Supercharge Your Chatbot with GPT 3.5 Turbo OpenAI API

Table of Contents:

1. Introduction
2. Requirements
3. Setting up the Environment
4. Implementing the Token Count
5. Looping through User Input
6. Creating the Completion
7. Checking Completion Token Limit
8. Removing Earlier Messages
9. Printing Tokens and Messages
10. Conclusion

Introduction:

In this article, we will explore how to use OpenAI's new chat completions with GPT 3.5 turbo. We will start with a demo that demonstrates the removal of earlier messages when the token count reaches a certain level. We will provide step-by-step instructions on how to implement this feature and highlight the requirements for using GPT 3.5 turbo.

Requirements:

To follow along with this tutorial, You will need to have the following requirements:

1. OpenAI API Key
2. pip (Python Package installer)
3. OpenAI and PPSolve packages installed

Setting up the Environment:

Before we dive into the implementation, we need to set up our environment. This involves installing the necessary dependencies and importing the required libraries, such as the OpenAI token and os.

Implementing the Token Count:

To keep track of the token count, we will define the maximum number of tokens allowed for the GPT 3.5 turbo model. We will also set the encoding for tick tokens and define the mixed tokens response.

Looping through User Input:

To continuously Interact with the OpenAI model, we will run a loop that Prompts the user for input. We will append the user's input to the original message and send it to the OpenAI API.

Creating the Completion:

Using the GPT 3.5 turbo model, we will Create a completion by setting the mixed tokens and messages. We will then receive the completion and append it to the messages.

Checking Completion Token Limit:

Before proceeding further, we will check the token limit of the completion. If the token count exceeds a certain threshold, we will remove earlier messages to ensure the response stays within the token limit.

Removing Earlier Messages:

To remove earlier messages, we will iterate through the messages list and remove them one by one until the token count is under the allowed limit. We will keep track of the removed tokens and print the remaining messages.

Printing Tokens and Messages:

To keep track of token usage and message content, we will print the total tokens used and the messages after token removal.

Conclusion:

In this article, we have learned how to use OpenAI's chat completions with GPT 3.5 turbo. We have implemented a feature to remove earlier messages when the token count reaches its limit, ensuring the model stays within the allowed token range. Feel free to adapt this code to your specific use case.

Now, let's dive into the implementation in Detail.

Implementing OpenAI's New Chat Completions with GPT 3.5 Turbo

GPT 3.5 turbo is an advanced language model developed by OpenAI. It allows users to interact with the model through a chat interface, making it easier to generate conversational responses. In this tutorial, we will explore how to use GPT 3.5 turbo's chat completions and implement a feature to remove earlier messages when the token count exceeds a certain limit.

1. Introduction

The introduction provides an overview of the tutorial and explains the purpose of using GPT 3.5 turbo's chat completions.

2. Requirements

The requirements section lists the prerequisites for following the tutorial, including having an OpenAI API key and installing the necessary dependencies.

3. Setting up the Environment

Before implementing the code, it is essential to set up the environment by installing the required packages and importing the Relevant libraries.

4. Implementing the Token Count

In this section, we define the maximum token count allowed for the GPT 3.5 turbo model and set the encoding for tick tokens and mixed tokens. This enables us to keep track of the token count in the conversation.

5. Looping through User Input

To interact with the GPT 3.5 turbo model continuously, we run a loop that prompts the user for input. The user's input is then appended to the original message and sent to the OpenAI API.

6. Creating the Completion

Using the GPT 3.5 turbo model, we create a completion by setting the mixed tokens and messages. The completion received from the OpenAI API is then appended to the messages list.

7. Checking Completion Token Limit

To ensure the response remains within the token limit, we check if the token count exceeds a certain threshold. If it does, we remove earlier messages until the token count is under the allowed limit.

8. Removing Earlier Messages

In this step, we iterate through the messages list and remove earlier messages one by one until the token count is within the specified range. We keep track of the removed tokens and print the remaining messages.

9. Printing Tokens and Messages

To monitor token usage and message content, we print the total tokens used and the messages after token removal. This allows us to verify the accuracy of our implementation.

10. Conclusion

In the conclusion section, we summarize the tutorial and highlight the importance of adapting the provided code to specific use cases. We also mention additional resources for further exploration of GPT 3.5 turbo's capabilities.

By following this tutorial, you will gain a better understanding of how to use OpenAI's chat completions with GPT 3.5 turbo and implement custom features like removing earlier messages Based on token count.

Highlights:

• Learn how to use OpenAI's chat completions with GPT 3.5 turbo.
• Implement a feature to remove earlier messages when the token count reaches a certain limit.
• Set up the environment by installing the necessary dependencies and importing libraries.
• Interact with the GPT 3.5 turbo model using a loop to continuously ask questions.
• Create completions based on user input and append them to the messages list.
• Check the completion token limit and remove earlier messages as needed.
• Print the token count and message content to monitor usage and ensure accurate results.

FAQ:

Q: What is GPT 3.5 turbo? A: GPT 3.5 turbo is an advanced language model developed by OpenAI. It allows users to interact with the model through a chat interface.

Q: What is the AdVantage of using chat completions? A: Chat completions make it easier to generate conversational responses and have interactive conversations with the model.

Q: How can I use chat completions effectively? A: By implementing features like removing earlier messages based on token count, you can ensure the conversation stays within the allowed token range and provides accurate responses.

Q: What are the requirements for using GPT 3.5 turbo? A: The requirements include having an OpenAI API key and installing the necessary packages, such as OpenAI and PPSolve.

Q: Can I customize the system message in the chat interface? A: Yes, you can customize the system message according to your preferences to provide a personalized experience to users.

Q: Where can I find the code for this implementation? A: The code will be available in the tutorial description and can also be accessed through the author's Patreon page for supporters.

Q: How can I adapt this code to my specific use case? A: Feel free to modify the code according to your requirements and integrate it into your own projects.