Chatbot architecture is the heart of chatbot development. Based on the usability and context of business operations the architecture involved in building a chatbot changes dramatically. So, based on client requirements we need to alter different elements; but the basic communication flow remains the same. Learn how to choose the right chatbot architecture and various aspects of the Conversational Chatbot.
Choosing the correct architecture depends on what type of domain the chatbot will have. For example, you might ask a chatbot something and the chatbot replies to that. Maybe in mid-conversation, you leave the conversation, only to pick the conversation up later. Based on the type of chatbot you choose to build, the chatbot may or may not save the conversation history. For narrow domains a pattern matching architecture would be the ideal choice. However, for chatbots that deal with multiple domains or multiple services, broader domain. In these cases, sophisticated, state-of-the-art neural network architectures, such as Long Short-Term Memory (LSTMs) and reinforcement learning agents are your best bet. Due to the varying nature of chatbot usage, the architecture will change upon the unique needs of the chatbot.
Figure: Architecture of a Conversational Chatbot
Following are the key components of a conversational chatbot architecture:
This is where the core Natural Learning Process (NLP) engine and context interpretation happens.
NLP Engine is the core component that interprets what users say at any given time and converts the language to structured inputs that system can further process. Since the chatbot is domain specific, it must support so many features. NLP engine contains advanced machine learning algorithms to identify the user’s intent and further matches them to the list of available intents the bot supports.
NLP Engine further has two components:
(Learn in detail about NLP engines and which NLP engine to choose)
It manages the actual context of the dialogue. For example, the user might say “He needs to order ice cream” and the bot might take the order. Then the user might say “Change it to coffee”, here the user refers to the order he has placed earlier, the bot must correctly interpret this and make changes to the order he has placed earlier before confirming with the user. Dialog management plugin enables us to do this.
Dialogue management further has following key plugins:
This is the key component in answering users’ frequently asked questions. Q & A system interprets the question and responds with relevant answers from the knowledge base. It has the following components
Plugins offer chatbots solution APIs and other intelligent automation components for chatbots used for internal company use like HR management and field-worker chatbots.
The server that handles the traffic requests from users and routes them to appropriate components. The traffic server also routes the response from internal components back to the front-end systems.
Front-end systems can be any client-facing platforms. They can be the actual chatbot interfaces that reside in various platforms like: