How to create Chatbot in Python: A Detailed Guide

Over the last few years, chatbots in Python have become quite popular in the tech and business sectors. 

In fact, chatbots are now responsible for about 30% of all tasks. Businesses use chatbots to extend services such as customer support, producing information, and more. With examples like Siri and Alexa, it’s easy to see how a chatbot might improve our lives. 

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In this post, we’ll look at constructing a chatbot in Python with a ChatterBot package that uses machine learning to generate responses.

What is a Chatbot?

A chatbot, also known as a chatterbot, is a software program that uses AI to converse with humans using a digital device through text or speech. Siri and Alexa are two of the two examples that come to mind.

These chatbots are designed to perform a specific task on users’ commands. Chatbots are frequently used to complete tasks like transactions, hotel reservations, and form submissions. With technical developments in artificial intelligence, chatbots enable limitless possibilities.

In any company, chatbots execute over 30% of the activities. Businesses use chatbots for various purposes, including customer service, information delivery, etc.

Chatbots are divided into two types: Rule-Based and Self-Learning.

The rule-based technique instructs a chatbot on how to answer queries based on a set of pre-determined rules taught when it was initially created. These pre-determined rules can be simple or complex. Though rule-based chatbots easily handle simple queries, they cannot handle complicated ones.

A chatbot that can understand things independently is known as a self-learning bot. These take advantage of cutting-edge technology like Machine Learning and Artificial Intelligence to learn from examples and behaviors. Obviously, these chatbots are far more intelligent compared to rule-based bots. There are two types of self-learning bots: retrieval-based and generation-based.

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1. Retrieval–based chatbots

A chatbot that operates on established input patterns and answers is known as a retrieval-based chatbot. The chatbot utilizes a heuristic technique to offer the proper answer after the question/pattern is entered. The retrieval-based paradigm is often used to develop goal-oriented chatbots with elements that are customizable, for example, the bot’s tone and flow to improve the UX further.

2. Generative chatbots

Unlike retrieval-based chatbots, generative bots use seq2seq neural networks to generate responses instead of predefined responses. These chatbots are created on the principle of machine translation, which entails translating source code to different languages. The input is turned into output in the seq2seq technique.

Chatbots right now

We now have smart AI-powered Chatbots employing natural language processing (NLP) to understand and absorb human commands (text and voice). Chatbots have quickly become a standard customer-interaction tool for businesses that have a strong online attendance (SNS and websites).

Python chatbots are exceptionally useful since they allow exchanging quick texts between companies and their customer. Famous chatbots include names like Alexa from Amazon and Siri from Apple. 

A Python-based chatbot intends to take information from you and analyze it using complicated AI algorithms before providing you with a text or vocal response. These bots can react to a wide range of queries and commands as they consistently learn from experience and human commands.

Even though Python chatbots have already started taking over the tech industry, Gartner expects that by 2020, chatbots will handle approximately 85% of customer-business interactions.

Perceiving its growing popularity, developers must know how to use the most popular developed language, Python, to create chatbots.

Today, we’ll show you how to use the ChatterBot Python package to make a simple chatbot in Python. So, let’s begin.

Python package Chatterbot generates automated responses in response to user queries. It generates a variety of replies using a combination of ML techniques. The feature allows programmers to create python chatbots that can talk with people and provide relevant responses. Not only that, but the ML algorithms help improve bot performance with time. 

How does Chatterbot function?

ChatterBot-powered chatbot retains use input and the response for future use. Each time a new input is supplied to the chatbot, this data (of accumulated experiences) allows it to offer automated responses.

The program selects the most relevant response from statements that fit the given input to give a response from a previously defined set of statements and responses. Chatbot’s accuracy increases as much as it assists humans.

How to make a chatbot in Python?

To create a chatbot in Python, you’ll need to import all of the essential libraries and set up the variables you’ll use in your bot. Also, remember when working with text data, you must first undertake data preparation before creating an ML model.

In text data, tokenizing can aid by breaking an expansive data set into consumable pieces, more legible bits (like words). After that, you can proceed to lemmatization, which converts a word into its lemma form. The pickle file is then created to store the python objects that are needed to estimate the bot’s responses.

Dataset testing and training are important aspects of the chatbot development process.

1. Prepare the dependencies

The first step to chatbot development is installation. For the installation, it’s preferable if you create and use Python’s new virtual environment. To do so, write and run the given command in the Python terminal:

Prepare the dependencies

You can also get the latest development version of ChatterBot directly from GitHub. You must write and run the following command:

pip install git+git:/

If you want to improve the command, go ahead and do so:

Prepare the dependencies, improved command

Now that your setup is ready. Let’s move on to the next step creating a chatbot using Python.

2. Import classes

The second step in the Python chatbot construction process is to import classes. All it needs to get started is importing two classes: ChatBot from Chatterbot and ListTrainer from Chatterbot.trainers. You can accomplish so by using the following command:

Import classes

3. Create and train the chatbot

The chatbot you’re making will be a member of the “ChatBot” class. You can train a ChatterBot instance to enhance performance after it has been created. The bot’s training guarantees that it has enough information to begin responding to specific inputs with specific responses. Now you must execute the given command:

Create and train the chatbot

The argument specifies the name of your Python chatbot (which matches the parameter name). You can use the “read only=True” command to prevent the bot’s potential to learn after the training. The command “logic adapters” refers to the list of adapters that the chatbot was trained with.

While “chatterbot.logic.MathematicalEvaluation” helps bots to solve math problems, “chatterbot.logic.BestMatch” assists in selecting the most appropriate, matching result.

Because you’ll need to provide a variety of responses, you can do so by specifying a list of strings that your Python chatbot can use to train and determine the most suitable response for input queries. Here’s an example of an answer that your chatbot can learn using Python:

example of an answer that your chatbot can learn using Python

You can also create and train your bot by writing an instance of “ListTrainer” and providing it with a list of strings such as:


Now your Python chatbot is all set to communicate.

4. Communicate with your Python Chatbot

You can use the .get response() function to communicate with your Python chatbot. When conversing, it should appear like this:

Communicate with your Python Chatbot

It’s important to note, though, that the python-based chatbot might not be able to answer all your questions. You must offer more training data to teach it further because its understanding and learning are currently quite restricted.

5. Train the Python Chatbot with an existing corpus of data

You can leverage a pre-existing corpus of data to further train your Python chatbot in this final stage of how to construct a chatbot in Python. Here’s an example of how to use a corpus of data provided by the bot to train your Python chatbot:

Train the Python Chatbot with an existing corpus of data

The good news is that ChatterBot supports a wide range of languages. As a result, you can designate a portion of a corpus in your preferred language. This is how we build a Python chatbot.

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The method we’ve shown here is just one of many possible approaches to making a chatbot using Python. You may also create a chatbot with NLTK, another useful Python package. While the give chatbot development lesson might be pretty basic with few cognitive skills, it should be enough to give you a fundamental understanding of chatbot anatomy. 

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Why is Python used for chatbots?

Python extends expansive libraries that are easy to refer to while creating chatbots. Its simple syntax fuels the lengthy coding process to accomplish faster than in any other language. Therefore, its usage in creating chatbots is frequent.

How do chatbots utilize NLP?

NLP or natural language processing slowly assists devices in learning human commands and extends automated replies in the same manner. Chatbots use NLP to maintain company-customer communication by extending the most relevant answers to user queries.

Name a few famous chatbots

A few popular chatbots are Siri from Apple, Cortana from Dell, and Alexa from Amazon.

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