Conversational AI Chatbot: Architecture Overview

Chat app can
receive and respond to user interactions
in a number of ways. A user interaction is any action that a user takes to
invoke or interact with a Chat app. The trained data of a neural network is a comparable algorithm with more and less code. When there is a comparably small sample, where the training sentences have 200 different words and 20 classes, that would be a matrix of 200×20.

• From healthcare to hospitality, retail to real estate, insurance to aviation, chatbots have become a ubiquitous and useful feature.
• Your chatbot will need to ingest raw data and prepare it for moving data and transforming it for consumption by business analysts.
• Through chatbots, acquiring new leads and communicating with existing clients becomes much more manageable.
• Thereby, making the designing and planning of your chatbot’s architecture crucial for your business.
• Continuously iterate and refine the chatbot based on feedback and real-world usage.
• This is important because you’ll need to ensure that platform or service that you choose will offer SLAs or future updates for the channel you choose for the chatbot.

Rather than employing a few if-else statements, this model takes a contextual approach to conversation management. The entity extractor extracts entities from the user message such as user location, date, etc. Rasa NLU is one such entity extractor (as well as an intent classifier). When provided with a user query, it returns the structured data consisting of intent and extracted entities. Rasa NLU library has several types of intent classifiers and entity extractors. You can either train one for your specific use case or use pre-trained models for generic purposes.

Command-line application or script

Such firms provide customized services for building your chatbot according to your instructions and business needs. Whereas, with these services, you do not have to hire separate AI developers in your team. Besides, if you want to have a customized chatbot, but you are unable to build one on your own, you can get them online. Services like Botlist, provide ready-made bots that seamlessly integrate with your respective platform in a few minutes. Though, with these services, you won’t get many options to customize your bot.

These engines are the prime component that can interpret the user’s text inputs and convert them into machine code that the computer can understand. This helps the chatbot understand the user’s intent to provide a response accordingly. The last phase of building a chatbot is its real-time testing and deployment. Though, both the processes go together since you can only test the chatbot in real-time as you deploy it for the real users. But that is very important for you to assess if the chatbot is capable enough to meet your customers’ needs. Monitor the entire conversations, collect data, create logs, analyze the data, and keep improving the bot for better conversations.

How to Use the OpenAI Dependency

You should consider how you want your Chat app to
interact with users. The following sections describe conversation patterns that
your Chat app might implement. Non-interactive Chat apps use the Chat API to send
messages or other types of requests to Chat. IBM Cloud Pak for Data is an open, extensible data platform that provides a data fabric to make all data available for AI and analytics, on any cloud.

The first step is to define the goals for your chatbot based on your business requirements and your customers’ demands. When you know what your chatbot should and would do, moving on to the other steps gets easy. Regardless of how simple or complex a chatbot architecture is, the usual workflow and structure of the program remain almost the same. It only gets more complicated after including additional components for a more natural communication. Based on how the chatbots process the input and how they respond, chatbots can be divided into two main types. In general, a chatbot works by comparing the incoming users’ queries with specified preset instructions to recognize the request.

You can also combine 2 statements into 1 in the case of missing inputs like date and time. However, exercise caution with this approach — combining 2 asks can sometimes confuse users. In case you were wondering — “We haven’t, still written a single word of content for the interaction that is supposed to be conversational”, here it is [finally!

As such, TOGAF provides a complete framework for designing and implementing an enterprise’s IT architecture, including its data architecture. As discussed earlier here, each sentence is broken down into individual words, and each word is then used as input for the neural networks. The weighted connections are then calculated by different iterations through the training data thousands of times, each time improving the weights to make it accurate. Neural Networks are a way of calculating the output from the input using weighted connections, which are computed from repeated iterations while training the data. Each step through the training data amends the weights resulting in the output with accuracy. It is the server that deals with user traffic requests and routes them to the proper components.

Web or HTTP service

Google has Dialogflow, which is essentially a SaaS based platform to build the bot. In this post, you’ll learn how to choose the best chatbot architecture to ensure that your chatbot or conversational agent is built on a solid framework. If you choose the wrong architecture, you may be opening yourself to a bunch of technical debt that will make future development and maintenance more difficult. Chat apps can also send messages or other requests to
Chat, which aren’t triggered by direct user interactions in
Chat. Instead, these Chat apps can be
triggered—for example, by third-party applications, or using a command-line
invocation from a user, but users can’t interact with these
Chat apps directly in Chat.

Some types of channels include the chat window on the website or integrations like Whatsapp, Facebook Messenger, Telegram, Skype, Hangouts, Microsoft Teams, SalesForce, etc. And, no matter the complexity of the chatbot, the basic underlying architecture of it remains the same. Before investing in a development platform, make sure to evaluate its usefulness for your business considering the following points. For instance, you can build a chatbot for your company website or mobile app.

Components of a Chatbot Architecture

And they can be integrated into different platforms, such as Facebook Messenger, WhatsApp, Slack, Google Teams, etc. Chatbot developers may choose to store conversations for customer service uses and bot training and testing purposes. Chatbot conversations can be stored in SQL form either on-premise or on a cloud. Without getting deep into the specifics of how AI systems work, the chatbot architecture diagram basic principle is that the more input data an AI can access, the more accurate and useful information can be produced. Copilot in Bing taps into the millions of searches made on the Microsoft Bing platform daily for its LLM data collection. The renderTypewriterText function needs to create a new speech bubble element, give it CSS classes, and append it to chatbotConversation.

Whereas, the more advanced chatbots supporting human-like talks need a more sophisticated conversational architecture. Such chatbots also implement machine learning technology to improve their conversations. Some chatbots work by processing incoming queries from the users as commands. These chatbots rely on a specified set of commands or rules instructed during development. The bot then responds to the users by analyzing the incoming query against the preset rules and fetching appropriate information. Choosing the correct architecture depends on what type of domain the chatbot will have.

Natural Language Processing (NLP)

In August 2023, Google’s Bard became generally available to everyone. Like Bing Chat and ChatGPT, Bard helps users search for information on the internet using natural language conversations in the form of a chatbot. Congratulations on successfully building your own chatbot using the GPT-4 API!

Depending upon your business needs, the ease of customers to reach you, and the provision of relevant API by your desired chatbot, you can choose a suitable communication channel. To create a chatbot that delivers compelling results, it is important for businesses to know the workflow of these bots. From the receipt of users’ queries to the delivery of an answer, the information passes through numerous programs that help the chatbot decipher the input.

The model (sometimes called the engine) is what actually creates the language. GPT-4 is on limited release via a waiting list at present, so if you can’t access it right now, you can use GPT-3.5-turbo instead. All code in this project works with both models, and GPT-3.5-turbo is also highly capable. Having rendered the message to the DOM, you now need to push an object to conversationArr in the format we looked at previously. This object will have a role of ‘user’ and the content property will hold whatever the user has typed in the text input. So now we can be sure that we will be able to have a logical, flowing conversation with the chatbot.

• I hope this post covers some of the more fundamental and essential aspects to architecture to consider for building a chatbot.
• This type of chatbot is very rarely used, as it requires the implementation of complex algorithms.
• To explore in detail, feel free to read our in-depth article on chatbot types.
• Its Data Management Body of Knowledge, DAMA-DMBOK 2, covers data architecture, as well as governance and ethics, data modelling and design, storage, security, and integration.
• Chatbots can be used to simplify order management and send out notifications.
• This also means added complexity, uncertainty and increased chances of error at each step.

A modular and well-organized architecture allows developers to make changes or add new features without disrupting the entire system. Fallback — Design a fallback script where your bot has no clue on how to respond. As you can see, updating reminders, the way I have here, turns out to be a multi-step process with a lot of back and forth communication.