Vision models

Vision completions enable the processing of images alongside text, allowing for a wide range of applications such as image description, object recognition, and data extraction from visual content. By sending a combination of text prompts and image URLs, the model can provide insightful responses based on the visual input.

Vision Completions

You can provide images in two ways:

• Remote URL: Supply a publicly accessible URL pointing to the image.
• Base64 Encoding: Encode the image in base64 format and pass it in the image_url field.

Remote Image URL

Using Regolo ClientPythonCURL

import regolo

regolo.default_key = "YOUR_REGOLO_KEY"
regolo.default_chat_model = "Qwen2.5-VL-32B-Instruct"

print(regolo.static_chat_completions(messages=[{
    "role": "user",
    "content": [
        {
            "type": "text",
            "text": "What’s in this image?"
        },
        {
            "type": "image_url",
            "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/de/Colosseo_2020.jpg/960px-Colosseo_2020.jpg"},
            "format": "image/jpeg"
        }
    ]
}]))

import requests

url = "https://api.regolo.ai/v1/chat/completions"
payload = {
    "model": "Qwen2.5-VL-32B-Instruct",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What’s in this image?"
                },
                {
                    "type": "image_url",
                    "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/de/Colosseo_2020.jpg/960px-Colosseo_2020.jpg"},
                    "format": "image/jpeg"
                }
            ]
        }
    ]
}
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_REGOLO_KEY"
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

curl -X POST https://api.regolo.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_REGOLO_KEY" \
-d '{
    "model": "Qwen2.5-VL-32B-Instruct",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What’s in this image?"
                },
                {
                    "type": "image_url",
                    "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/de/Colosseo_2020.jpg/960px-Colosseo_2020.jpg"},
                    "format": "image/jpeg"
                }
            ]
        }
    ]
}'

Base64 Encoding

This script demonstrates how to encode a local image as Base64 and send it to a multimodal model (text + image) for analysis.

Replace 'YOUR-API-KEY' with your actual API key before running.

Python

import base64
import json
import requests
from pathlib import Path

API_URL = "https://api.regolo.ai/v1/chat/completions"
API_KEY = "YOUR-API-KEY"
MODEL = "gemma-3-27b-it"

IMAGE_PATH = Path("colosseo.jpg")

if not IMAGE_PATH.exists():
    raise FileNotFoundError(f"Image not found: {IMAGE_PATH.resolve()}")

with open(IMAGE_PATH, "rb") as f:
    image_bytes = f.read()

image_b64 = base64.b64encode(image_bytes).decode("utf-8")

payload = {
    "model": MODEL,
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What’s in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{image_b64}",
                        "format": "image/jpeg"
                    }
                }
            ]
        }
    ]
}

headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

print("Sending request to Regolo AI API...")
response = requests.post(API_URL, headers=headers, data=json.dumps(payload))

if response.status_code != 200:
    print(f"Error {response.status_code}:")
    print(response.text)
else:
    result = response.json()
    try:
        content = result["choices"][0]["message"]["content"]
        print("Model response:")
        print(content)
    except Exception:
        print("Unexpected response format:")
        print(response.text)