Content Extractor with Vision LLM

Extract and describe content from documents using Vision Language Models.

Requirements

• Python 3.8 or higher
• Operating system: Windows, macOS, or Linux
• Disk space: At least 1GB free space (more if using local Llama model)

Features

• Extract text and images from PDF, DOCX, and PPTX files
• Describe images using local (Ollama) or cloud-based (OpenAI) Vision Language Models
• Save extracted text and image descriptions in markdown format
• Support for both CLI and library usage
• Multiple extraction methods for different use cases
• Detailed logging with timestamps for all operations

Installation

1. Install System Dependencies

   # macOS (using Homebrew)
   brew install --cask libreoffice  # Required for DOCX/PPTX processing
   brew install poppler             # Required for PDF processing
   
   # Ubuntu/Debian
   sudo apt-get update
   sudo apt-get install libreoffice poppler-utils
   
   # Windows
   # Download and install:
   # - LibreOffice: https://www.libreoffice.org/download/download/
   # - Poppler: http://blog.alivate.com.au/poppler-windows/
   # Add poppler's bin directory to your system PATH
   

2. Install the Package

   # Using pip
   pip install pyvisionai
   
   # Using poetry
   poetry add pyvisionai
   

3. Create Working Directories (optional)

   # The package will create these automatically if they don't exist
   mkdir -p content/source content/extracted content/log
   

4. Setup for Image Description

   For cloud image description (default, recommended):

   # Set OpenAI API key
   export OPENAI_API_KEY='your-api-key'
   

   For local image description (optional):

   # Start Ollama server
   ollama serve
   
   # Pull the required model
   ollama pull llama3.2-vision
   

Usage

Command Line Interface

1. Extract Content from Files

   # Process a single file (using default page-as-image method)
   file-extract -t pdf -s path/to/file.pdf -o output_dir
   file-extract -t docx -s path/to/file.docx -o output_dir
   file-extract -t pptx -s path/to/file.pptx -o output_dir
   
   # Process with specific extractor
   file-extract -t pdf -s input.pdf -o output_dir -e text_and_images
   
   # Process all files in a directory
   file-extract -t pdf -s input_dir -o output_dir
   

2. Describe Images

   # Using GPT-4 Vision (default, recommended)
   describe-image -i path/to/image.jpg
   
   # Using local Llama model
   describe-image -i path/to/image.jpg -u llama
   
   # Additional options
   describe-image -i image.jpg -v  # Verbose output
   

Library Usage

from pyvisionai import create_extractor, describe_image_openai, describe_image_ollama

# 1. Extract content from files
extractor = create_extractor("pdf")  # or "docx" or "pptx"
output_path = extractor.extract("input.pdf", "output_dir")

# With specific extraction method
extractor = create_extractor("pdf", extractor_type="text_and_images")
output_path = extractor.extract("input.pdf", "output_dir")

# 2. Describe images
# Using GPT-4 Vision (default, recommended)
description = describe_image_openai(
    "image.jpg",
    model="gpt-4o-mini",  # default
    api_key="your-api-key",  # optional if set in environment
    max_tokens=300  # default
)

# Using local Llama model
description = describe_image_ollama(
    "image.jpg",
    model="llama3.2-vision"  # default
)

Logging

The application maintains detailed logs of all operations:

• Logs are stored in content/log/ with timestamp-based filenames
• Each run creates a new log file: pyvisionai_YYYYMMDD_HHMMSS.log
• Logs include:
  • Timestamp for each operation
  • Processing steps and their status
  • Error messages and warnings
  • Extraction method used
  • Input and output file paths

Environment Variables

# Required for OpenAI Vision (if using cloud description)
export OPENAI_API_KEY='your-api-key'

# Optional: Ollama host (if using local description)
export OLLAMA_HOST='http://localhost:11434'

License

This project is licensed under the Apache License 2.0.

Command Parameters

file-extract Command

file-extract [-h] -t TYPE -s SOURCE -o OUTPUT [-e EXTRACTOR] [-m MODEL] [-k API_KEY] [-v]

Required Arguments:
  -t, --type TYPE         File type to process (pdf, docx, pptx)
  -s, --source SOURCE     Source file or directory path
  -o, --output OUTPUT     Output directory path

Optional Arguments:
  -h, --help             Show help message and exit
  -e, --extractor TYPE   Extraction method:
                         - page_as_image: Convert pages to images (default)
                         - text_and_images: Extract text and images separately
  -m, --model MODEL      Vision model for image description:
                         - gpt4: GPT-4 Vision (default, recommended)
                         - llama: Local Llama model
  -k, --api-key KEY      OpenAI API key (can also be set via OPENAI_API_KEY env var)
  -v, --verbose          Enable verbose logging

describe-image Command

describe-image [-h] -i IMAGE [-m MODEL] [-k API_KEY] [-t MAX_TOKENS] [-v]

Required Arguments:
  -i, --image IMAGE      Path to image file

Optional Arguments:
  -h, --help            Show help message and exit
  -m, --model MODEL     Vision model to use:
                        - gpt4: GPT-4 Vision (default, recommended)
                        - llama: Local Llama model
  -k, --api-key KEY     OpenAI API key (can also be set via OPENAI_API_KEY env var)
  -t, --max-tokens NUM  Maximum tokens for response (default: 300)
  -v, --verbose         Enable verbose logging

Examples

File Extraction Examples

# Basic usage with defaults (page_as_image method, GPT-4 Vision)
file-extract -t pdf -s document.pdf -o output_dir

# Specify extraction method
file-extract -t docx -s document.docx -o output_dir -e text_and_images

# Use local Llama model for image description
file-extract -t pptx -s slides.pptx -o output_dir -m llama

# Process all PDFs in a directory with verbose logging
file-extract -t pdf -s input_dir -o output_dir -v

# Use custom OpenAI API key
file-extract -t pdf -s document.pdf -o output_dir -k "your-api-key"

Image Description Examples

# Basic usage with defaults (GPT-4 Vision)
describe-image -i photo.jpg

# Use local Llama model
describe-image -i photo.jpg -m llama

# Customize token limit
describe-image -i photo.jpg -t 500

# Enable verbose logging
describe-image -i photo.jpg -v

# Use custom OpenAI API key
describe-image -i photo.jpg -k "your-api-key"