Modèles vision-langage : Guide pratique pour développeurs

What Are Vision-Language Models?

Vision-language models (VLMs) can understand both images and text simultaneously. They enable applications like image captioning, visual Q&A, and document analysis.

Key Models

• GPT-4o: Excellent at document understanding, chart analysis, and general vision tasks.
• Gemini 2.0: Strong at multi-image reasoning and video understanding.
• Claude 3.5 Sonnet: Good at scientific diagram interpretation.
• LLaVA: Open-source option for self-hosting.

Common Use Cases

• Document OCR: Extract structured data from invoices, receipts, forms.
• Product Analysis: Upload a product photo to get descriptions, tags, and categories.
• Accessibility: Generate alt text for images automatically.
• Content Moderation: Detect inappropriate visual content.
• Chart Understanding: Extract data points from charts and graphs.

Integration Patterns

### Basic Image Analysis

Send an image as base64 or URL along with a text prompt.

### Multi-Image Comparison

Send multiple images and ask the model to compare or rank them.

### Structured Output

Combine vision with JSON mode to extract structured data from images.

Tips

• Resize images to 1024px max dimension to save tokens.
• Use JPEG over PNG for photos (smaller payload).
• Always provide specific prompts — "What is in this image?" gives vague results.
• Test with diverse inputs to catch edge cases.