Structured Data Extraction

A Python project for extracting structured data (like invoice fields) from unstructured PDF documents using LLM-based parsing. This repository includes sample PDFs, and processing-pipeline for exporting extracted data to Excel.

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Features

• Extracts structured fields (customer name, address, invoice number, date, amount due) from PDFs.
• LLM-based workflow (LLaMa) for high-quality parsing.
• Used locally hosted open-source LLM to mitigate data privacy concerns associated with cloud-based APIs.
• Saves extracted results into an Excel file (extracted_invoice_data.xlsx).
• Easily extensible for other document types or models..

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Project Structure

. ├── Sample PDFs/
├── PDFdataExtraction_ollama.py
├── util.py
├── extracted_invoice_data.xlsx
├── requirements.txt
├── .gitignore
└── README.md

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Installation

Clone the repository:

git clone https://github.com/AnandKri/structured-data-extraction.git
cd structured-data-extraction

Ensure Ollama is installed and the required model is pulled:

ollama pull llama3.2:1b-instruct-q8_0

Create a virtual environment:

python -m venv venv
source venv/bin/activate   # Linux/macOS
venv\Scripts\activate      # Windows

Install dependencies:

pip install -r requirements.txt

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Usage

Run the extraction script:

python PDFdataExtraction_ollama.py