Introduction to Chatbot | Artificial Intelligence Chatbot Tutorial -2024

Chatbots have been gaining popularity over the years and can be seen on almost every website we visit. They are being increasingly used by businesses for customer support and are predicted to improve customer service for many industries in the coming years. And, of course, with AI in the picture, it only makes sense to introduce well-functioning chatbots. So, in this chatbot tutorial, we will talk about how you can also build an AI chatbot. Let us look at what we will be learning today!

Introduction to Chatbots

Chatbots are not a recent development. They are simulations that can understand human language, process it, and interact back with humans while performing specific tasks. For example, a chatbot can be employed as a helpdesk executive. Joseph Weizenbaum created the first chatbot in 1966, named Eliza. It all started when Alan Turing published an article named “Computer Machinery and Intelligence” and raised an intriguing question, “Can machines think?” ever since, we have seen multiple chatbots surpassing their predecessors to be more naturally conversant and technologically advanced. These advancements have led us to an era where conversations with chatbots have become as normal and natural as with another human. Before looking into the AI chatbot, learn the foundations of artificial intelligence.

Check out the detailed eligibility requirements for the different AI courses offered by Great Learning

Today, almost all companies have chatbots to engage their users and serve customers by catering to their queries. We practically will have chatbots everywhere, but this doesn’t necessarily mean that all will be well-functioning. The challenge here is not to develop a chatbot but to develop a well-functioning one.

Let’s have a look at the basics of how to make a chatbot in Python:

Identifying opportunities for an Artificial Intelligence chatbot

The first step is to identify the opportunity or the challenge to decide on the purpose and utility of the chatbot. To understand the best application of Bot to the company framework, you will have to think about the tasks that can be automated and augmented through Artificial Intelligence Solutions. The respective artificial intelligence solution broadly falls under two categories for each type of activity: “Data Complexity” or “Work Complexity”. These two categories can be further broken down into 4 analytics models: Efficiency, Expert, Effectiveness, and Innovation.

Types of Chatbots

There are many types of chatbots available. A few of them can be majorly classified as follows:

Text-based chatbot: In a text-based chatbot, a bot answers the user’s questions via a text interface.
Voice-based chatbot: In a voice or speech-based chatbot, a bot answers the user’s questions via a human voice interface.

There are mainly two approaches used to design the chatbots, described as follows:

In a Rule-based approach, a bot answers questions based on some rules on which it is trained on. The rules defined can be very simple to very complex. The bots can handle simple queries but fail to manage complex ones.
Self-learning bots are the ones that use some Machine Learning-based approaches and are definitely more efficient than rule-based bots. These bots can be further classified into two types: Retrieval Based or Generative.

There are many types of chatbots available, depending on the complexity. A few of them can be majorly classified as follows:

Traditional chatbots: They are driven by system and automation, mainly through scripts with minimal functionality and the ability to maintain only system context.
Current chatbot: They are driven by back-and-forth communication between the system and humans. They have the ability to maintain both system and task contexts.
Future chatbot: They can communicate at multiple levels with automation at the system level. They have the ability to maintain the system, task, and people contexts. There is a possibility of introducing of master bots and eventually a bot OS.

Top Applications of Chatbots

Virtual reception assistant
Virtual help desk assistant
Virtual tutor or teacher
Virtual driving assistant
Virtual email, complaints, or content distributor
Virtual home assistant [example: Google Home]
Virtual operations assistant [example: Jarvis from the movie Iron Maiden]
Virtual entertainment assistant [example: Amazon Alexa]
Virtual phone assistant [example: Apple Siri]
Assist the visually impaired person in describing the surroundings
Can help a warehouse executive in locating the stocked product

The Architecture of chatbots

Typical chatbot architecture should consist of the following:

Chat window/session/front end application interface
The deep learning model for Natural Language Processing [NLP]
Corpus or training data for training the NLP model
Application Database for processing actions to be performed by the chatbot

Please refer to the below figure to understand the architectural interface:

Corpus or Training Data

Corpus means the data that could be used to train the NLP model to understand the human language as text or speech and reply using the same medium. The corpus is usually huge data with many human interactions .

Corpus can be designed using one of the following methods:

Manual
Accumulated over time in an organized fashion.

Following are the components of a corpus:

Input pattern
Output pattern
Tag

Let us take a business scenario where we need to deploy and design a chatbot that acts as a virtual help desk assistant. Keeping this business scenario in mind, a sample corpus is manually designed as follows:

Pairs: Collection of all transactions [Input and Output] to be used for training the chatbot.
Read/patterns: Patterns that are or could be expected as inputs from end-users.
Response: Patterns that are or could be delivered as outputs from the chatbot to end-users.
Regular Expressions: Patterns that are used to generalize patterns for reading and response. This is mainly used to optimize the corpus by making it more generic and avoiding generating static read and write responses.
Tag: To group similar text instances and use the same as targeted outputs to train neural networks.

Simple Text-based Chatbot using NLTK with Python

Algorithm for this text-based chatbot

Input the corpus

Design NLTK responses and converse-based chat utility as a function to interact with the user.
Run the chat utility function.

Example of a possible corpus

Code to import corpus

Reflections are the pairs or corpus that we have defined above.

Chatbot window

We have designed a function that enables the user to interact with a bot using text. The function keeps the chat window alive unless it is asked to break or quit. The name of our text bot is Jason. The algorithm for this function is as follows:

The text bot introduces itself to the user.
Chatbot asks the user to type in the chat window using the NLTK converse function.
Bot understands what the user has typed in the chat utility window using NLTK chat pairs and reflections function.

Evaluate or test the chatbot

There could be multiple paths using which we can interact and evaluate the built text bot.

Since there is no text pre-processing and classification done here, we have to be very careful with the corpus [pairs, refelctions] to make it very generic yet differentiable. This is necessary to avoid misinterpretations and wrong answers displayed by the chatbot. Such simple chat utilities could be used on applications where the inputs have to be rule-based and follow a strict pattern. For example, this can be an effective, lightweight automation bot that an inventory manager can use to query every time he/she wants to track the location of a product/s.

Data pre-processing

Text case [upper or lower] handling

Convert all the data coming as an input [corpus or user inputs] to either upper or lower case. This will avoid misrepresentation and misinterpretation of words if spelled under lower or upper cases.

Tokenization

Convert a sentence [i.e., a collection of words] into single words.

Sentence Tokens

Code to perform tokenization

Stemming

It is a process of finding similarities between words with the same root words. This will help us to reduce the bag of words by associating similar words with their corresponding root words.

Code to perform stemming:

Generate BOW [Bag of Words]

Process of converting words into numbers by generating vector embeddings from the tokens generated above. This is given as input to the neural network model for understanding the written text.

Code to perform stemming:

One hot encode the output or targets [In our case, we have defined them as “TAG” in the corpus]

Process of converting words into numbers by generating vector embeddings from the tokens generated above.

Tag from the corpus:

 ['access',
 'catalog',
 'goodbye',
 'greeting',
 'hours',
 'l2support',
 'location-Bangalore',
 'location-Mumbai',
 'machine',
 'message',
 'name']

One hot encoded tag:

Text classification

Design a classifier model which can be trained on the corpus with respect to the target variable, i.e., the Tag from the corpus. There is a list of classifiers that can be used for this purpose which are as follows:

Multinomial Naïve Bayes
Support Vector Machines [SVM]
Neural network classifier

In this implementation, we have used a neural network classifier.

Code for Neural Network classifier:

Text-based Chatbot using NLP with Python