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Documentation Index

Fetch the complete documentation index at: https://docs.vlm.run/llms.txt

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Generate comprehensive, contextual captions for images using state-of-the-art vision-language models. Perfect for accessibility, content management, and automated image analysis workflows.
Image captioning example showing detailed scene description

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Example Response

This is an example of the response from the Chat Completions API example (using the image shown above): 
A classic, light turquoise Volkswagen Beetle with chrome accents is parked on a cobblestone street, set against a warm yellow stucco wall with rustic brown wooden doors and windows.
Tags: car, volkswagen, beetle, street, cobblestone, wooden, doors, windows

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Usage Example

For best results, we recommend using the Structured Outputs API to get responses in a structured and validated data format.
from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Caption the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {"role": "user",
        "content": [
          {"type": "text", "text": "Generate a detailed caption for this image"},
          {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg", "detail": "auto"}}
        ]
      }
    ],
)

# Print the response
print(response.choices[0].message.content)
# >> "A classic, light turquoise Volkswagen Beetle..."

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FAQ

You can ask simply ask for a more detailed caption by providing a more detailed prompt. In most cases, you can provide the number of words you want the caption to be, and the model will generate a more detailed caption.
  • Common Objects: person, car, truck, bus, bicycle, motorcycle
  • Scenes: street, building, park, forest, beach, etc.
  • Time-of-Day: morning, afternoon, evening, night
  • Weather: sunny, cloudy, rainy, snowing, etc.
The tags come in the format of a list of strings.