Building a Conversational Chatbot

Explore the fundamentals of building a conversational chatbot in Python, focusing on dynamic learning, best match responses, and custom training. Understand how to create interactive bots that learn from user inputs and handle varied queries. Gain practical skills to design intelligent, adaptable chatbots for real-world applications.

We'll cover the following...

What makes a chatbot conversational?
How to create a conversational chatbot
The execution flow of the chatbot
Is this chatbot truly intelligent?

We have explored how to create specialized chatbots: a rule-based clock bot and an AI-powered calculator. Each one served a unique purpose but had specific limitations. Now, we’re diving into the heart of chatbot development—a conversational chatbot capable of learning and responding dynamically to user inputs.

A conversational chatbot is not just a tool; it’s an experience. Imagine asking a bot, “What’s the weather like?” or “Tell me a joke,” and getting meaningful, human-like responses. That’s what we will build today (or at least a very basic version). Let’s unravel the science behind it step by step.

What makes a chatbot conversational?

Unlike our previous bots, a conversational chatbot is designed to engage users in a natural flow of dialogue. It’s no longer about one-off queries or rigid rules; it’s about understanding, context, and learning. Here’s what sets it apart:

Dynamic learning: Dynamic learning is what gives a chatbot its intelligence. Instead of relying solely on preprogrammed responses, a conversational bot learns from user interactions. This means every conversation is an opportunity for the bot to refine its understanding and provide better answers in the future. When a user asks something the bot doesn’t know, like “What’s your favorite color?”—it might respond with “I don’t know.” But if you train it with the answer “Blue,” it stores this information. The next time someone asks the same question, the bot should remember and respond accurately: “My favorite color is Blue.”
Best match responses: A conversational chatbot doesn’t rely on exact matches between user queries and responses. Instead, it uses machine learning algorithms to find the closest match for any input, even if phrased differently. This flexibility is a game-changer for natural interactions. For example, when you ask, “What’s 2 + 2?” and then “Solve 2 plus 2,” the bot recognizes these as essentially the same question. Humans are unpredictable in how we phrase questions. A rigid bot would fail when faced with variations, but a conversational bot thrives on this variability. The bot can handle real-world conversations more effectively by focusing on meaning rather than exact wording.
Custom training: One size does not fit all, especially conversational AI. Custom training allows you to tailor the chatbot to specific domains or audiences, making it more effective for your unique use case. We can train the bot with custom datasets relevant to your domain. Think of this as training an employee. A new hire might know basic office skills, but if they’re in finance, you’ll train them to handle spreadsheets, budgets, and accounting tasks. Similarly, custom training equips your chatbot to excel in specific scenarios.

How to create a conversational chatbot

Building a conversational chatbot involves several core components, each crucial in making the bot interactive, dynamic, and intelligent. Let’s explore these building blocks step-by-step:

The first step in creating your chatbot is defining its personality, purpose, and processing capabilities. Naming the bot gives it a personal touch—our bot in this lesson will be called EduBot. Its brainpower comes from logic adapters, which select the most relevant response to user queries:

Python 3.10.4

# Function to create the chatbot
def create_bot(name):
    from chatterbot import ChatBot  # Import the ChatBot class from the ChatterBot library
    Bot = ChatBot(
        name=name,  # Set the name of the bot (e.g., EduBot)
        read_only=False,  # Allow the bot to learn from user input during conversations
        logic_adapters=["chatterbot.logic.BestMatch"],  # Use the BestMatch logic adapter for dynamic responses
        storage_adapter="chatterbot.storage.SQLStorageAdapter"  # Store data persistently in an SQLite database
    )
    return Bot  # Return the configured chatbot object

In the above code:

Line 7: To make the bot dynamic and adaptive, we set read_only = False, enabling it to learn and store responses during interactions, ensuring it evolves with every conversation.
Line 8: We’ll use ChatterBot’s BestMatch logic adapter to flexibly handle different inputs.
Line 9: The storage_adapter allows the bot to persistently store learned responses. In this case, we use the SQLStorageAdapter, which saves the data to an SQLite database, ensuring the bot retains what it has learned even after restarting.

Next, to give EduBot a solid foundation, we start by training it with a general dataset. But how do we train the chatbot? Preloaded datasets like ChatterBot’s English corpus respond to common topics such as greetings, basic knowledge, and small talk. This step equips the bot to handle generic queries, making it ready for broad interactions.

In the above code:

Lines 2–3: The ChatterBotCorpusTrainer provides preloaded datasets, such as greetings, small talk, and common phrases, for foundational training.
Line 4: The train("chatterbot.corpus.english") loads the English corpus, covering a wide range of conversational topics, to make the bot versatile. This step equips the bot with general knowledge, ensuring it can handle basic conversations like greetings or simple questions.

We know general training is useful, but what if our chatbot needs to specialize? Custom training allows us to teach EduBot specific question-answer pairs tailored to your needs. For instance, we can train it to answer organizational FAQs or respond with humorous quips. The custom_train function teaches the bot specific question-answer pairs, tailoring it to a particular domain or audience.

In the above code:

Lines 2–3: The ListTrainer is a trainer that uses Python lists as input. Each list element represents a part of a conversation.
Line 4: The conversation parameter is a list containing question-answer pairs.

Once created and trained, EduBot enters its most exciting phase: real-world interactions. The bot listens continuously for user input, generates responses dynamically, and remembers what it learns during the conversation. It also exits gracefully when users signal the end of the chat with phrases like "bye".

# Start and interact with the chatbot
def start_chatbot(Bot):
    print('\033c')  # Clear the terminal screen
    print("Hello, I am EduBot. How can I assist you today?")
    bye_list = ["bye", "goodbye", "exit"]  # Define exit phrases
    
    while True:
        user_input = input("You: ")  # Take user input
        if user_input.lower() in bye_list:  # Check if the user wants to exit
            print("EduBot: Goodbye! Have a great day!")
            break
        
        response = Bot.get_response(user_input)  # Generate a response
        print("EduBot:", response)  # Display the bot’s response

Running the chatbot

In the above code:

Line 3: This clears the terminal screen for a clean start.
Line 5: This is a predefined list of exit phrases; when matched, the bot ends the session gracefully.
Lines 7–11: These capture user input dynamically during the conversation.
Lines 13–14: The bot processes the user’s input and generates a relevant response using its logic adapters and training data.

This function powers real-time interactions, allowing users to dynamically test the bot’s training and behavior.

The execution flow of the chatbot

All the chatbot-related functionality, including creating, training, and running the chatbot, is housed in the functions.py file, while the main.py file serves as the entry point for execution. This separation of concerns is a widely accepted programming practice that enhances code readability, maintainability, and reusability. By isolating the logic in functions.py, we can make updates or add new features without modifying the execution script. Similarly, main.py focuses solely on orchestrating the workflow, keeping the implementation streamlined and easy to follow.

In the above code:

Line 1: This imports the functions defined in functions.py to access the chatbot’s logic.
Line 4: The chatbot is initialized with the name EduBot using create_bot, configured to dynamically learn and respond.
Line 7: It is trained with a general-purpose dataset via train_all_data to handle common queries.
Lines 10–14: Additionally, the bot is trained with custom data using custom_train, equipping it with domain-specific knowledge—for example, answering "What is Educative?" with "An Online Learning Platform!".
Line 17: Finally, start_chatbot launches the bot into an interactive mode where it listens, learns, and responds to user inputs in real-time.

This modular structure allows users to focus on customizing and testing their chatbot without diving into underlying complexities. Let’s go ahead and give it a try and witness how our chatbot works!

# function to create the chatbot
# we have the read_only to false so the chatbot learns from the user input as 
def create_bot(name):
    from chatterbot import ChatBot
    Bot = ChatBot(name = name,
                  read_only = False,                  
                  logic_adapters = ["chatterbot.logic.BestMatch"],                 
                  storage_adapter = "chatterbot.storage.SQLStorageAdapter")
    return Bot

# function to train the bot with a variety of topics
# the language we have chosen is english
# we can train the bot for other languages as well
def train_all_data(Bot):
    from chatterbot.trainers import ChatterBotCorpusTrainer
    corpus_trainer = ChatterBotCorpusTrainer(Bot)
    corpus_trainer.train("chatterbot.corpus.english")

# function to train the bot with custom data
# it uses ListTrainer to train data from lists 
def custom_train(Bot, conversation):
    from chatterbot.trainers import ListTrainer
    trainer = ListTrainer(Bot)
    trainer.train(conversation)

# function to start and take responses from the chatbot
# the chatbot stays running unless a word is typed from the bye_list 
def start_chatbot(Bot):
    print('\033c')
    print("Hello, I am Edubot. How can I help you")
    bye_list = ["bye edubot", "bye", "good bye"] 
    
    while (True):
        user_input = input("me: ")   
        if user_input.lower() in bye_list:
            print("Edubot: Good bye and have a blessed day!")
            break
        
        response = Bot.get_response(user_input)
        print("Edubot:", response)

AI chatbot

Is this chatbot truly intelligent?

Sometimes, chatting with our bot might feel like talking to a toddler who’s just learning to string sentences together—it tries, but occasionally, it spits out random gibberish that leaves you scratching your head. Ask it something unexpected, and you might get a response that’s as meaningful as a fortune cookie predicting tomorrow’s weather. It’s not perfect, but hey, it’s learning!

Our chatbot is not an advanced generative AI system. It lacks context awareness, meaning it won’t grasp deeper nuances or handle complex queries like “What’s the weather like in Paris?” unless explicitly trained for such scenarios. Its learning is also quite naive; the bot memorizes exact inputs and outputs without the ability to generalize or infer beyond its training. Furthermore, its understanding is limited—it processes input as simple strings and patterns, missing the reasoning capabilities found in cutting-edge large language models (LLMs) like GPT.

However, despite its limitations, this chatbot serves as an excellent foundational tool for learning conversational AI. It helps demystify core concepts such as training datasets, response generation, and dynamic learning. Moreover, its customizability allows you to tailor it to specific domains, whether for FAQs, educational tools, or simple customer service bots. Finally, its low complexity makes it accessible—unlike LLMs, it doesn’t require heavy computational resources or complex setups, enabling you to focus on understanding the fundamentals without external dependencies.

Interesting fact: The Uncanny Valley is a phenomenon where a humanoid object appears almost, but not exactly, like a real human, causing a sense of unease. Developers aim to design chatbots that are friendly and relatable without triggering this effect.

As you advance, these basics will serve as stepping stones to more complex and intelligent chatbot systems. Ready to take the next step? Let’s keep building!