BeagleTA

BeagleTA: A Text Analysis Processing Pipeline for the analysis of relationships between documents based on word usage

• Oliver Bonham-Carter, Web

• email: obonhamcarter@allegheny.edu

• Date: 9th April 2025

• Github: BeagleTA

Overview

This project is a text analysis processing pipeline designed to analyze relationships between documents based on word usage. The pipeline consists of three main components: a parser, a CSV cleaner/filter, and a random sampler. Each component is implemented as a separate binary in Rust, allowing for efficient data processing and analysis.

Rinfomaid

Rinfomaid: A textual AI application, markdown archival and RAG document query system written in Rust. Neat-o!, right?

• GitHub: Rinfomaid

Date: 16 July 2025

Oliver Bonham-Carter

Email: obonhamcarter at allegheny.edu

MIT Licence

The RinfoMaid is a Rust command-line tool that interacts with the Ollama API to generate text using AI models. The program can either take a prompt from the command line, a prompt file, or ask the user to enter a prompt. It then sends the prompt to the Ollama API and retrieves a generated response, which is saved to a specified output file in markdown format.

Infomaid

A Textual AI and RAG-Enabled Learning Application

A simple textual AI RAG application using Ollama for generative AI, chatting with PDFs AND chatting (querying) documents prepared in xml!

GitHub link: https://github.com/developmentAC/infomaid

Overview

Infomaid is a simple AI prompt-based solution with built in Retrieval augmented generation (RAG) support!

Gradealert

GitHub link: https://github.com/developmentAC/gradeAlert

Table of contents

• Overview
• Tool
• Usage At Terminal
• CSV
• Outputted files
• Placing Gradebook Files
• Pushing in Bulk
• File Structure
• Summary of Commands
• A Work In Progress

Overview

GitHub Classroom is an excellent resource to handle work repositories for a course of many students. Each student, after “accepting” an assignment is issued a unique repository in which work can be completed and pushed to the instructor.

Sentiminer

GitHub link: https://github.com/developmentAC/sentiminer

Table of contents

• Overview
• Mechanism
• Commands
• Output
• References
• Evolution

Overview

Sentiment analysis is the semantic study of emotion in language. This form of analysis is used to determine the level of satisfaction or dissatisfaction in textual data such as reviews, survey responses, online and social media posting and similar. Due to the often high volume of feedback from the above types of textual data, sentiment analysis provides an automated solution for determining the “feeling” of the text.