/Docprompt

Enterprise-ready Document Analysis with Large Language Models

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Docprompt

Document AI, powered by LLM's
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About

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.

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
    • Document Search, powered by Rust 🔥
  • Support for most OCR providers with batched inference
    • Google ✅
    • Amazon Textract ✅
    • Tesseract ✅
    • Azure Document Intelligence 🔴
  • Layout Aware Page Representation
    • Run Document Layout Analysis with text-only LLM's!
  • Prompt Garden for common document analysis tasks zero-shot, including:
    • Markerization (Pdf2Markdown)
    • Table Extraction
    • Page Classification
    • Key-value extraction (Coming soon)
    • Segmentation (Coming soon)

Documents and large language models

Features

  • Representations for common document layout types - TextBlock, BoundingBox, etc
  • Generic implementations of OCR providers
  • Document Search powered by Rust and R-trees 🔥
  • Table Extraction, Page Classification, PDF2Markdown

Installation

Use the package manager pip to install Docprompt.

pip install docprompt

With an OCR provider

pip install "docprompt[google]

With search support

pip install "docprompt[search]"

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)

Converting a PDF to markdown

Coverting documents into markdown is a great way to prepare documents for downstream chunking or ingestion into a RAG system.

from docprompt import load_document_node
from docprompt.tasks.markerize import AnthropicMarkerizeProvider

document_node = load_document_node("path/to/my.pdf")
markerize_provider = AnthropicMarkerizeProvider()

markerized_document = markerize_provider.process_document_node(document_node)

Extracting Tables

Extract tables with SOTA speed and accuracy.

from docprompt import load_document_node
from docprompt.tasks.table_extraction import AnthropicTableExtractionProvider

document_node = load_document_node("path/to/my.pdf")
table_extraction_provider = AnthropicTableExtractionProvider()

extracted_tables = table_extraction_provider.process_document_node(document_node)

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

Document Search

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 🔥 🚀

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