Multimodal images
Radium supports vision-capable models. Pass images as base64-encoded data URLs or public URLs to get descriptions, OCR, visual classifications, and more.The script
vision.py
Run it
Processing multiple images in parallel
batch_vision.py
Tips
- Use
clarke-1.0for vision tasks — it has strong image understanding. - Compress images to < 1MB before base64 encoding to stay within payload limits (unverified limit).
- Use
"detail": "low"for simple classification (faster, fewer tokens) and"high"** for OCR or detailed analysis (OpenAI-specific parameters — verify Radium support). - Video frames — extract 1 frame per second, then batch classify with the example above.
- URL vs base64 — URLs reduce payload size but add a network hop; base64 is more reliable for local files.
Next steps
Batch processing
Process large image datasets in parallel
Structured extraction
Extract structured data from image descriptions