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Docprompt - Getting Started

Supercharged Document Analysis

  • Common utilities for interacting with PDFs
  • PDF loading and serialization
  • PDF byte compression using Ghostscript 👻
  • Fast rasterization 🔥 🚀
  • Page splitting, re-export with PDFium
  • Support for most OCR providers with batched inference
  • Google ✅
  • Azure Document Intelligence 🔴
  • Amazon Textract 🔴
  • Tesseract 🔴

Installation

Base installation

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

When a large language model returns a result, we might want to highlight that result for our users. However, language models return results as text, while what we need to show our users requires a page number and a bounding box.

After extracting text from a PDF, we can support this pattern using DocumentProvenanceLocator, which lives on a DocumentNode

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  # With OCR results available, we can now instantiate a locator and search through documents.  document_node.locator.search("John Doe") # This will return a list of all terms across the document that contain "John Doe" document_node.locator.search("Jane Doe", page_number=4) # Just return results a list of matching results from page 4

This functionality uses a combination of rtree and the Rust library tantivy, allowing you to perform thousands of searches in seconds 🔥 🚀