llm-data-converter 2.0.7

Convert any document, text, or URL into LLM-ready data format with advanced neural OCR capabilities powered by state-of-the-art pre-trained models

LLM Data Converter v2.0.0

Convert any document, text, or URL into LLM-ready data format with advanced neural OCR capabilities powered by state-of-the-art pre-trained models.

Installation

pip install llm-data-converter

Requirements:

• Python 3.8 or higher

System Dependencies for Neural OCR

For neural OCR functionality to work properly, you may need to install additional system dependencies:

Ubuntu/Debian:

sudo apt update
sudo apt install -y libgl1 libglib2.0-0 libgomp1
pip install setuptools

macOS:

# Usually not needed, but if you encounter OpenGL issues:
brew install mesa

Note: The package will automatically download and cache neural models on first use.

Quick Start

from llm_converter import FileConverter

# Basic conversion with neural OCR
converter = FileConverter()
result = converter.convert("document.pdf").to_markdown()
print(result)

Features

• Multiple Input Formats: PDF, DOCX, TXT, HTML, URLs, Excel files, and more
• Multiple Output Formats: Markdown, HTML, JSON, Plain Text
• LLM Integration: Seamless integration with LiteLLM and other LLM libraries
• Local Processing: Process documents locally without external dependencies
• Layout Preservation: Maintain document structure and formatting
• Neural OCR: Advanced document understanding powered by state-of-the-art pre-trained models:
  • Layout Detection: Neural models for document structure understanding
  • Text Recognition: High-accuracy OCR with confidence scoring
  • Table Structure: Intelligent table detection and parsing with proper markdown output
  • Automatic Model Download: Models are automatically downloaded and cached

Neural Document Processing

Version 2.0.0 introduces advanced neural document processing capabilities:

Neural OCR (Default)

Uses state-of-the-art pre-trained models for superior accuracy:

• Layout Detection: Advanced neural models for document structure understanding
• Text Recognition: High-accuracy OCR with confidence scoring
• Table Structure: Intelligent table detection and parsing with proper markdown output
• Automatic Model Download: Models are automatically downloaded on first use
• Document Understanding: Comprehensive document analysis beyond simple OCR

Usage Examples

Convert PDF to Markdown

from llm_converter import FileConverter

converter = FileConverter()
result = converter.convert("document.pdf").to_markdown()
print(result)

Convert URL to HTML

from llm_converter import FileConverter

converter = FileConverter()
result = converter.convert_url("https://example.com").to_html()
print(result)

Convert Excel to JSON

from llm_converter import FileConverter

converter = FileConverter()
result = converter.convert("data.xlsx").to_json()
print(result)

Chain with LLM

from llm_converter import FileConverter
from litellm import completion

converter = FileConverter()
document_content = converter.convert("report.pdf").to_markdown()

# Use with any LLM
response = completion(
    model="openai/gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant that analyzes documents."},
        {"role": "user", "content": f"Summarize this document:\n\n{document_content}"}
    ]
)

print(response.choices[0].message.content)

Supported Formats

Input Formats

• Documents: PDF, DOCX, TXT
• Web: URLs, HTML files
• Data: Excel (XLSX, XLS), CSV
• Images: PNG, JPG, JPEG (with neural OCR capabilities)

Output Formats

• Markdown: Clean, structured markdown with proper table formatting
• HTML: Formatted HTML with styling
• JSON: Structured JSON data
• Plain Text: Simple text extraction

Advanced Usage

Custom Configuration

from llm_converter import FileConverter

converter = FileConverter(
    preserve_layout=True,
    include_images=True,
    ocr_enabled=True   
)

result = converter.convert("document.pdf").to_markdown()
print(result)

Batch Processing

from llm_converter import FileConverter

converter = FileConverter()
files = ["doc1.pdf", "doc2.docx", "doc3.xlsx"]

results = []
for file in files:
    result = converter.convert(file).to_markdown()
    results.append(result)

Testing Neural OCR

# Test the neural OCR capabilities
from llm_converter.pipeline.neural_document_processor import NeuralDocumentProcessor

# Initialize neural document processor
processor = NeuralDocumentProcessor()

# Extract text with layout awareness
text = processor.extract_text_with_layout("sample.png")
print(text)

API Reference

FileConverter

Main class for converting documents to LLM-ready formats.

Methods

• convert(file_path: str) -> ConversionResult: Convert a file to internal format
• convert_url(url: str) -> ConversionResult: Convert a URL page contents to internal format
• convert_text(text: str) -> ConversionResult: Convert plain text to internal format

ConversionResult

Result object with methods to export to different formats.

Methods

• to_markdown() -> str: Export as markdown
• to_html() -> str: Export as HTML
• to_json() -> dict: Export as JSON
• to_text() -> str: Export as plain text

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Third-Party Dependencies

This project uses several third-party libraries:

• EasyOCR - Apache 2.0 License (https://github.com/JaidedAI/EasyOCR)
• PyTorch - BSD 3-Clause License (https://pytorch.org/)
• Transformers - Apache 2.0 License (https://github.com/huggingface/transformers)
• Pillow - HPND License (https://python-pillow.org/)
• python-docx - MIT License (https://github.com/python-openxml/python-docx)
• pandas - BSD 3-Clause License (https://pandas.pydata.org/)
• numpy - BSD 3-Clause License (https://numpy.org/)
• pdf2image - MIT License (https://github.com/Belval/pdf2image)
• markdownify - MIT License (https://github.com/matthewwithanm/markdownify)

All dependencies are used in accordance with their respective licenses.