How do I add multimodal AI features with one API?

If you want multimodal AI with one API, the simplest path is to use a provider that exposes a single “vision-capable” model endpoint that accepts both text and images in the same request.

What “one API” usually means

A multimodal API can typically:

• take text prompts
• take images as input
• sometimes also handle audio, video, or files
• return text, structured data, or tool calls

So instead of wiring together separate OCR, captioning, and vision models, you send everything to one model call.

---

Basic approach

1) Pick a multimodal model

Choose a model that supports image understanding, for example a “vision” or “multimodal” model.

2) Send mixed inputs in one request

Your request usually contains:

• a text instruction
• one or more images, often as:
  • image URL
  • base64-encoded file
  • uploaded file reference

3) Ask for the output format you want

You can request:

• plain text
• JSON
• labels/tags
• extracted fields
• summary
• Q&A over an image

---

Example use cases

With one multimodal API, you can build:

• image captioning
• document extraction
• receipt/invoice parsing
• product recognition
• visual Q&A
• screenshot analysis
• diagram interpretation

---

Example pattern

A typical request looks like this conceptually:

{
  "model": "multimodal-model",
  "input": [
    {
      "type": "text",
      "text": "Describe this image and list any visible text."
    },
    {
      "type": "image",
      "image_url": "https://example.com/image.jpg"
    }
  ]
}

The response might be:

{
  "description": "A person holding a red umbrella in the rain...",
  "visible_text": ["Sale", "50% off"]
}

---

Tips for good results

• Be specific in the prompt: ask for exactly what you want.
• If extracting data, define a schema.
• Use high-quality images when possible.
• For documents, crop or preprocess to improve accuracy.
• If the model supports it, ask for structured JSON output.

---

If you’re building an app

A common flow is:

1. User uploads image
2. Backend sends image + prompt to multimodal API
3. Model returns analysis
4. App renders result, stores it, or passes it to business logic

---

If you want the fastest implementation

Use:

• a multimodal model endpoint
• image upload or URL support
• structured output mode if available

That’s usually enough to add vision features without building a separate computer-vision pipeline.

If you want, I can show you:

1. a Python example
2. a JavaScript example
3. a receipt/document extraction example
4. how to do it with a specific API/provider