the following section, we’ll look at some of the key components commonly found in chatbot architectures, as well as some common chatbot architectures.

Using Natural Language Processing (NLP)

So depending on the action predicted by the dialogue manager, the respective template message is invoked. If the template requires some placeholder values to be filled up, those values are also passed by the dialogue manager to the generator. Then the appropriate message is displayed to the user and the bot goes into a wait mode listening for the user input. When accessing a third-party software or application it is important to understand and define the personality of the chatbot, its functionalities, and the current conversation flow. Delving into chatbot architecture, the concepts can often get more technical and complicated.