Extraxt

Extraxt is a Python-based MuPDF library that enables parsing and extracting data from PDF documents.

Core Functionality

Nested JSON Output: Constructs nested JSON objects reflecting the document's content.
Subtitle and Field Matching: Define subtitles and corresponding data fields in snake case (e.g. first_name, address_line_one, income_(secondary)).
Sensitive Data Configuration: Enables sensitive data controls and configuration via the API (Coming soon).

Extraxt streamlines the extraction process, converting PDF content into structured JSON for easy data manipulation and integration.

Installation

Install Extraxt

pip install extraxt

Upgrade to new version of Extraxt

pip install --upgrade extraxt

Using Conda with Extraxt

conda create --name [YOUR_ENV] python=3.11 -y
conda activate [YOUR_ENV]
pip install extraxt

Usage

Extraxt is able to consume either an asynchronous byte stream or a buffer directly from disk.

Before you begin:

Matching something like Phone (Secondary) -> phone_(secondary) will require the usage of parenthesis as of 0.0.17. This will soon be opt in, where by default the parenthesis will be redacted.
As of 0.0.17, sensitive data is not configurable via the API, and instead "Date of birth" is parsed as "age" only.

Read file from disk

Reading from a Buffer stream can be done using with open as is standard in Python. From there you can invoke .read() on the binary and pass your fields specification. fields accepts an object of user-input key's (subtitles), where the value is a series of matches (snaked_cased) to that of the exact PDF text content within your document.

from extraxt import Extraxt

from .config import FIELDS

extraxt = Extraxt()


def main():
    with open("file.pdf", "rb") as buffer:
        stream = buffer.read()
        output = extraxt.read(stream, FIELDS)
        print(output)


if __name__ == "__main__":
    main()

Read file in asynchronous API

FastAPI

For cases using FastAPI, Extraxt is a synchronous package and will block the main thread. To perform non-blocking/asynchronous extraction, you will need to use asyncio and Futures.

import traceback
import json

from fastapi import File, HTTPException, JSONResponse
from extraxt import Extraxt

from .util import event_loop
from .config import FIELDS

extraxt = Extraxt()


async def process_file(file: File):
    try:
        content = file.read()
        if not content:
            raise HTTPException(500, "Failed to read file.")
        content = await event_loop(extraxt.read, content, FIELDS)

    except Exception as e:
        tb = traceback.format_exc()
        raise HTTPException(500, f"Failed to triage file {tb}")

    return JSONResponse({
        "content": json.loads(content),
    })

0mjs/extraxt