token-vision 0.1.0a1

A fast, offline token calculator for AI vision models

Warning: This is an experimental alpha release. APIs and functionality may change significantly between versions.

Token Vision

A fast, offline token calculator for images with various AI models (Claude, OpenAI, Google). Calculate image tokens and costs without making API calls or hitting rate limits.

Author

Ashraf Ali (ashrafali.net)

Advantages

• Offline Operation: Calculate tokens without internet connectivity or API keys
• No Rate Limits: Process unlimited images without worrying about API quotas
• Ultra Fast: Get instant results without network latency
• Cost Efficient: Plan and estimate costs without spending API credits
• Multi-Model Support: One library for all major vision models
• Accurate: Uses the same tiling and scaling algorithms as the official implementations
• Developer Friendly: Clean API with type hints and comprehensive documentation
• Extensible: Support for custom models and pricing configurations

Installation

pip install token-vision

Quick Start

from token_vision import ImageTokenCalculator

# Initialize calculator
calculator = ImageTokenCalculator()  # Defaults to gemini-1-5-flash

# Calculate tokens for an image
tokens = calculator.calculate(
    image_path="path/to/image.jpg",
    model="gemini-1-5-flash"  # Optional, defaults to gemini-1-5-flash
)

# Get cost
cost = calculator.get_cost(tokens)
print(f"Tokens: {tokens:,}")
print(f"Cost: ${cost:.6f}")

Features

• Support for multiple AI models:
  • Claude (3.5 Sonnet, 3.5 Haiku, 3 Opus, 3 Sonnet, 3 Haiku)
  • GPT-4o (Latest, Nov 2024, Aug 2024, May 2024)
  • Gemini (1.5 Pro, 1.5 Flash)
• Accurate token calculation based on image dimensions
• Cost estimation for both input and output tokens
• Support for various image input formats
• Batch processing capabilities
• Memory efficient processing
• Custom model configuration support

Documentation

Supported Models

The library supports the following models with their respective token calculation methods:

Claude Models

• claude-3-5-sonnet-20241022
• claude-3-5-haiku-20241022
• claude-3-opus-20240229
• claude-3-sonnet-20240229
• claude-3-haiku-20240307

OpenAI Models

• gpt-4o
• gpt-4o-2024-11-20
• gpt-4o-2024-08-06
• gpt-4o-2024-05-13

Google Models

• gemini-1-5-pro
• gemini-1-5-flash (default)

Custom Models

You can add your own models or override existing ones using a JSON configuration file:

from token_vision import ImageTokenCalculator
from token_vision.models import load_custom_models

# Load custom models
load_custom_models("path/to/custom_models.json")

# Use custom model
calculator = ImageTokenCalculator()
tokens = calculator.calculate("image.jpg", model="custom-model-v1")

Example custom_models.json:

{
    "custom-provider": {
        "name": "Custom Provider",
        "max_images": 10,
        "token_multiplier": 1.0,
        "models": {
            "custom-model-v1": {
                "name": "Custom Model V1",
                "input_rate": 15000,
                "output_rate": 75000,
                "batch_input_rate": 7500,
                "batch_output_rate": 37500,
                "cached_input_rate": 1500,
                "cached_output_rate": 7500
            }
        }
    }
}

Advanced Usage

# Custom configuration
calculator = ImageTokenCalculator(
    default_model="gemini-1-5-flash",
    detail_level="high"
)

# Process multiple images
results = calculator.calculate_batch([
    "image1.jpg",
    "image2.jpg"
])

# Get batch costs (uses batch rates)
costs = calculator.get_cost_batch(results)

# Using numpy arrays
import numpy as np
image_array = np.array(...)  # Your image data
result = calculator.calculate(
    image=image_array,
    model="gemini-1-5-flash",
    detail_level="low"
)

Development

Setup

1. Clone the repository:

git clone https://github.com/nerveband/token-vision.git
cd token-vision

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install development dependencies:

pip install -e ".[dev]"

Running Tests

pytest

License

This project is licensed under the MIT License - see the LICENSE file for details.