About me

I am working in ABInBev as Senior Manager – Data Science with experience in Statistical/Machine Learning and Predictive Modelling and analytics consulting. With a passion for machine learning and data-driven solutions, I have been actively involved in the development and implementation of advanced analytics frameworks. I am master’s in computer science and have 12+ years of experience in conceptualization and delivery of actionable models across Finance, Media, Retail, Wholesale and Logistics industries.

Some published links about this topic

Blog post: Linkedin Post

Package url : PyAIKit

Objective

The objective of this presentation is to showcase PyAIKit, a Python package designed to simplify the usage of large language models (LLMs) like ChatGPT. This abstract highlights the purpose, value, outline, and possible use cases of PyAIKit, emphasizing its significance in making LLMs more accessible and user-friendly.

Purpose

The purpose of this presentation is to demonstrate how PyAIKit simplifies the utilization of LLMs, focusing on ChatGPT API. By leveraging PyAIKit’s functionalities and pre-built components, developers can easily incorporate LLMs into their projects, reducing complexity and unlocking the full potential of these advanced language models.

Outline/Discussion points

Introduction

Overview of the challenges and complexities associated with utilizing LLMs like ChatGPT through its API.

PyAIKit- Simplifying LLM Usage

Explanation of the key features, functionalities, and benefits of PyAIKit in the context of LLM integration.

Use Cases

Exploration of possible use cases where PyAIKit can be leveraged with ChatGPT API, including:
a. Sentiment Analysis
b. Document Summarizer
c. Translator
d. Text Generator

Value Addition

PyAIKit offers some advantages to developers, including:

• PyAIKit enhances the usability and accessibility of ChatGPT API by providing a streamlined interface.
• It abstracts away complexities and handles authentication, API calls, and error handling behind the scenes.
• Developers can seamlessly integrate ChatGPT functionality without dealing with API intricacies.
• PyAIKit offers enhanced control over text generation, eliminating the need for trial and error.
• It has already been optimized with different prompts and OpenAI tokens, saving effort and cost for users.

Limitations of PyAIKit

• Data Security: As PyAIKit utilizes the OpenAI API, it’s crucial to ensure the security of confidential information when transmitting data through the API.

• Cost Considerations: Although the package is open-source, it relies on the ChatGPT API, which may involve charges based on usage.

• Model Limitations: PyAIKit depends on the underlying ChatGPT models for text generation and NLP tasks. While ChatGPT performs impressively, it’s important to recognize that the model has some limitations.

Conclusion

In conclusion, PyAIKit simplifies the usage of large language models like ChatGPT for the developers.