Chatbots with Generative AI Models

Project Overview

This project aims at the development of the latest generative AI-powered chatbots. These chatbots are capable of having conversations with a user like a real human assistant using GPT-3.5-turbo and GPT-4. In this project, you will learn everything from the code setup to using OpenAI API for creating the chatbot.

You'll learn:

• How to prepare the code and execute it either on one’s own machine or by using online servers such as Colab.
• The crucial Python libraries for creating AI-powered bots.
• How to use AI technology to provide intelligent responsive feedback.

By the end, you will not just learn the workings of these bots but you will also know how they can be applied practically in sectors like corporate customer care, education, and others.

Prerequisites

Let’s ensure you're all set before we start this exciting process of creating chatbots! Do not worry, it is all easy, but having some prerequisites in preparation will help everything to work efficiently.

• Basic Knowledge of Python Programming: You do not have to be a coding master, but it would be better if you could be able to write python commands.
• Understanding of Concept Of APIs: Understanding of how API’s work to communicate with AI Models.
• OpenAI API Key: We need an API key to connect with GPT-3.5 and GPT-4. But it is easy to get.
• Google Colab or Local Python Setup: This project is based on Google Colab. This means you are able to code in your internet browser. You can also set up the environment locally.

Approach

In this project, we take a simple yet very effective method to build an AI chatbot. Then, we dive into generative AI models like GPT-3.5 turbo and GPT-4 that power your natural conversations. Then, we set up the coding environment on Google Colab for the browser-based coding. We then set up the OpenAI API. It will allow our chatbot to respond back smartly and humanly.

Now that it's set up and ready to roll, we start building the chatbot by writing code for how it will interact with users and respond to human input. Last, we test the chatbot, iterating on its response to make sure it sounds like a real conversation.

Workflow and Methodology

Workflow

• Prepare the Environment: All the required libraries to run the program should be installed. Also, configure the OpenAI API credentials necessary for accessing the GPT models.
• Prepare the Bot: Write the code to get started with the chatbot. So that the bot will greet and take user inputs and give responses using GPT-3.5-turbo or GPT-4.
• Generate Response: A user asks a question to the chatbot. Then the chatbot processes it. Then generate responses using models.
• Conversation loops: The loop makes the bot go on continuous conversations until the user ends the conversation.
• Test: Chat with the bot, asking different questions. Adjust the code to generate a correct response. Experiment with parameters such as temperature and max tokens.
• Deployment: If everything is working as expected. Then it is time to make the bot available for use, allowing interactions with real users!

Methodology

The methodology of this project is to build an AI chatbot as simple and effective as possible. Initially, we import and install the needed Python libraries such as OpenAi. So that we’ll be able to take advantage of GPT-3.5-turbo and GPT-4 models. We next set up the OpenAI API, plugging in the API key. It provides us with access to the chatbot’s language abilities.

After all that is done, we create a function that takes user inputs and passes them to the GPT model via the OpenAI API. We take inputs from people who are using the model and process that through the model to generate human-like responses that are later displayed back to the user. It will keep on chatting with the user in a loop until the user wants to quit.

Finally, we evaluate our chatbot’s performance by playing with parameters such as the max length of the response (max tokens), and creativity (temperature) to find a good balance between informative and natural-sounding responses.

Data Collection and Preparation

Data Collection Workflow

The objective of this project is to obtain data interactively as the users engage. Here is how it works:

• User Input: Users give their questions to the chatbot during a live discussion.
• Send Input to GPT Models: Each input is routed directly to the GPT-3.5-turbo or GPT-4 models via the OpenAI API.
• No Data Storage for Training: Since the generative AI models are employed in the project, there is no need to gather or keep the data for retraining purposes.

Data preparation workflow

This project focuses mainly on interacting in real time with GPT models hence not much data preparation is needed. The chatbot keeps tracking throughout the talk what the users say. GPT models that we use in this project have already been trained, so it doesn’t require us to clean and organize data by hand.

Code Explanation

STEP 1:

Import and install necessary libraries

With this command, we install OpenAI library which enables us to work with the GPT models. After installation, this library can be used for the purpose of sending requests to OpenAI API and receiving responses generated by models.

!pip install openai

This snippet of code imports the necessary libraries for the project. In this case, os lets you communicate with your environment, while openai provides access to the OpenAI API. To ease the process of using AI models and API, the OpenAI class is imported.

import os
import openai
from openai import OpenAI

STEP 2:

Configure API key

This segment of code initializes your OpenAI API key, which allows you to use GPT models including GPT-3.5-turbo and GPT-4 without any restrictions.
To obtain your API key, you can do the following:

• Create an Account on OpenAI: Visit the OpenAI website first to create an account.
• Get Your API Key: After you log into the account you created, go to the dashboard. On the API section there, you can create new API keys. You must copy it immediately after creating the key since it is the only time you will get to see that key again.
• Make Openai Key as Environment Variable: For safety reasons, Most people tend to use an environment variable to store their OpenAI API keys which this code does by using os.environ.get(“OPENAI_API_KEY”). You can do this in your terminal or environment manager.

In the code (like in the client object), you may choose to assign your API key directly but it is better to conceal the api key by saving it in an environment variable to prevent it from being accessed in public domain code repositories

# Replace with your OpenAI API key
openai.api_key = os.environ.get("OPENAI_API_KEY")
client = OpenAI(api_key='sk-Tubj3Vw0akvUJBa2lbMjTBlbkFJvpnY3gQZomOyBfA1Kf57')

STEP 3:

Generating Real-Time API Responses with GPT-3.5-turbo

The generate_api_validation_tests function is used to optimize chatbot conversation using the GTP-3.5-turbo model. The model accepts the input and generates output based on its system message “You are a helpful Chatbot assistant,” which serves as a guideline for what the system should do. Max_tokens is used to limit the size of the response returned. Temperature is used to determine the creativity and predictability of the generated response. Finally, the function displays the generated response to the user.

def generate_api_validation_tests(response, max_tokens=20, temperature=0.7):
    response = client.chat.completions.create(
        model = "gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful Chatbot assistant ."},
            {"role": "user", "content": response}  # This line will use the provided response
        ],
        max_tokens=max_tokens,
        temperature=temperature
    )
    return response.choices[0].message.content

Conversation in loops for GPT-3.5-turbo

This section of the code initiates an interactive loop, allowing both the chatbot and the user to engage in a continuous dialogue. It begins by prompting the user for a query through the input() command. Next, the user’s query is sent to the generate_api_validation_tests function for processing. This function issues a command to either GPT-4 or GPT-3.5-turbo, which generates a response. The response is then printed out to display the answer to the user.

The bot asks whether the user would like to continue the conversation after each response. If the response is yes, the loop continues. This allows more questions and answers. If the user provides any other input, the loop terminates, and the conversation ends.