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
Today, almost all companies have
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 primarily utilized to enhance the corpus by generalizing it and avoiding the creation of static read and write responses.
– **Tag**: Grouping similar text instances and using them as targeted outputs to train neural networks.
### Simple Text-based Chatbot using NLTK with Python
#### Algorithm for this text-based chatbot
– Design NLTK responses and a chat utility function to interact with the user.
– Run the chat utility function.
#### Example of a possible corpus
– Reflections are the pairs or corpus defined above.
#### Code to import corpus
– We have designed a function that allows users to interact with a bot using text. The function keeps the chat window open until instructed to break or quit. The text bot’s name is Jason.
#### Evaluate or test the chatbot
– Interaction paths and evaluation methods for the built text bot are discussed. Care must be taken with the corpus [pairs, reflections] to ensure it is generic yet differentiable to avoid misinterpretations.
### Data Pre-processing
#### Text case [upper or lower] handling
– Convert all input data to either upper or lower case to avoid misrepresentation and misinterpretation of words.
#### Tokenization
– Convert sentences into individual words.
#### Stemming
– Finding similarities between words with the same root words to reduce the bag of words.
#### Generate BOW [Bag of Words]
– Convert words into numbers by generating vector embeddings from the tokens generated. The input provided to the neural network model is for text comprehension.
The code for stemming is used to perform stemming.
The output or targets are one-hot encoded [referred to as “TAG” in the corpus].
Words are converted into numbers by generating vector embeddings from the tokens.
Tags from the corpus are listed.
The tags are one-hot encoded.
A classifier model is designed to train on the corpus with the target variable being the tags.
The implementation uses a neural network classifier.
The text classification process involves various steps such as preprocessing, tokenization, stemming, and generating bag of words.
A chat utility function is designed to interact with users using text.
A function is designed to enable the bot to speak the output text.
The chatbot algorithm involves multiple steps including data preprocessing, tokenization, stemming, and using neural networks to classify words.
A speech function is created to convert text to speech using Google’s GTTS.
A listen function is created to enable the computer to recognize speech input from the user. These time limits are established to prevent any delays in the conversation if there is no speech detected.
A chatbot window has been created to allow users to interact with a bot using voice commands. The chat window will remain open until the user requests to end the conversation. The voice bot, named Lilia, follows a specific algorithm:
– Lilia introduces herself to the user.
– Lilia prompts the user to speak.
– Lilia listens to the user using the defined listen function.
– The listen function converts the user’s voice into text.
– A neural network classifier predicts the user’s input using the text.
– The prediction is then converted to speech by Lilia using the speak function.
– If the user’s speech is unclear, Lilia asks the user to repeat until understood.
– If the user requests to quit, Lilia ends the chat session.
To evaluate the chatbot, various interactions can be explored, as shown in the accompanying video. Understanding the client’s goals for the chatbot and designing a conversation that aligns with those goals are essential steps in the development process.
When building a chatbot, the choice between code-based frameworks and chatbot platforms depends on the project’s requirements. Testing the chatbot with beta users is crucial to ensure it fulfills its intended purpose and continues to improve over time. The success or failure of a model depends on the corpus used to build it. While we created our own corpus, it can be time-consuming to include all scenarios in one. Consider using a ready corpus if available royalty-free with all possible training scenarios. Additionally, explore voice-based corpus options alongside text-based data.
If you’re interested in Artificial Intelligence technologies and want to pursue a career in the field, consider upskilling with Great Learning’s PG course in Artificial Intelligence and Machine Learning.
### Frequently Asked Questions
**What is a chatbot, and how does it work?**
A chatbot is software that mimics human interaction through voice or text exchanges. Chatbots are increasingly used in B2B and B2C settings for basic activities or issue resolution.
**How does a chatbot work step by step?**
Chatbots follow three actions: understanding, taking action, and responding. They analyze the user’s message, interpret it, take action based on algorithms, and provide a suitable response.
**Is Alexa a chatbot?**
Alexa can be considered a chatbot. Amazon has introduced a feature for iOS that allows users to interact with Alexa through voice commands and see responses on a screen.
**Which algorithm is best for a chatbot?**
Decision trees, recurrent neural networks, NLP, and Naive Bayes are commonly used algorithms for chatbots.
**Is developing a chatbot easy?**
Beginners can start developing chatbots using platforms that are user-friendly and require no coding experience. Understanding drag-and-drop functionality is essential.
**What are two types of chatbots?**
There are AI chatbots and rule-based chatbots. AI chatbots can operate without human intervention and have advanced capabilities and contextual awareness, requiring less training data. Please rewrite the following sentence:
“The dog ran quickly through the park.”
The dog sprinted through the park with speed. Can you please rewrite this sentence for me? Please rephrase this sentence.
