AIPDF: Simple PDF OCR with GPT-like Multimodal Models

Screw traditional OCRs or heavy libraries to get data from PDFs, GenAI does a better job!

AIPDF is a stand-alone, minimalistic, yet powerful pure Python library that leverages multi-modal gen AI models (OpenAI, llama3 or compatible alternatives) to extract data from PDFs and convert it into various formats such as Markdown or JSON.

Installation

pip install aipdf

in macOS you will need to install poppler

brew install poppler 

Quick Start

from aipdf import ocr

# Your OpenAI API key   
api_key = 'your_openai_api_key'

file = open('somepdf.pdf', 'rb')
markdown_pages = ocr(file, api_key, prompt="extract markdown, extract tables and turn charts into tables")

Ollama

You can use with any ollama multi-modal models

ocr(pdf_file, api_key='ollama', model="llama3.2", base_url= 'http://localhost:11434/v1', prompt=...)

Any file system

We chose that you pass a file object, because that way it is flexible for you to use this with any type of file system, s3, localfiles, urls etc

From url

pdf_file = io.BytesIO(requests.get('https://arxiv.org/pdf/2410.02467').content)

# extract
pages = ocr(pdf_file, api_key, prompt="extract tables and turn charts into tables, return each table in json")

From S3

s3 = boto3.client('s3', config=Config(signature_version='s3v4'),
                  aws_access_key_id=access_token,
                  aws_secret_access_key='', # Not needed for token-based auth
                  aws_session_token=access_token)


pdf_file = io.BytesIO(s3.get_object(Bucket=bucket_name, Key=object_key)['Body'].read())
# extract 
pages = ocr(pdf_file, api_key, prompt="extract tables and turn charts into tables, return each table in json")

Why AIPDF?

1. Simplicity: AIPDF provides a straightforward function, it requires minimal setup, dependencies and configuration.
2. Flexibility: Extract data into Markdown, JSON, HTML, YAML, whatever... file format and schema.
3. Power of AI: Leverages state-of-the-art multi modal models (gpt, llama, ..).
4. Customizable: Tailor the extraction process to your specific needs with custom prompts.
5. Efficient: Utilizes parallel processing for faster extraction of multi-page PDFs.

Requirements

• Python 3.7+

We will keep this super clean, only 3 required libraries:

• openai library to talk to completion endpoints
• pdf2image library (for PDF to image conversion)
• Pillow (PIL) library

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

If you encounter any problems or have any questions, please open an issue on the GitHub repository.

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AIPDF makes PDF data extraction simple, flexible, and powerful. Try it out and simplify your PDF processing workflow today!