gptpdf

Using VLLM (like GPT-4o) to parse PDF into markdown.

Our approach is very simple (only 293 lines of code), but can almost perfectly parse typography, math formulas, tables, pictures, charts, etc.

Average cost per page: $0.013

This package use GeneralAgent lib to interact with OpenAI API.

pdfgpt-ui is a visual tool based on gptpdf.

Process steps

Use the PyMuPDF library to parse the PDF to find all non-text areas and mark them, for example:

Use a large visual model (such as GPT-4o) to parse and get a markdown file.

DEMO

examples/attention_is_all_you_need/output.md for PDF examples/attention_is_all_you_need.pdf.
examples/rh/output.md for PDF examples/rh.pdf.

Installation

pip install gptpdf

Usage

from gptpdf import parse_pdf
api_key = 'Your OpenAI API Key'
content, image_paths = parse_pdf(pdf_path, api_key=api_key)
print(content)

See more in test/test.py

API

parse_pdf

Function:

def parse_pdf(
        pdf_path: str,
        output_dir: str = './',
        prompt: Optional[Dict] = None,
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
        model: str = 'gpt-4o',
        verbose: bool = False,
        gpt_worker: int = 1
) -> Tuple[str, List[str]]:

Parses a PDF file into a Markdown file and returns the Markdown content along with all image paths.

Parameters:

pdf_path: str
Path to the PDF file
output_dir: str, default: './'
Output directory to store all images and the Markdown file
api_key: Optional[str], optional
OpenAI API key. If not provided, the OPENAI_API_KEY environment variable will be used.
base_url: Optional[str], optional
OpenAI base URL. If not provided, the OPENAI_BASE_URL environment variable will be used. This can be modified to call other large model services with OpenAI API interfaces, such as GLM-4V.
model: str, default: 'gpt-4o'
OpenAI API formatted multimodal large model. If you need to use other models, such as:
- qwen-vl-max
- GLM-4V
- Yi-Vision
- Azure OpenAI, by setting the base_url to https://xxxx.openai.azure.com/ to use Azure OpenAI, where api_key is the Azure API key, and the model is similar to azure_xxxx, where xxxx is the deployed model name (tested).
verbose: bool, default: False
Verbose mode. When enabled, the content parsed by the large model will be displayed in the command line.
gpt_worker: int, default: 1
Number of GPT parsing worker threads. If your machine has better performance, you can increase this value to speed up the parsing.
prompt: dict, optional
If the model you are using does not match the default prompt provided in this repository and cannot achieve the best results, we support adding custom prompts. The prompts in the repository are divided into three parts:
- prompt: Mainly used to guide the model on how to process and convert text content in images.
- rect_prompt: Used to handle cases where specific areas (such as tables or images) are marked in the image.
- role_prompt: Defines the role of the model to ensure the model understands it is performing a PDF document parsing task.
You can pass custom prompts in the form of a dictionary to replace any of the prompts. Here is an example:
```
prompt = {
    "prompt": "Custom prompt text",
    "rect_prompt": "Custom rect prompt",
    "role_prompt": "Custom role prompt"
}

content, image_paths = parse_pdf(
    pdf_path=pdf_path,
    output_dir='./output',
    model="gpt-4o",
    prompt=prompt,
    verbose=False,
)
```
**args"": LLM other parameters, such as temperature, top_p, max_tokens, presence_penalty, frequency_penalty, etc.

Join Us 👏🏻

Scan the QR code below with WeChat to join our group chat or contribute.

floatin/gptpdf