docprompt 0.1.4

Documents and large language models.

Docprompt

Docprompt is a library for Document AI. It aims to make enterprise-level document analysis easy thanks to the zero-shot capability of large language models while also providing a toolset for working with various document formats.

Supercharged Document Analysis

• Common utilities for interacting with PDFs
  • PDF loading and serialization
  • PDF byte compression using Ghostscript :ghost:
  • Fast rasterization :fire: :rocket:
  • Page splitting, re-export with PikePDF
• Support for most OCR providers with batched inference
  • Google :white_check_mark:
  • Azure Document Intelligence :red_circle:
  • Amazon Textract :red_circle:
  • Tesseract :red_circle:

Documents and large language models

• Documentation: https://docprompt.dev
• GitHub: https://github.com/Page-Leaf/docprompt
• PyPI: https://pypi.org/project/docprompt/
• Free software: Apache-2.0

Features

• Representations for common document layout types - TextBlock, BoundingBox, etc
• Generic implementations of OCR providers

Installation

Use the package manager pip to install Docprompt.

pip install docprompt

With an OCR provider

pip install "docprompt[google]

Usage

Simple Operations

from docprompt import load_document

# Load a document
document = load_document("path/to/my.pdf")

# Rasterize a single page using Ghostscript
page_number = 5
rastered = document.rasterize_page(page_number, dpi=120)

# Split a pdf based on a page range
document_2 = document.split(start=125, stop=130)

Performing OCR

from docprompt import load_document, DocumentNode
from docprompt.tasks.ocr.gcp import GoogleOcrProvider

provider = GoogleOcrProvider.from_service_account_file(
  project_id=my_project_id,
  processor_id=my_processor_id,
  service_account_file=path_to_service_file
)

document = load_document("path/to/my.pdf")

# A container holds derived data for a document, like OCR or classification results
document_node = DocumentNode.from_document(document)

provider.process_document_node(document_node) # Caches results on the document_node

document_node[0].ocr_result # Access OCR results