Document AI - Intelligent document processing and extraction
Project description
Document AI
Documentation: https://zeel-04.github.io/doc-intelligence/
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 with uv
uv pip install doc-intelligence
Or with pip:
pip install doc-intelligence
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 doc_intelligence.processer import DocumentProcessor
from doc_intelligence.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
class Balance(BaseModel):
ending_balance: float
# Configure extraction with citations
config = {
"response_format": Balance,
"llm_config": {
"model": "gpt-5",
"reasoning": {"effort": "minimal"},
},
"extraction_config": {
"include_citations": True,
"extraction_mode": "single_pass",
"page_numbers": [0, 1], # Optional: specify which pages to process
}
}
# Extract structured data
response = processor.extract(config)
# Get the extracted data and citations
data, citations = response
print(f"Extracted data: {data}")
print(f"Citations: {citations}")
Sample Output
The extract method returns a tuple containing the extracted data and citation information:
(Balance(ending_balance=111.61),
{'ending_balance': {'value': 111.61,
'citations': [{'page': 0,
'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/doc-intelligence.git
cd doc_intelligence
uv venv
uv sync
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