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Chatbots with Generative AI Models

In this project, we look into the growing field of robots that use generative AI models. We specifically look at how to build and use advanced language models like GPT-3.5-turbo and GPT-4. By looking closely at how important language models are in chatbot creation, the project shows how they can help create interactions that feel more like talking to a person and speed up processes in many areas. The goal of the project is to make generative AI less mysterious by giving readers a step-by-step guide on how to use these models to build chatbots, along with code examples and a way to interact with the models. In addition, by talking about what these improvements mean for the future of chatbot technology, the project shows how generative AI has changed conversational bots and looks forward to more breakthroughs in the area.

Explanation All Code

Step 1:

Install required packages

!pip install openai

Step 2:

Import required packages

import os
import openai
from openai import OpenAI

You're probably trying to talk to the GPT-3 model through the OpenAI API. Unfortunately, it looks like you haven't given your API key or imported the appropriate libraries. Getting started: Make sure you have your OpenAI API key and the "openai" library loaded. Then, change "OPENAI_API_KEY" to your personal API key. If you need more help, please don't hesitate to ask!


Create an OpenAI Account & API key

First, create an account on the OpenAI website by visiting openai.com and clicking "Sign Up". Follow the instructions and verify your email. After logging in, go to the API section by clicking the "API" tab or visiting this link.

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

Step 3:

Using GPT-3.5-turbo


Using the OpenAI API, it looks like you're trying to make a function that will make API validation tests. Your function 'generate_api_validation_tests' takes a response as input and uses the GPT-3.5-turbo model to make a completion. The response is added to a system message that tells the user they are talking to a helpful chatbot helper.

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

The code continuously interacts with the user by asking questions, generating responses using the `generate_api_validation_tests` function, and checking if the user wants to continue. The loop ends if the user does not respond with "yes."

# Start loop
while True:
    # Ask user for a question
    user_question = input("User: ")
    # Generate response based on user's question
    api_validation_tests = generate_api_validation_tests(user_question, max_tokens=20, temperature=0.5)
    print("Chatbot: ", api_validation_tests)
    # Ask the user if they want to continue
    continue_response = input("Continue conversation? (yes/no): ")
    if continue_response.lower() != "yes":
        break  # Exit the loop if the user does not want to continue

Using GPT-4 model


Using the OpenAI API, it looks like you're trying to make a function that will make API validation tests. Your function 'generate_api_validation_tests' takes a response as input and uses the GPT-4 model to make a completion. The response is added to a system message that tells the user they are talking to a helpful chatbot helper. Allow yourself to inquire if you have any concerns or require extra help with this feature.

def generate_api_validation_tests(response, max_tokens=20, temperature=0.7):
    response = client.chat.completions.create(
        model="gpt-4",
        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

Step 4:

This piece of code makes a loop where a robot can talk to the user. Inside the loop:


  • The person is asked to write a question.
  • Using the generate_api_validation_tests function, the robot comes up with an answer based on the user's question.
  • The chatbot's answer is shown on the screen.
  • The person is asked if they'd like to keep talking. If the person says "yes", the robot will continue the conversation with the person. If the person says "no", the loop ends.

The person using the robot can keep talking until the person using the robot chooses to end the conversation.

# Start loop
while True:
    # Ask user for a question
    user_question = input("User: ")
    # Generate response based on user's question
    api_validation_tests = generate_api_validation_tests(user_question, max_tokens=20, temperature=0.5)
    print("Chatbot: ", api_validation_tests)
    # Ask the user if they want to continue
    continue_response = input("Continue conversation? (yes/no): ")
    if continue_response.lower() != "yes":
        break  # Exit the loop if the user does not want to continue

Conclusion

They proved that chatbots can make conversation better and are useful in many situations. By creating a loop where users can interact with the robot, we showed how these kinds of systems can be used for healthcare, education, virtual help, customer service, and finding information. The ability to automate interactions and respond quickly makes the user experience better and makes real-life chores easier. Additionally, chatbot systems can be improved and developed further, which could lead to even more complex and useful uses in various fields and industries.
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