Robert Johns | 09 Nov, 2023

Learn To Build A Python Chatbot In 24 Hours

Do you want to build an AI-powered Python chatbot? How about creating a Python chatbot with the same tools that OpenAI uses to power ChatGPT? Better yet, why not add machine learning into the mix to create a Python chatbot that uses your contextual data to generate responses?

That’s why I created a 24-Hour chatbot course to help you build a Python chatbot with GPT-3.5. 

The 24-Hour Chatbot: Python Course

Because of the early stage of the technology, there are very few helpful chatbot tutorials. That's one reason I created this course. Better still, I want to teach students how to build from zero to AI hero in 24 hours or less. 

This year introduced unprecedented growth in AI tools. And, as a developer, you must understand how to leverage AI and machine learning in the workplace. This chatbot tutorial gives you exactly the tools you need to enhance your resume. Specifically, it provides a portfolio-worthy project to include either as a student or in a real workplace environment.




In this Python chatbot course, we’ll be building an AI-powered chatbot by Python, machine learning, vector embeddings, Pandas, NumPy, and of course, the OpenAI Python library and API. That’s right, these are the same tools that power ChatGPT.

And what’s really cool about this Python chatbot course is the use of our own contextual data. Learn from my expertise to understand how to introduce a contextual data source into your code. That way, the Python chatbot can pull from your proprietary data when it answers questions.

That’s right, your chatbot will include your own data in its responses!

If any of this sounds brand new to you, don’t worry! I’m going to cover all of the essentials you need to get up and running, including setting up API access with OpenAI and installing all of the dependencies you need to build a Python chatbot in only 24 hours.

Python Chatbot Course Structure

This Python chatbot course is, by definition, hands-on. That said, before we fire up our PyCharm IDE to start coding this Python project, we’ll have a few preliminary steps to follow.

To give you a better idea of what to expect, let’s break down the course structure:

We’ll start with an introduction to the fundamental concepts of AI chatbots, including:

  • OpenAI Essentials: I’ll cover everything you need to know about the OpenAI API, the various GPT large language models, and the OpenAI Python library. 
  • Machine Learning Essentials: I’ll summarize the core concepts we’ll use, including cosine similarity, cosine distance, and vector embeddings.
  • Additional Resources: I’ll provide a range of resources you can use to learn more about the various concepts and techniques we’ll be using, including the Linux command line, OpenAI docs, machine learning fundamentals, Pandas, and NumPy

Once we have that covered, I’ll show you how to get up and running with OpenAI tools:

    • OpenAI API: I’ll walk you through how to set up an account, create an API key, and securely store this on your computer
    • OpenAI Python Library: Among the other Python dependencies you’ll need to build a Python chatbot, I’ll walk you through how to install the OpenAI Python library

Finally, we’ll be ready to fire up PyCharm and build our Python chatbot. I’ll walk you through every stage of the coding process by explaining what we’ll be doing and why we’re doing it.

Course Materials

In this Python chatbot course, I’ve placed an emphasis on getting you up and running fast! 

To help you do that, I’ve put together a range of course materials:

  • Course slides to summarize key machine learning concepts, including further resources
  • Course slides to cover API setup, security, and installing Python dependencies 
  • Detailed walkthrough videos for setting up API access, security, and Python libraries
  • Complete source code for the Python chatbot, including the dependency file
  • Detailed walkthrough videos for how to create a Python chatbot

About Dr. Johns

Dr. Johns earned his data experience in blast engineering and advanced structural engineering. His two PhDs come from the University of Southampton, where his research assisted the Ministry of Defence (MOD). 

His work resulted in the development and validation of scaling methods and the refinement of threat models. With extensive experience in research and data analysis, Dr. Johns implemented computational modeling via CFD and FEA and used statistical production of confidence intervals to analyze and interpret data.

With his courses, including the 24-Hour Chatbot: How to Build a Chatbot with GPT-3.5, he teaches students with academic techniques not often found in an online course.

What Is A Chatbot?

Great question! A chatbot is a computer program that’s been designed to simulate human conversation through either text prompts or voice interactions. 

In 2024, chatbots leverage AI tools like natural language processing (NLP) and machine learning to understand and respond to a user’s questions or commands. Importantly, they do this in a conversational way, which is how they simulate a real person.

Perhaps the most famous chatbot at this point is OpenAI’s ChatGPT (which is no doubt one of the reasons you’re curious about this course!). That said, chatbots can be found in various applications, including messenger apps, websites, mobile apps, and voice assistants like Apple’s Siri.

If you want to get really granular, chatbots can be classified as either rule-based or AI-powered. With a rule-based chatbot, they simply use a predefined set of rules and patterns to generate responses, meaning they are limited to the information in their knowledge base.

In contrast, AI-powered chatbots use machine learning algorithms to analyze and understand a user’s input while continuously learning from interactions and improving responses over time. This is what we’ll be building in this Python chatbot course.

Limitations With A Chatbot

While AI-powered chatbots like ChatGPT and Google Bard are tremendously impressive, certain limitations can affect their performance:

  • Lack of context: Chatbots can struggle with context, meaning that you’ll need to be very specific to prevent misunderstandings, and this often requires prompt engineering.
  • Domain knowledge: If a chatbot is trained for a specific domain, such as programming, it will be unable to provide meaningful responses in areas like baking or the weather.
  • Difficulties with complex queries: Chatbots can struggle with complex or ambiguous queries, leading to inaccurate or irrelevant responses.
  • Lack of empathy: Perhaps this will change in the future, but chatbots lack emotional intelligence, so they cannot understand or respond to human emotions.
  • Error propagation: This is one of the biggest issues with AI-powered chatbots, as they’re only as useful as the information they are trained on. Garbage In, Garbage Out.
  • Lack of creativity: Chatbots rely on learned patterns, so they can struggle with generating creative or innovative solutions to problems outside of their capabilities.

How Does A Chatbot Work?

In general, chatbots use a combination of technologies and algorithms to understand and respond to user inputs, which we tend to call prompts.

Let’s take a look at a generalized overview of how a text-based chatbot works:

  1. Input processing: A user sends a prompt (message or command) to the chatbot, and the input is converted to a format that the chatbot can understand.
  2. Natural Language Understanding (NLU): The chatbot uses NLP techniques to break the input into words and phrases, which helps it to understand the user's intent.
  3. Intent recognition: The chatbot determines the user's intent by the input with predefined intents it’s been trained on.
  4. Entity extraction: Alongside intent, the chatbot identifies specific pieces of information called entities to represent relevant parameters or details from the input. 
  5. Dialog management: The chatbot determines an appropriate response or action to take based on the intent and entities.
  6. Response generation: The chatbot generates a response to return to the user, whether that’s a predefined or dynamically generated message.
  7. User interaction: The response is returned to the user, completing an iteration of the conversation. The chatbot then waits for the next input to continue the conversation.

What To Know Before Building A Python Chatbot

Prerequisite Python Skills

If you want to take this Python chatbot course, you should be familiar with the following:

  • Variable assignment
  • String manipulation
  • Built-in data structures like lists, tuples, sets, and dictionaries
  • Control structures via conditionals and iteration
  • User-defined functions
  • Python Standard Library
  • File handling, including reading in and writing out
  • Error handling via exceptions and try-except blocks
  • Basic input/output at the console/terminal
  • Importing libraries and modules

If you’re not completely comfortable with these Python skills, I’d recommend checking out my in-depth Python masterclass, Python with Dr. Johns

In this Python masterclass, I take an academic approach to teaching these Python subjects in great detail, with slides, video walkthroughs, and source code. The masterclass also includes the 24-hour Python Chatbot course, so you get the best of both worlds! 

In addition to the Python skills above, we’re going to be using machine learning techniques, data handling with Pandas and NumPy, and API calls to build a Python chatbot. 

That said, I’ll be assuming you already understand or have the following skills:

  • Command line skills (intermediate)
  • Making API calls to a third-party service (OpenAI)
  • Machine Learning skills like vector embeddings and cosine similarity to analyze data
  • Data handling and manipulation skills with Pandas and NumPy

Important: you don’t need to be an expert in any of these areas to take this Python chatbot course, and you can also use this chatbot project to learn these skills in a practical setting.

Necessary Hardware

The nice thing about this Python chatbot course is that you’ll only need a laptop or desktop PC with an internet connection to communicate with the OpenAI API.

Of course, you’ll also need to have a stable version of Python 3 installed (I recommend 3.8+) and a Python IDE or code editor. If you want to emulate my setup, I’ll be using PyCharm.

Required Accounts For Chatbot API Integration

To take advantage of the OpenAI API, you’ll need to register for an account with OpenAI. You’ll then be able to generate an API key, which your computer will use to authenticate itself with the OpenAI API service.

If any of this sounds confusing, don’t worry because I’ll be walking you through these steps for creating an account and generating an API key in the course. 

Ethical Considerations Of A Python Chatbot

Creating a Python chatbot can raise several ethical considerations that you need to bear in mind during both the development and deployment process:

  • Privacy and Data Protection: You must ensure your chatbot collects and stores user data in a secure and responsible manner. You’ll also need to inform users about any data being collected and how it will be used.
  • Transparency and Disclosure: You need to disclose to users that they are interacting with a chatbot and not a human and also be transparent about the chatbot's capabilities, limitations, and how user data will be processed.
  • Bias and Fairness: Implement measures to minimize biases in the chatbot's responses and ensure fair treatment of all users.
  • User Consent and Control: You have to obtain user consent before collecting and using any data, and also give users control over the data they share by allowing them to opt out or delete data.
  • User Safety and Well-being: Design your chatbot to prioritize user safety and well-being and implement safeguards to prevent the dissemination of any inappropriate content or the promotion of harmful activities.
  • Accuracy and Reliability: Clearly communicate the limitations of the chatbot's knowledge and indicate when any information that’s provided may be uncertain.
  • User Support and Escalation: Offer obvious and clear ways for users to seek human assistance or escalate issues when needed.
  • Continuous Monitoring and Improvement: Regularly monitor and evaluate your chatbot's performance to identify and fix any ethical concerns that arise. This can be helped by engaging with users to gather feedback.

Why Build A Chatbot For Your Python Portfolio?

Many would agree that 2024 has been the year of AI, with AI-powered chatbots like ChatGPT seeing 100 million active monthly users.

And with the OpenAI API and Python library, organizations of all shapes and sizes can leverage the same tools that power ChatGPT to build their own AI-powered chatbots.

So whether you want to impress your current or future employer, building a Python chatbot is an excellent way to level up your portfolio and demonstrate your advanced Python skills in an area that will only grow in demand. 

That said, let’s look at some more reasons to build a Python chatbot for your portfolio:

  • Highlights your AI, NLP, and ML expertise
  • Demonstrates the practical application of Python libraries and frameworks
  • Showcases your communication and user experience skills
  • A great way to spark interesting and unique conversations at an interview

10 Benefits Of A Python Chatbot

  1. 24/7 availability: Chatbots don’t log off, so they’re always online to support users.
  2. Faster response: Chatbots provide instant responses, reducing response times versus traditional customer support.
  3. Increased efficiency: Chatbots free up human resources to focus on complex or critical tasks by automating repetitive and routine tasks.
  4. Continuous improvement: AI-powered chatbots can learn from user interactions and improve their responses.
  5. Cost-effective: Chatbots can handle huge volumes of interactions for very little cost after they’re developed and deployed.
  6. Scalability: Chatbots can handle multiple conversations asynchronously and simultaneously.
  7. Data collection and analytics: Chatbots can collect data during user interactions, which you can use for analytics and insights.
  8. Platform integration: It’s easy to integrate chatbots with various platforms, including websites, messaging apps, and more.
  9. Better customer service: Chatbots provide quick and accurate responses to frequently asked questions, which helps to resolve common issues instantly.
  10. User engagement: Chatbots can use data and user profiles to deliver a personalized experience, including custom recommendations or suggestions.

How To Discuss A Chatbot During An Interview

When the time comes to discuss your Python chatbot during an interview, here are some tips to feel ready for whatever might come up:

  • Summarize your chatbot: Be ready to give a brief overview of your Python chatbot, including its purpose, target audience, and the problem it aims to solve. Be sure to highlight the use of your own contextual data via machine learning.
  • Technical implementation: You need to understand the technical aspects of your Python chatbot from front to back, so be ready to explain how you used the OpenAI API, which GPT model you used and why, and why you needed to use vector embeddings and machine learning techniques via Pandas and NumPy. 
  • Highlight key functionality: Be ready to explain how your chatbot uses machine learning to provide contextual answers from your own data set to answer user queries. 
  • Design considerations: Be prepared to discuss why you used Python because of the excellent OpenAI library or chose a particular GPT model based on price and token count considerations.
  • Challenges and solutions: Be ready to discuss any challenges or obstacles you encountered during development. For example, how did you handle user queries that did not match up with your contextual data? Also,  be honest about skill gaps and how you overcame them.
  • User feedback and iteration: If applicable to your use case, discuss how you incorporated user feedback to improve your chatbot.
  • User data and analytics: If you’ve included some form of user data collection, discuss how you plan to use this to help with business intelligence insights.
  • Lessons learned and future enhancements: Be ready to reflect on any lessons you learned while developing a Python chatbot and discuss improvements you want to make or new features you’d like to add.

Also, remember to customize your discussions and answers to fit your interviewer's technical level. This means you might need to adjust the depth of your answers, but regardless, be sure to be confident, articulate, and enthusiastic about your Python chatbot project.

The 24-Hour Chatbot Tutorial with Dr. JohnsPython Chatbot Tutorial: How to Build a Chatbot in Python

When you’re ready to get started with our Python chatbot tutorial, here are the steps we’ll be taking. The goal is simple. We plan to build our own AI-powered chatbot in 24 hours or less. So how do we get started? We will follow these steps:

  1. Generate an OpenAI API Key and securely store it on your machine
  2. Install Python dependencies, including OpenAI Python Library
  3. Gather contextual data for our Python chatbot to use for generating responses
  4. Clean and prepare contextual data for tokenization
  5. Analyze token counts to choose appropriate GPT models
  6. Use OpenAI library with machine learning to generate embeddings from contextual data 
  7. Code our Python chatbot to gather user queries and format data for ingestion
  8. Utilize machine learning to query embeddings and find data for a user response
  9. Code our Python chatbot to produce a user-friendly response for the user query
  10. Test our chatbot for accuracy and appropriateness of the AI response

Benefits Of A Python Chatbot Course

If you’ve read through the benefits of an AI-powered chatbot and you’re ready to reap the rewards of this innovative technology, let’s discuss the benefits of our Python chatbot course:

  1. Saves time: You don’t need to find an external developer capable of creating a chatbot, as you can build one yourself in 24 hours.  
  2. Saves money: Rather than spending money on an external developer, you can invest in yourself or your own developer team. This can also save money on maintenance, as you’ll understand the inner workings of the end product.
  3. Gives you marketable skills: By learning this valuable skill, you can sell your services to other people that want an AI-powered chatbot but are unable to build it themselves

Python Chatbot Summary

By taking our course, you’ll learn how to build a Python chatbot by blending machine learning, vector embeddings, Pandas, NumPy, and the OpenAI Python library and API. 

This means you’ll learn how to use the same tools that power the hugely successful ChatGPT. 

And the cool thing about our course is that we’ll be designing a Python chatbot to use our own contextual data to answer user queries and questions.

You just need the fundamentals of Python, and I’ll cover everything else you need to build a Python chatbot in only 24 hours.

Let’s get started!

Enroll in “The 24-Hour Chatbot” Course


Python Chatbot FAQs

1. What Is An NLP Chatbot?

An NLP chatbot is an AI-powered conversational tool that uses Natural Language Processing techniques to understand and respond to user queries in a human-like way.

2. How Long Does It Take To Build A Chatbot With Python?

By taking our Python chatbot course, you can build a chatbot with Python in 24 hours or less.

3. Where Can I Learn To Build A Chatbot?

If you’re not sure where to start, we’d highly recommend our Python chatbot course as we’ll be blending machine learning with the OpenAI API to build an AI-powered chatbot in 24 hours. We’ll also design our chatbot to use our contextual data to generate responses.

4. Can I Train The Chatbot On My Own Data?

You can fine-tune your chatbot with your own data by selecting the appropriate model from the OpenAI family of GPT models. You can also design a chatbot to use your contextual data to generate responses, which is the approach we’ve taken in our Python chatbot course.

5. Why Do Most Chatbots Fail?

One of the major challenges with creating a chatbot is the accuracy of the responses generated from a user query, hence why many chatbots do not succeed. In contrast, one of the primary reasons that ChatGPT has been so successful is its impressive accuracy levels, which is why we’re using the OpenAI API to handle the AI aspect of our chatbot.

By Robert Johns

Technical Editor for | 15+ Years in Python, Java, SQL, C++, C#, JavaScript, Ruby, PHP, .NET, MATLAB, HTML & CSS, and more... 10+ Years in Networking, Cloud, APIs, Linux | 5+ Years in Data Science | 2x PhDs in Structural & Blast Engineering

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