Abstract

RelataGPT is a web application designed to analyze email relationships using ChatGPT. This publication clearly states its purpose by explaining how the system enables users to gain insights into email interactions. It presents a structured approach to processing email data, integrating OpenAI models, and visualizing relationships between email participants.

The main contributions include:

The use of Laravel for backend processing and Docker for deployment.
Asynchronous request handling via Laravel queues to process large volumes of email data efficiently.
A user-friendly interface for visualizing email connections and deriving meaningful insights.
While the publication effectively outlines the methodology, it lacks a references section and a detailed discussion of existing research. Future work could explore alternative approaches and trade-offs made during development.

Methodology

Technical Approach
The RelataGPT system follows a structured methodology that includes:

Data Ingestion - Users upload email data in JSON format, which is parsed and structured for analysis.
Processing Requests - The system processes email relationships using OpenAI’s API, extracting meaningful connections.
Asynchronous Processing - Laravel queues handle high volumes of requests efficiently to ensure scalability.
Result Display - The processed data is visualized through an interactive interface, helping users understand communication patterns.
Design Decisions
The solution was implemented using:

Laravel for backend processing.
Docker for deployment.
Asynchronous processing to ensure the system scales under concurrent requests.
However, the publication does not discuss alternative approaches or trade-offs considered during the design phase. Including this information would improve the justification of the chosen architecture.

Results

Key Findings
The publication highlights:

Efficient handling of email analysis queries.
Ability to uncover hidden relationships in email communications.
Scalable processing using asynchronous Laravel queues.
Interpretation
The results indicate that RelataGPT effectively processes email interactions, offering valuable insights into communication networks. However, the publication does not define key evaluation metrics or provide a structured assessment framework.

Recommendations for Improvement
To enhance the study, the following aspects should be included:

A section discussing the significance of analyzing email interactions.
A comparison with existing email analysis tools to establish a baseline.
A references section citing related work.
A discussion of dataset sources, collection methods, and preprocessing steps.
By addressing these aspects, the publication can provide a stronger foundation for future research and practical applications.