Official Python SDK for VLM Run
Project description
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
- 💬 Need help? Email us at support@vlm.run or join our Discord
- 📚 Check out our Documentation
- 📣 Follow us on Twitter and LinkedIn
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file vlmrun-0.5.4.tar.gz.
File metadata
- Download URL: vlmrun-0.5.4.tar.gz
- Upload date:
- Size: 67.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
533b2c057905f24086b133f0769f0b90c38bb97f6f4ad19ff5b46306b66ea27d
|
|
| MD5 |
11c07368aee78c5e4cfdbac29697fb47
|
|
| BLAKE2b-256 |
f2ae3b1b4b7e9319d8c3449e18a50c039cb2900575204a860aef37d7647b07cc
|
File details
Details for the file vlmrun-0.5.4-py3-none-any.whl.
File metadata
- Download URL: vlmrun-0.5.4-py3-none-any.whl
- Upload date:
- Size: 72.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
fa8183762684c64f5dca2e38bf98a8403ced9c4299cbc0c777f9c133a526df6b
|
|
| MD5 |
a0dcf4be43d94ba57767044376dc06ee
|
|
| BLAKE2b-256 |
b576b9bf8775652ddcb41514450d0db4767851420e9f0dc6ad833f4e580eaa91
|