Create Your Own Chatbot - [Part - 1]

 

Introduction

Are you intrigued by the world of chatbots and their ability to streamline interactions? Imagine having your very own ChatGPT-like virtual assistant, capable of handling customer queries and simplifying processes. In this blog post, we'll explore how you can create your own chatbot using OpenAI, with a focus on a pizza orderbot as an example.

Chatbots have revolutionized customer interactions, providing efficient and scalable solutions. With OpenAI's advanced language models, you can develop a chatbot that understands natural language, automates responses, and offers personalized experiences.

Learn how to train your chatbot using OpenAI's API, integrate it seamlessly, and enhance customer satisfaction. Unleash your creativity and embark on the journey of creating your own chatbot with OpenAI, using the pizza orderbot as an inspiring illustration.

Let's dive in and unlock the potential of chatbot creation with OpenAI!

Building a Chatbot: Pizza Orderbot Example

In this section, we'll dive into the step-by-step process of building a chatbot using the pizza orderbot as our guiding example. Through Python and Jupyter Notebook, we'll learn how to train the chatbot using OpenAI's API, integrate it seamlessly into an interface, and optimize its functionality for customer satisfaction. By following these steps, you'll gain a solid foundation in chatbot development, adaptable to your specific needs and applications.

1) Getting an API Key from Open AI
To kickstart our pizza orderbot project, we need to obtain an API key from OpenAI. This key grants us access to OpenAI's powerful language models, allowing us to incorporate their capabilities into our chatbot application. The process of acquiring the API key is straightforward and well-documented in the official OpenAI documentation.

2) Setting the API Key:
Once we have the API key, we'll install the necessary dependencies, including the OpenAI library by using below command.

pip install openai

By importing the library and assigning the API key to the openai.api_key variable, we ensure our code can access the OpenAI API and leverage its capabilities effectively with the appropriate authentication.

import openai

openai.api_key = '<YOUR API KEY>'

The code snippet provided illustrates this setup process, enabling you to proceed smoothly with the development of your chatbot. You can also store this API key in your environment variable and access it in the code, whichever way suits you.

3) Creating helper function:
Now let's create a helper function that generates chatbot responses based on user’s messages.

Here is the code snippet:

def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature, # This parameter controls the degree of randomness in the model's output.
    )
    return response.choices[0].message["content"]

The get_completion_from_messages function utilizes the OpenAI API to generate responses from the chatbot model. It takes three parameters:

• messages - a list of message objects containing user input and previous responses

• model - specifying the language model to use, such as "gpt-3.5-turbo"

• temperature - controlling the randomness of the generated responses. Experimenting with different temperature values allows you to control the chatbot's level of creativity and randomness.

The function makes an API call to openai.ChatCompletion.create, passing the model, messages, and temperature as parameters. The generated response is then extracted using response.choices[0].message["content"] and returned as the chatbot's reply.

4) Collecting and Displaying Chat Messages:
To enhance the user interface of your pizza orderbot, the provided code snippet demonstrates a function called collect_messages that collects and displays chat messages between the user and the assistant.

Here is the code snippet:

def collect_messages(_):

    prompt = inp.value_input
    inp.value = ''
    context.append({'role':'user', 'content':f"{prompt}"})
    response = get_completion_from_messages(context)
    context.append({'role':'assistant', 'content':f"{response}"})
    panels.append(
    pn.Row('User:', pn.pane.Markdown(prompt, width=600)))
    panels.append(
    pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))

    return pn.Column(*panels)




Here's an overview:
This function is triggered when a user input is received. The user's input, represented as a message object, is added to the context list with the role set to 'user'. The get_completion_from_messages function is called, passing the context as messages, to generate the assistant's response.
The assistant's response, also represented as a message object, is added to the context list with the role set to 'assistant'. The panels list is updated to display the user and assistant messages using the pn.Row and pn.pane.Markdown components. The Panel module is explained in next step.

To Be Continued...

In this part we have seen the how to get API key from openAI and use that to start building chatbot. We are ready with the setup. In the next part we will train the model and create our first chatbot.

Stay tuned!