Images

One photo of a receipt and the expense claim it should back up, in one request. matches is a Truth question that compares three fields at once; currency is a Choice read straight off the print. Both are answered from a single read of the image.

Sending an image#

Add an images array next to the state. Each image has an id you choose, a media_type and the bytes as base64 in data, without a data: prefix. Refer to images by id in your instructions when there is more than one.

curl https://api.decisionnode.com/v1/decide \
  -H "Authorization: Bearer $DECISIONNODE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "decisionnode-latest",
    "state": {
      "claim": { "amount": 48.2, "currency": "EUR", "date": "2026-09-28" }
    },
    "images": [
      { "id": "receipt", "media_type": "image/jpeg", "data": "<base64>" }
    ],
    "questions": {
      "matches": {
        "type": "truth",
        "instructions": "Does the receipt show the same total, currency and date as the claim?",
        "criteria": {
          "true": "Total, currency and date on the receipt all match the claim",
          "false": "Any of them differs, or the receipt does not show it"
        }
      },
      "currency": {
        "type": "choice",
        "instructions": "Which currency is the receipt in?",
        "criteria": {
          "EUR": "euro",
          "GBP": "pound sterling",
          "USD": "US dollar"
        }
      }
    }
  }'

Image object

idstringrequired

Your name for the image, for example receipt or photo_1.

media_typestringrequired

image/jpeg, image/png or image/webp.

datastringrequired

The image bytes, base64 encoded, with no data: prefix.

Encoding a file#

Read the file's bytes, base64 encode them and put the text in data, with the media_type that matches the file. Each image may be up to 10 MB before encoding; the other limits are on Limits.

import base64, pathlib

raw = pathlib.Path("receipt.jpg").read_bytes()
data = base64.b64encode(raw).decode()
image = {"id": "receipt", "media_type": "image/jpeg", "data": data}

What the model reads#

• Printed and handwritten text, including totals, dates and reference numbers.
• Objects and their condition, as in a listing photo.
• Layout: which number is the total, which line is the date, whether a stamp or signature is present.
• Several images against each other, such as a photo and the document it should match.
• How many: cars in a drone frame, items on a shelf, with a Number question.