doc-intelligence 0.1.0

Document AI - Intelligent document processing and extraction

Document AI

Documentation: https://zeel-04.github.io/document-ai/

A library for parsing, formatting, and processing documents that can be used to build AI-powered document processing pipelines with structured data extraction and citation support.

Features

• Extract structured data from PDF documents using LLMs
• Automatic citation tracking with page numbers, line numbers, and bounding boxes
• Support for digital PDFs
• Type-safe data models using Pydantic
• OpenAI integration with support for reasoning models

Installation

Requirements

• Python >= 3.10
• OpenAI API key

Install uv

First, install uv if you haven't already:

curl -LsSf https://astral.sh/uv/install.sh | sh

Install from Source

Clone the repository and install the package:

git clone https://github.com/zeel-04/document-ai.git
cd document-ai
uv sync

Install from Git (Alternative)

You can also install directly from the git repository:

uv pip install git+https://github.com/zeel-04/document-ai.git

Quick Start

Set up your OpenAI API key:

echo "OPENAI_API_KEY=your-api-key-here" > .env

Here's a simple example to extract structured data from a PDF:

from dotenv import load_dotenv
from document_ai.processer import DocumentProcessor
from document_ai.llm import OpenAILLM
from pydantic import BaseModel

# Load environment variables
load_dotenv()

# Initialize the LLM
llm = OpenAILLM()

# Create a processor from a PDF file
processor = DocumentProcessor.from_digital_pdf(
    uri="path/to/your/document.pdf",
    llm=llm,
)

# Define your data model with citations
# If you want to include citations for any field, 
# Use the `processor.citation_type` as the type.
class MyData(BaseModel):
    my_data: str
    my_data_citation: processor.citation_type

# Extract structured data
response = processor.extract(
    model="gpt-5-mini",
    reasoning={"effort": "low"},
    response_format=MyData,
)

# Get the extracted data
data = response.model_dump()
print(data)

Sample Output

{
    "my_data": "my data",
    "my_data_citation": [{
        "page": 0,
        "lines": [10],
        "bboxes": [{
            "x0": 0.058823529411764705,
            "top": 0.6095707475757575,
            "x1": 0.5635455037254902,
            "bottom": 0.6221969596969696
        }]
    }]
}

Documentation

For more detailed documentation, see the docs directory or visit the documentation site.

Development Setup

Prerequisites:

• Python 3.10+
• uv

git clone https://github.com/zeel-04/document-ai.git
cd document-ai
uv venv
uv sync