dim-for-python 0.2.2

Vectorization with LLMs for Python

dim-python

A Python binding for dim-rs, providing efficient text and image vectorization using OpenAI models.

Installation

pip install dim-for-python

Features

• Async text vectorization using OpenAI's language models
• Async image vectorization using OpenAI's vision models
• Concurrent processing for improved performance
• Custom API endpoint support
• Type hints included

Requirements

• Python 3.7+
• OpenAI API key
• Rust toolkit (for building from source)

Usage

Text Vectorization

import asyncio
from dim_for_python import vectorize_string

async def main():
    # Initialize text and prompts
    text = "The quick brown fox jumps over the lazy dog"
    prompts = [
        "What is the mood of this text?",
        "What actions are described in this text?"
    ]

    # Get vector representation
    vector = await vectorize_string(
        string=text,
        prompts=prompts,
        model="gpt-4",
        api_key="your-api-key"
    )

    print(f"Vector dimension: {len(vector)}")
    print(f"Vector: {vector}")

# Run the async function
asyncio.run(main())

Image Vectorization

import asyncio
from dim_for_python import vectorize_image
from PIL import Image
import io

async def main():
    # Load and prepare image
    image = Image.open("path/to/image.jpg")
    img_byte_arr = io.BytesIO()
    image.save(img_byte_arr, format='PNG')
    img_bytes = img_byte_arr.getvalue()

    # Define prompts for image analysis
    prompts = [
        "What is the main subject of this image?",
        "Describe the colors in this image"
    ]

    # Get vector representation
    vector = await vectorize_image(
        image_bytes=img_bytes,
        prompts=prompts,
        model="gpt-4-vision-preview",
        api_key="your-api-key"
    )

    print(f"Vector dimension: {len(vector)}")
    print(f"Vector: {vector}")

# Run the async function
asyncio.run(main())

Using Custom API Endpoint

vector = await vectorize_string(
    string="Your text here",
    prompts=["Your prompt"],
    model="gpt-4",
    api_key="your-api-key",
    base_url="https://your-custom-endpoint.com"
)

API Reference

vectorize_string

async def vectorize_string(
    string: str,
    prompts: List[str],
    model: str,
    api_key: str,
    base_url: Optional[str] = None
) -> List[float]

Parameters:

• string: The input text to vectorize
• prompts: List of prompts to guide the vectorization
• model: OpenAI model identifier
• api_key: Your OpenAI API key
• base_url: Optional custom API endpoint

Returns:

• List of floating-point numbers representing the text vector

vectorize_image

async def vectorize_image(
    image_bytes: bytes,
    prompts: List[str],
    model: str,
    api_key: str,
    base_url: Optional[str] = None
) -> List[float]

Parameters:

• image_bytes: Raw bytes of the image
• prompts: List of prompts to guide the vectorization
• model: OpenAI model identifier
• api_key: Your OpenAI API key
• base_url: Optional custom API endpoint

Returns:

• List of floating-point numbers representing the image vector

Development

Building from source

# Clone the repository
git clone https://github.com/yourusername/dim-python
cd dim-python

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install development dependencies
pip install maturin pytest pytest-asyncio Pillow

# Build and install in development mode
maturin develop

Running tests

pytest tests/ -v

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

This project is a Python binding for dim-rs, which provides the core functionality for text and image vectorization.