Skip to main content

Official Python SDK for VLM Run

Project description

VLM Run Logo

VLM Run Python SDK

Website | Platform | Docs | Blog | Discord

PyPI Version PyPI Version PyPI Downloads
License Discord Twitter Follow

The VLM Run Python SDK is the official Python SDK for VLM Run API platform, providing a convenient way to interact with our REST APIs.

🚀 Getting Started

Installation

pip install vlmrun

Installation with Optional Features

The package provides optional features that can be installed based on your needs:

  • Chat with Orion via the CLI (see vlmrun chat)

    pip install "vlmrun[cli]"
    
  • Video processing features (numpy, opencv-python):

    pip install "vlmrun[video]"
    
  • Document processing features (pypdfium2):

    pip install "vlmrun[doc]"
    
  • OpenAI SDK integration (for chat completions API):

    pip install "vlmrun[openai]"
    
  • All optional features:

    pip install "vlmrun[all]"
    

Basic Usage

from PIL import Image
from vlmrun.client import VLMRun
from vlmrun.common.utils import remote_image

# Initialize the client
client = VLMRun(api_key="<your-api-key>")

# Process an image using local file or remote URL
image: Image.Image = remote_image("https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.invoice/invoice_1.jpg")
response = client.image.generate(
    images=[image],
    domain="document.invoice"
)
print(response)

# Or process an image directly from URL
response = client.image.generate(
    urls=["https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.invoice/invoice_1.jpg"],
    domain="document.invoice"
)
print(response)

OpenAI-Compatible Chat Completions

The VLM Run SDK provides OpenAI-compatible chat completions through the agent endpoint. This allows you to use the familiar OpenAI API with VLM Run's powerful vision-language models.

from vlmrun.client import VLMRun

client = VLMRun(
    api_key="your-key",
    base_url="https://agent.vlm.run/v1"
)

response = client.agent.completions.create(
    model="vlmrun-orion-1",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)

For async support:

import asyncio
from vlmrun.client import VLMRun

client = VLMRun(api_key="your-key", base_url="https://agent.vlm.run/v1")

async def main():
    response = await client.agent.async_completions.create(
        model="vlmrun-orion-1",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

Installation: Install with OpenAI support using pip install vlmrun[openai]

🔗 Quick Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

vlmrun-0.5.5.tar.gz (68.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

vlmrun-0.5.5-py3-none-any.whl (74.6 kB view details)

Uploaded Python 3

File details

Details for the file vlmrun-0.5.5.tar.gz.

File metadata

  • Download URL: vlmrun-0.5.5.tar.gz
  • Upload date:
  • Size: 68.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for vlmrun-0.5.5.tar.gz
Algorithm Hash digest
SHA256 348e6ee3560f7ea96207a5d38e09fd93c4c52305435574740c85e93845caa2dc
MD5 3aeb54be8a76585b8815e772c7c09c69
BLAKE2b-256 6e0f62972f63028aa13f45cae1e578523c63c7446af02d1e411b43e09725766f

See more details on using hashes here.

File details

Details for the file vlmrun-0.5.5-py3-none-any.whl.

File metadata

  • Download URL: vlmrun-0.5.5-py3-none-any.whl
  • Upload date:
  • Size: 74.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for vlmrun-0.5.5-py3-none-any.whl
Algorithm Hash digest
SHA256 7a7f44e038ae932967fea75693c11acfc6ccd393c42e764091d296c2d37c1668
MD5 ad5127a667d2c56936c65965b4df7ba3
BLAKE2b-256 90a3d7c729ff030fe6190f66fa1301aa139324f76b8b3e8df77a879e28ec4cd3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page