Artificial Intelligence (AI) powered chatbots have gained immense popularity as a customer service tool. These chatbots use natural language processing (NLP) technology which is developed through Python programming language. In 2019, chatbots handled 69% of customer service requests, as compared to just 20% in 2017, showcasing their potential to revolutionise the way customer service functions. Hence, chatbots have become a crucial element of customer service, ensuring efficient handling of product discovery, account creation, refund queries and other similar tasks.
Python offers various options for creating a chat program. One such way is to design a chat box by creating a new thread for each user. The ‘tkinter’ module is a powerful graphical user interface kit, which allows users to construct a chatroom by opening a new window for every participant.
Forms of Chatbots
Chatbots primarily exist in two main types:
- Chatbots based on rules
- Chatbots that use Artificial Intelligence to learn and gather new information which can either be retrieval-based or generative.
Chatbots that operate according to predefined rules
A chatbot that follows a set of rules or a decision tree to determine the next course of action in response to the user’s input is known as a rule-based chatbot. After receiving input from the user, the chatbot methodically checks each rule present in its database until it finds a matching rule. Once it finds the relevant rule, it performs the appropriate action accordingly.
To automate dull and repetitive responsibilities like answering frequently asked questions and providing crucial product information, rule-based chatbots are often utilised in customer service applications. Additionally, these automated systems can also function as guidance tools or hint systems for players in video games, aiding the gaming experience.
Flexible conversational robots
Artificial Intelligence (AI) powered chatbots with the ability to learn from user interactions and improve automatically without complex coding are called self-learning chatbots. These chatbots can be trained using minimal data, which helps them become more accurate and responsive as time passes. The technology is rapidly gaining popularity as it offers a cost-effective, quick-to-deploy, and scalable solution to customer service requirements.
Adaptive chatbots are advantageous for businesses as they can offer a more customised service to clients, leading to increased satisfaction.
Information Retrieval-based conversational robots: Retrieval-based chatbots are designed to respond to commonly asked questions with pre-programmed answers. When a user submits a query, the bot searches through a database of pre-determined responses and provides the most applicable answer to the user. These chatbots do not have the ability to offer unusual responses as they can only provide answers that are programmed into the database.
Generic chatbots: Generative chatbots, on the other hand, have the ability to create unique responses to user questions. These bots come with a database of pre-programmed answers to frequently asked questions. When a user asks a question, the bot can mix and match from the existing answers in the database and generate a new response.
Guidelines for Programming a Chatbot in Python
Defining Program RequirementsThe initial phase of chatbot creation is to utilise Python’s ChatterBot package. For best results, use the most recent version of the Python virtual environment.
Use the following command in the Python shell to start the Python interpreter:
pip install chatterbot
pip install chatterbot_corpus
The command may be improved further by adding:
pip install --upgrade chatterbot_corpus
pip install --upgrade chatterbot
Importing Foreign CoursesSubsequently, classes need to be loaded into the application. The ChatBot and ListTrainer classes from the ChatterBot library are critical for this.
Use the following command to import the classes:
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
Developing and Sustaining the ChatbotNow that the requisite classes have been imported, it’s time to create an instance of the “ChatBot” class. This instance of the class, which will serve as the chatbot, must be trained to possess the necessary knowledge and capabilities to respond suitably to user queries. The goal of the training process is to ensure that the chatbot has sufficient knowledge for effective user interaction and accuracy in providing responses to their inquiries.
Use this command to train the chatbot:
my_bot = ChatBot(name='PyBot', read_only=True,
The Python chatbot’s name is specified as a value that is passed into the function as the ‘name’ argument. After the bot has been trained, the ‘read only=True’ command can be used within the function to prevent it from learning new material. When used as a command, the term ‘logic adapters’ is used to refer to the set of adapters that are used for training the chatbot.
The ‘chatterbot.logic.MathematicalEvaluation’ command can be used to instruct the bot to evaluate mathematical expressions, providing results for their solutions. Additionally, the ‘chatterbot.logic.BestMatch’ command can be used to determine the best match for a given input.
It is important to remember that a chatbot will only be able to respond to questions if it is provided with a list of predetermined answers. To accomplish this, you can use a technique known as ‘training’ when creating a Python chatbot. Training involves selecting the most suitable answer from a range of potential outcomes.
In order to further refine and enhance the performance of the chatbot, you can use an instance known as “ListTrainer” and provide it with a list of string inputs that are similar in nature to aid in the development and training process. This will help the chatbot to better understand the types of conversations it will be expected to handle.
There is currently functional chatbot conversation.
Conversing through an Online ForumUse the .get_response() method to communicate with the chatbot. During the course of the conversation, it will appear like this:
I hope you’re well.
>>> print(my_bot.get_response("i feel awesome today))
wonderful, I’m glad to hear that.
>>> print(my_bot.get_response(what's your name?"))
Hey, my name is pybot. Inquire of me on mathematical matters.
>>> print(my_bot.get_response(show me the Pythagorean theorem"))
It may be shown that a squared + b squared equals c squared.
>>> print(my_bot.get_response("do you know the law of cosines?"))
The formula for c2 is as follows: c=a2 + b2 – 2ab cos (gamma)
It is important to note that the chatbot is not yet sufficiently equipped to fully understand and answer all of your questions. To improve the chatbot’s performance and expertise, it is necessary to provide additional training data. This will enable the chatbot to gain more knowledge and experience, thus allowing it to better comprehend and respond to inquiries.
Chatbots can be trained using a corpus of data.Finally, when the chatbot development is complete, the last step is to train it. This can be done using an existing dataset.
Python chatbots can be trained by using the bot itself as an example.
from chatterbot.trainers import ChatterBotCorpusTrainer
ChatterBot’s multilingual support is its strongest feature. Choose a specific portion of a corpus in the language of your choice.
Python offers a range of possibilities for developers looking to develop chatbots. For more advanced features and comprehensive documentation, external libraries such as spaCy, DeepPavlov, and NLTK can be employed. spaCy, an open-source toolkit, provides tokenization, part-of-speech tagging, sentence boundary detection, similarity, text categorization, and rule-based matching services. Additionally, NLTK, another open-source program, is made even more user-friendly by its accompanying lexical databases, such as WordNet. Lastly, DeepPavlov is another open-source framework that is powered by TensorFlow and Keras.