academicAI

Open source AI-powered tools for academic research

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Information

Contained within this repository is a number of tools, utilising generative AI, which can be used for academic purposes.

The main interface for each tool is the respective Jupyter notebook. These notebooks interface with respective folders that contain feature logic.

These are not designed to test or experiment on AI, but instead provide general functions that may help with day-to-day research activities.

The authors and contributors to this repository make no guarentee all features will work as intended on all devices.

If you come across any bugs, or you want to make enhancements, or you want to contribute new tools, please feel free to open a PR. Otherwise if you have any issues, please raise them here.

Responsible AI in Academia

This repository contains a set of general guidelines for the use of generative AI in academic settings. This includes general use as part of the research process and in situations where the AI itself is the focus of the research.

Find the guidelines here.

Tool list

The below features can be found at the notebook within the folder corresponding to the bullet-point's number

• 01 Summarise all (supported) files within a folder (including within subdirectories), creating an excel spreadsheet within each folder detailing its contents. Great for summarising large volumes of documents.
• 02 From a list of documents, find relevant pages and return summaries of these pages from a folder of documents (i.e. this tool finds relevant pages (from one or many documents) based on a criteria you provide and summarises them)
• 03 Transcribe audio: use OpenAI's Whisper Large model to convert audio or video to text.
• 04 Miscellaneous: General AI functions including image generation (using Dalle3) and text to voice (using ElevenLabs)

Contributing

Interesting in providing additional tools, feel free to make a PR detailing the feature.

Setup

1. Clone this repository

2. Update the config.py file with the necessary details (instructions on what is needed is contained in that file)

3. Install Python (we recommended Python 3.12)

4. Create a Virtual Environment: Run the following command to create a virtual environment named venv within your project directory:

   python3 -m venv venv

5. Activate the Virtual Environment:

   On Unix-based systems (Linux/MacOS), use the following command:

   source venv/bin/activate

   On Windows, the command is slightly different:

   .\venv\Scripts\activate

   Once activated, your terminal prompt will usually change to indicate that the virtual environment is active. From now on, any Python or Pip commands you run will use the versions in the virtual environment, not the global versions.

6. Install required packages using:

   pip install -r requirements.txt

Once these setup instructions are complete, you should be able to open a notebook and carry out the logic within. Make sure to select the created venv if prompted to select a kernel.