Conversational AI chat-bot Architecture overview by Ravindra Kompella

Chatbot Components and Architectures SpringerLink

chatbot architecture diagram

Apps Script handles the

authorization flow and the OAuth 2.0 tokens for user authentication. You can use

Apps Script to build public Chat apps, but isn’t

recommended due to daily

quotas and limits. Artificial Intelligence Markup Language (AIML) is most popularly used for writing patterns and response chatbot architecture diagram in the process of chatbot development. This architectural model of a chatbot is easier to build and much more reliable. Though there cannot be 100% accuracy of responses, you can know the possible types of responses and ensure that no inappropriate or incorrect response is delivered by the chatbot.

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Thus, it is important to understand the underlying architecture of chatbots in order to reap the most of their benefits. For one thing, Copilot allows users to follow up initial answers with more specific questions based on those results. Each subsequent question will remain in the context of your current conversation. This feature alone can be a powerful improvement over conventional search engines. Microsoft has made a deliberate and undeniable commitment to the integration of generative artificial intelligence into its line of services and products. Below are the main components of a chatbot architecture and a chatbot architecture diagram to help you understand chatbot architecture more directly.

Hybrid chatbots

Due to the varying nature of chatbot usage, the architecture will change upon the unique needs of the chatbot. We will explore the usability of rule-based and statistical machine learning – based dialogue managers, the central component in a chatbot architecture. We conclude this chapter by illustrating specific learning architectures, based on active and transfer learning. Remember, building an AI chatbot with a suitable architecture requires a combination of domain knowledge, programming skills, and understanding of NLP and machine learning techniques.

chatbot architecture diagram

Therefore, they are unable to indulge in complex conversations with humans. A chatbot is a dedicated software developed to communicate with humans in a natural way. Most chatbots integrate with different messaging applications to develop a link with the end-users.


Likewise, you can also integrate your present databases to the chatbot for future data storage purposes. 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.

chatbot architecture diagram

It interprets what users are saying at any given time and turns it into organized inputs that the system can process. The NLP engine uses advanced machine learning algorithms to determine the user’s intent and then match it to the bot’s supported intents list. Chatbots or automated conversational programs offer more personalized ways for customers to access services using a text-based interface. With a natural language processing pipeline and predefined rich pattern, AIML can be used to build a smart chatbot. These bots parse user message, find synonyms and concepts, tag parts of speech and find out which rule matches the user query.

Different chatbot architectures

The complimentary credits you get on signing up should be more than enough to complete this tutorial. As you go through the sign-up process, be sure to copy and paste your API key somewhere safe, as you will need it soon. A basic understanding of JavaScript is enough – you don’t need to be super advanced. If you plan to move to another intent after resolution, you might want to add that to the dialog.

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Chatbots receive the intent from the user and deliver answers from the constantly updated database. However, in some cases, chatbots are reliant on other-party services or systems to retrieve such information. This is an important part of the architecture where most of the processes related to data happen. They are basically, one program that shares data with other programs via applications or APIs. The chatbot then fetches the data from the repository or database that contains the relevant answer to the user query and delivers it via the corresponding channel. Once the right answer is fetched, the “message generator” component conversationally generates the message and responds to the user.

Frequently Asked Questions

They also have a brain, which has three main parts are Knowledge source, stock phrases, and conversation memory. When we say something to that, first it analyzes the word and looks for the keyword to give a reply to the users. It analyses the keyword using the three main parts of the brain and gives a reply to the user’s queries. Custom actions involve the execution of custom code to complete a specific task such as executing logic, calling an external API, or reading from or writing to a database. In the previous example of a restaurant search bot, the custom action is the restaurant search logic. Connecting a chatbot framework to a knowledge base that has data structured in a way that can be used as a catalyst to adding knowledge into your chatbot.

chatbot architecture diagram

However, these bots do not run machine learning algorithms or any other APIs unless specially programmed. Now, since ours is a conversational AI bot, we need to keep track of the conversations happened thus far, to predict an appropriate response. 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. AI-enabled chatbots rely on NLP to scan users’ queries and recognize keywords to determine the right way to respond.

Cognitive services like sentiment analysis and language translation may also be added to provide a more personalized response. When a chatbot receives a query, it parses the text and extracts relevant information from it. This is achieved using an NLU toolkit consisting of an intent classifier and an entity extractor. The dialog management module enables the chatbot to hold a conversation with the user and support the user with a specific task. Building a chatbot from scratch that perfectly serves your purpose requires professional help. It is recommended to procure chatbot development services from a trusted company that has good experience in building chatbots that give human-like responses.