aicapture 0.1.2

A Python module to extract content from PDF documents using Vision Language Models (VLMs)

AI Vision Capture

A powerful Python library for extracting and analyzing content from PDF documents using Vision Language Models (VLMs). This library provides a flexible and efficient way to process documents with support for multiple VLM providers including OpenAI, Anthropic Claude, Google Gemini, and Azure OpenAI.

Features

• 🔍 Multi-Provider Support: Compatible with major VLM providers (OpenAI, Claude, Gemini, Azure, OpenSource models)
• 📄 PDF Processing: Efficient PDF to image conversion with configurable DPI
• 🚀 Async Processing: Asynchronous processing with configurable concurrency
• 💾 Two-Layer Caching: Local file system and cloud caching for improved performance
• 🔄 Batch Processing: Process multiple PDFs in parallel
• 📝 Text Extraction: Enhanced accuracy through combined OCR and VLM processing
• 🎨 Image Quality Control: Configurable image quality settings
• 📊 Structured Output: Well-organized JSON and Markdown output

Quick Start

Install the library:

pip install aicapture

from vision_capture import VisionParser

# Initialize parser
parser = VisionParser()

# Process a single PDF
result = parser.process_pdf("path/to/your/document.pdf")

# Process a folder of PDFs asynchronously
async def process_folder():
    results = await parser.process_folder_async("path/to/folder")
    return results

Configuration

The library is configured through environment variables that can be set in your shell or via a .env file.

1. Copy .env.template to .env
2. Choose ONE vision provider:

USE_VISION=claude  # Options: openai, claude, gemini, azure-openai

3. Configure your chosen provider's API key and settings
4. Adjust common settings if needed (DPI, concurrency, etc.)

See .env.template for detailed configuration options and examples.

Output Format

The library produces structured output in both JSON and Markdown formats:

{
  "file_object": {
    "file_name": "example.pdf",
    "file_hash": "sha256_hash",
    "total_pages": 10,
    "total_words": 5000,
    "pages": [
      {
        "page_number": 1,
        "page_content": "extracted content",
        "page_hash": "sha256_hash"
      }
    ]
  }
}

Advanced Usage

from vision_capture import VisionParser, GeminiVisionModel

# Configure Gemini vision model with custom settings
vision_model = GeminiVisionModel(
    model="gemini-2.0-flash",
    api_key="your_gemini_api_key"
)

# Initialize parser with custom configuration
parser = VisionParser(
    vision_model=vision_model,
    dpi=400,
    image_quality="high",
    prompt="""
    Please analyze this technical document and extract:
    1. Equipment specifications and model numbers
    2. Operating parameters and limits
    3. Maintenance requirements
    4. Safety protocols
    5. Quality control metrics
    """
)

# Process PDF with custom settings
result = parser.process_pdf(
    pdf_path="path/to/document.pdf",
    cache_enabled=True
)

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

For detailed guidelines, see our Contributing Guide.

License

Copyright 2024 Aitomatic, Inc.

Licensed under the Apache License, Version 2.0. See LICENSE for details.