Mastering the OpenAI Library in Python

Introduction

Large Language Models (LLMs) is an emerging technolgy that has started to gain popularity and traction with developers and tech industry. Among the most prominent resource when programming in Python is the OpenAI library. This article aims to provide a guide into the OpenAI library in Python for beginners.

How to Use the OpenAI Python Library

Whether you are a seasoned developer or just beginning your journey into machine learning, the OpenAI Python library offers an approachable entry point. Here’s what is covered in this lesson:

• Choosing the Right GPT Model: Different Generative Pre-trained Transformer (GPT) models serve various purposes. Selecting the appropriate model is essential for your specific application.
• Parameter Tuning of Models: Tuning of parameters can significantly alter the model’s performance and output. It’s important to understand the effect of these parameters such that you can tailor the output for needs.
• Dealing with Conversation Memory: Managing conversation memory is vital for maintaining context and coherence in dialogue systems.
• Creating a ChatGPT Clone with a Custom Persona: With the OpenAI library, you can even create a personalized ChatGPT clone that reflects a specific persona.

OpenAI vs. LangChain

A comparison between the OpenAI library and LangChain reveals some key differences:

OpenAI Library:

• Less abstraction, easier to understand
• Less powerful than LangChain
• No out-of-box solutions for techniques such as RetrievalQA and Agents
• Viable for simple Language Model as a Language Model (LLM) projects

OpenAI API Reference

LangChain:

• Popular amongst LLM developers
• Lots of features are abstracted, making it harder to understand
• Great for streamlining complex LLM applications

LangChain Python Documentation

Model Parameters

Utilizing OpenAI models effectively requires a good understanding of the parameters that govern their behavior. Here are some key parameters to consider:

• Model: Choose the GPT model that best suits your needs. Different models have varying capabilities and requirements.
• Temperature: This parameter controls the randomness of text generation. A higher temperature value leads to more random output, while a lower value makes the model more deterministic.
• Top_p: An alternative to temperature, top_p controls the diversity of predicted token responses. For example, a value of 0.1 means drawing from the top 10% of all most likely token outputs.
• Max_tokens: This parameter limits the maximum tokens per API call, allowing you to control the length of the generated text.

You can experiment with these parameters in the OpenAI Playground, and further details about other parameters are available in the original documentation.

For more insights on controlling temperature and top_p, refer to this comprehensive guide.

Which Model to Choose?

Selecting the right model is crucial to achieving optimal performance while balancing cost. Here’s a summary of the differences between the available OpenAI GPT family of models:

• GPT-4 vs. GPT-3.5: GPT-4 performs better but is slower and costs about 20x more than GPT-3.5.
• 0613 Versions: These are models frozen as of June 13th, 2023, with function calling capabilities.
• Larger token limits: There are 16k or 32k variants of the base model that allow for larger token limits. This is great for long text prompts, however it costs 2x more than base models.
• Common Choices: Most of the time, gpt-3.5-turbo is the preferred choice since it is the cheapest, while other variants can be used when it is not sufficient.
• Deprecated Models: Note that text-davinci-003 will be deprecated. It costs 10x more than gpt-3.5-turbo but has identical performance.

More details on model selection and other OpenAI models can be found here.

Message Format

Understanding the message format is essential for effective communication with the model. The ‘role’ can be either “system”, “user”, or “assistant”, and the ‘content’ will contain the actual text of the message.

• System: A message that goes at the beginning of the prompt, used to steer the behavior of the model, such as setting personality or focusing the conversation.
• User: Typically instructions or questions from the user.
• Assistant: The model’s response to the user’s input.

Explore further and try different models in the OpenAI Playground.

Code Notebook

The notebook for this lesson is available here: Google Colab

There are some exercises at the end of the notebook for you to try. Feel free to copy this notebook and make modifications to it. If you have any questions about the code or the content of this article, feel free to reach me at johnfengphd@gmail.com.

And that concludes our lesson on the OpenAI library in Python! Whether you’re looking to build a ChatGPT clone, optimize model parameters, or understand the various models available, this guide provides a solid foundation. Happy coding!