Boosting Team Collaboration with Slack and OpenAI

Table of Contents

1. Introduction
2. Background and Project Requirements
3. Approach Inspired by Customer Service Feedback
4. The Generative Sum Function
5. The Fine Sentiment Function
6. Code Overview
7. Solution in Action: Demo
8. Conclusion and Future Improvements
9. About the Author
10. FAQs

Introduction

Welcome to this article where we will explore an integration solution called "Open Eye Integration." This solution aims to bridge the gap between Slack and Open Eye, providing a condensed summary of Slack Channel content. In the following sections, we will dive into the project requirements, understand the approach taken, explore the key functions developed, and witness the solution in action through a demo. Additionally, we will discuss potential future improvements and wrap up with information about the author. So, let's get started!

Background and Project Requirements

To kick things off, let's first understand the background and project requirements that led to the development of the Open Eye Integration solution. The task at HAND was to integrate Slack with Open Eye and Create a future where the content from a Slack channel is sent to Open Eye and generates a condensed summary. The goal was to enable users to process and summarize information efficiently. The solution designed for this challenge involves starting the message, sending it to the OpenAI API, processing the information to create a general summary, and posting the summary on the Slack channel.

Approach Inspired by Customer Service Feedback

Drawing inspiration from customer service feedback, a two-fold approach was developed to tackle the integration challenge. The first key function is the "Generative Sum Function." Triggered by a comment, this function obtains the messages from the Slack channel, concatenates them into a single Paragraph, and generates a concise summary. The Second function, called the "Fine Sentiment Function," is event-Based. Whenever a new message is received, it triggers the OpenAi API to analyze the sentiment. Based on the sentiment analysis, it reacts with an appropriate emoji, indicating a positive or negative sentiment.

The Generative Sum Function The Generative Sum Function is the heart of the Open Eye Integration solution. It leverages the T5 model, specifically the "t5-base" model with a temperature of 0.2. This low temperature ensures minimal creativity, allowing the summary to be closest to the actual message. Additionally, a maximum number of tokens of 300 is set to ensure the summary does not exceed a certain length, thereby maintaining readability.

The Fine Sentiment Function The Fine Sentiment Function also utilizes the T5 model, but with a temperature of 0. This temperature value ensures that the sentiment analysis result is precise and aligned with the input message. To facilitate comparison, the function preprocesses the response by converting it to lowercase and removing punctuation. The sentiment is then determined as either positive or negative, providing valuable insights into the feedback received.

Code Overview

Now that we have a grasp of the approach and the key functions, let's dive into the code and understand how these functions work in the Open Eye Integration solution. The code begins by importing the necessary dependencies and setting up the environment for seamless integration between Slack, Open Eye, and the application. The Slack API key is stored as a secret to ensure security.

Two additional essential functions handle events in the code: the "Slack Listen Event Handler" and the "Slack Slash Command Handler." The Slack Listen Event Handler is triggered when a message is received in the channel. It verifies that the message is a Timely one and then reacts by either turning up or turning down an emoji based on its sentiment. The Slack Slash Command Handler is triggered when the "/open-i" command is used. It gathers the recent messages from the past two minutes, concatenates them, passes them to the Generative Sum Function, and posts the summary in the Slack channel.

Solution in Action: Demo

To provide a clear understanding of the Open Eye Integration solution, a demo video has been prepared. In the demo, feedback messages about an online banking service are used to showcase the functionality. The messages are copied and pasted into the Slack channel, capturing the system's response to positive sentiment messages. The positive feedback message is acknowledged with a positive reaction and added to the "Feedback Positive" channel. The summarized message is generated by using the Slack Slash Command, "/open-i," and it appears as a summary in the Slack channel.

Conclusion and Future Improvements

In conclusion, the Open Eye Integration solution successfully integrates Slack and Open Eye, providing a condensed summary of Slack channel content. The solution demonstrates an approach inspired by customer service feedback, utilizing two key functions: the Generative Sum Function and the Fine Sentiment Function. The code overview gives a clear understanding of how these functions are implemented within the solution. The demo video showcases the solution in action, offering a real-world example and validation of its functionality.

As for future improvements, automation of the process could be considered, allowing the integration to run at predefined intervals for unread messages. The sentiment analysis could also be expanded to include categories such as Bot Improvement Recognition, Complaints, Health, and New Filters. Additionally, integrating with other messaging platforms like emails, forms, WhatsApp, and Telegram could enhance the solution's versatility and effectiveness.

About the Author

The Open Eye Integration solution was created by a talented developer named [Author Name]. They have a strong background in software development and a passion for creating innovative solutions. To know more about the author and their work, You can visit their profile on [Banker Profile Name]. Feel free to reach out to the author for any feedback or further inquiries.

FAQs

Q: What is the purpose of the Open Eye Integration solution? A: The purpose of the Open Eye Integration solution is to integrate Slack and Open Eye, generating a condensed summary of Slack channel content for efficient information processing.

Q: Which models are used in the solution? A: The solution utilizes the T5 model to power the Generative Sum Function and the Fine Sentiment Function.

Q: How does the sentiment analysis work? A: The sentiment analysis is performed on new messages using the Fine Sentiment Function. It analyzes the sentiment and reacts with an appropriate emoji based on the positive or negative result.

Q: Can the solution be extended to other messaging platforms? A: Yes, the solution can be extended to integrate with other messaging platforms such as emails, forms, WhatsApp, and Telegram, enhancing its capabilities.

Q: Are there any plans for future improvements to the solution? A: Yes, automation of the process and categorizing sentiment beyond positive and negative are potential future improvements to enhance the solution's functionality and usability.