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Convert any document, text, or URL into LLM-ready data format

Project description

LLM Data Converter

Convert any document, text, or URL into LLM-ready data format.

Installation

pip install llm-data-converter

Requirements:

  • Python 3.8 or higher

System Dependencies for OCR

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

Ubuntu/Debian:

sudo apt update
sudo apt install -y libgl1-mesa-glx libglib2.0-0

macOS:

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

Note: The package will automatically detect if OpenGL is available and provide helpful warnings if system dependencies are missing.

Quick Start

from llm_converter import FileConverter

# Basic conversion
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

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("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 OCR capabilities)

Output Formats

  • Markdown: Clean, structured markdown
  • 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()

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)

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 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:

All dependencies are used in accordance with their respective licenses.

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