aicapture 0.1.4

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

Coming Soon Features

• 🔗 Cross-Document Knowledge Capture: Capture structured knowledge across multiple documents

• 🎥 Video Knowledge Capture: Capture structured knowledge from video

Quick Start

Installation

pip install aicapture

Basic Setup

1. Set your chosen provider and API key (example using OpenAI):

export USE_VISION=openai
export OPENAI_API_KEY=your_openai_key

2. Use in your code:

from vision_capture import VisionParser

# Initialize parser
parser = VisionParser()

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

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

For detailed configuration options and examples, see:

• Configuration Guide
• Advanced Usage Examples

Configuration

Production Environment

In production, configure the library using environment variables in your shell or deployment environment.

Common settings you may want to adjust:

# Optional performance settings
export MAX_CONCURRENT_TASKS=5      # Number of concurrent processing tasks
export VISION_PARSER_DPI=333      # Image DPI for PDF processing

Development Environment

For local development:

1. Clone the repository
2. Copy .env.template to .env
3. Edit .env with your settings
4. Install development dependencies: pip install -e ".[dev]"

See .env.template for all available configuration options.

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,
    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",
)

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/tiny-but-mighty)
3. Commit your changes (git commit -m 'feat: add small but delightful improvement')
4. Push to the branch (git push origin feature/tiny-but-mighty)
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.