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Understand images ​

Type: Recipe

Outcome ​

Send text and an image in one provider-neutral user message. Use this for descriptions, visual question answering, or a first-pass review when the chosen model supports image input.

Prerequisites ​

  • @anvia/core, @anvia/openai, and a server-side OPENAI_API_KEY
  • A completion model whose capabilities.imageInput is true
  • An authorized, provider-reachable image URL

Implementation ​

ts
import { AgentBuilder } from '@anvia/core/agent'
import { Message, UserContent } from '@anvia/core/completion'
import { OpenAIClient } from '@anvia/openai'

const client = new OpenAIClient({ apiKey: process.env.OPENAI_API_KEY })
const model = client.completionModel('gpt-5')
if (!model.capabilities.imageInput) throw new Error('Selected model has no image input.')

const agent = new AgentBuilder('visual-reviewer', model)
  .instructions('Describe only visible evidence. State uncertainty clearly.')
  .build()

const response = await agent.prompt(Message.user([
  UserContent.text('Describe the scene in three bullets.'),
  UserContent.imageUrl('https://example.com/authorized-image.jpg', { detail: 'auto' }),
])).send()

console.log(response.output)

Run and expected behavior ​

Replace the URL with an accessible image, save the file, and run it with pnpm tsx. The response describes the visible content; wording and accuracy vary by model. An unsupported model is rejected by the explicit capability check or request validation.

Boundaries ​

Do not use visual output as proof of identity, authenticity, safety, diagnosis, or legal fact. Validate URL schemes and hosts to prevent SSRF-style access, use short-lived signed URLs for private assets, enforce size and media-type limits, and remove metadata when policy requires it.

In production, prefer bytes or controlled storage where supported, scan uploads, record user consent and retention, evaluate representative images, and route important decisions to deterministic checks or human review.

Source and extensions ​

Run the image attachment cookbook. Next, compare URL and base64 inputs or combine the result with a validated structured schema.

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