Tutorial: Extracting Unstructured Text Using Large Language Models

This repository contains code to accompany the paper Spavound, S., and Schaer, O., and Markou, P.,, Tutorial: Extracting Unstructured Text Using Large Language Models (April 10, 2026). Available at SSRN: https://ssrn.com/abstract=6556303.

Prerequisites

1. OpenAI API Setup

This tutorial requires a paid OpenAI developer account. Follow these steps to generate your credentials:

• Account & Billing: Go to the OpenAI Platform and sign up. You must add a payment method and purchase a minimum of $5 in credits to activate your API access (Tier 1).
• Generate Key: Navigate to the API Keys section and click "Create new secret key."
• Security: Save this key immediately in a secure location. You will not be able to view it again.
• Environment Variable: For security, do not hard-code your key. Set it as an environment variable in your terminal:
  • macOS/Linux: export OPENAI_API_KEY='your-key-here'
  • Windows: setx OPENAI_API_KEY "your-key-here"

2. System Dependencies (Poppler)

The pdf2image library requires Poppler, a system-level PDF rendering library. You must install it based on your operating system:

• Linux (Ubuntu/Debian): sudo apt-get install poppler-utils
• macOS: brew install poppler
• Windows: Download binaries from poppler-windows, extract them, and add the bin folder to your system PATH.

3. Python Environment Setup

The required Python packages are listed in requirements.txt. Follow these steps to isolate the dependencies and ensure the code runs with the correct versions.

Create and activate the virtual environment:

# Create the environment
python3 -m venv .venv

# Activate the environment (macOS/Linux)
source .venv/bin/activate

# Activate the environment (Windows)
.venv\Scripts\activate

pip install -r requirements.txt

Usage

1. Open the tutorial_example.ipynb tutorial files in your preferred editor (JupyterLab, Jupyter Notebook, or VS Code).
2. Select your Python kernel.
3. Execute the cells sequentially.