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Auriko supports vision through the OpenAI-compatible image_url content part. Pass an image URL or a base64 data URL in the content array of a user message.

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Prerequisites

  • An Auriko API key
  • Python 3.10+ with the OpenAI SDK (pip install openai) or the auriko SDK (pip install auriko)
    • OR Node.js 18+ with the OpenAI SDK (npm install openai) or @auriko/sdk (npm install @auriko/sdk)
  • A vision-capable model (e.g., gpt-4o, claude-sonnet-4-6, gemini-flash-latest)

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Analyze images from URLs

Pass an image URL as a content part in the user message: 
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AURIKO_API_KEY"],
    base_url="https://api.auriko.ai/v1",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What is in this image?"},
            {"type": "image_url", "image_url": {
                "url": "https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png"
            }},
        ],
    }],
    max_tokens=300,
)

print(response.choices[0].message.content)

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Analyze base64-encoded images

For local files or private images, encode the bytes as a data URL: 
import base64
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AURIKO_API_KEY"],
    base_url="https://api.auriko.ai/v1",
)

with open("chart.png", "rb") as f:
    b64 = base64.b64encode(f.read()).decode("utf-8")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Summarize the trend in this chart."},
            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}},
        ],
    }],
    max_tokens=500,
)

print(response.choices[0].message.content)

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Send multiple images

Send several images in a single request by adding multiple image_url content parts: 
messages=[{
    "role": "user",
    "content": [
        {"type": "text", "text": "Compare these two images."},
        {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png"}},
        {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"}},
    ],
}]

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Control image resolution

You can set detail on the image_url content part to control how much resolution the model uses: 
{
    "type": "image_url",
    "image_url": {
        "url": "https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png",
        "detail": "low",
    },
}
ValueBehavior
autoThe model decides based on image size (default)
lowFixed low-resolution processing, fewer tokens
highHigh-resolution processing, more tokens for fine detail
Use low for cost-sensitive workloads where fine detail isn’t needed. Use high when the model needs to read small text or distinguish fine visual features.

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Response shape

Vision responses use the standard ChatCompletionResponse shape. The model’s analysis appears in choices[0].message.content as text.

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Errors

SituationHTTPSDK error
Image too large for the model’s context window400BadRequestError
Model doesn’t support vision400BadRequestError
Some models accept image URLs directly. Others require Auriko to process the image first, which adds these constraints:
SituationHTTPSDK error
Image URL unreachable400BadRequestError
Total image data exceeds 30 MB400BadRequestError
More than 1,500 images in one request400BadRequestError
Image URL isn’t HTTPS400BadRequestError
Unsupported image format400BadRequestError
URL resolution behavior varies by model. For consistent results across models, use base64-encoded images.
Check Supported parameters for the accepted content part types and see Error codes for the full error taxonomy.