Extract invoice fields into JSON

The problem

Your agent may miss or mix up invoice details when reading a scan or a document with an unfamiliar layout. It needs a tool that can read the invoice and separate fields such as the invoice number, tax, and total due. Without that, the information sent to your accounting process may be wrong.

How NativePort helps

NativePort uses Mistral’s document-reading service to read invoices and collect details such as the invoice number, date, and total. Your agent can use those results to prepare the information for your accounting process. You can check the collected values against the invoice before accepting them.

Let your agent build it

You don’t need to write this code yourself. Copy the prompt and paste it into your agent. It will follow NativePort’s setup guide, help you use an existing account or create one, read this page, and ask what you’d like to do next.

View prompt

Help me use NativePort for this use case.

1. Read https://nativeport.ai/SKILL.md and follow its instructions to learn how to use NativePort. Save or update the skill if your environment supports it.

2. Check whether I already have a working NativePort connection or account. Reuse it if available. Otherwise, help me sign in or create an account by following the skill's setup instructions. Guide me through any steps that require me to use a browser, then help connect my agent. Keep credentials private.

3. Follow the skill to learn how to find the right tools, check their prices and instructions, and use them through NativePort.

4. Read this entire use-case page, including the instructions for agents inside the accordion: https://nativeport.ai/use-cases/invoice-to-json/

Once setup is complete and you have read the page, briefly explain what you can help me do and ask me what I would like to do next. Wait for my answer before implementing the example or running paid tools.

Instructions for the agents - You don't need to read this

Technical implementation

Use Python 3 with requests installed (python -m pip install requests). Set NATIVEPORT_API_KEY in your environment to your NativePort key. Run the snippets on your server, where your key stays private.

Set DOCUMENT_URL to an HTTPS URL the document service can fetch, such as a time-limited file link. The example reads page 0 (the first page). Select only pages you need, up to 100 per request; split longer documents into batches. Send the URL, not inline file bytes.

python

import os
import requests

BASE = "https://api.nativeport.ai"
HEADERS = {"Authorization": f"Bearer {os.environ['NATIVEPORT_API_KEY']}"}

payload = {
    "document": {"type": "document_url", "document_url": os.environ["DOCUMENT_URL"]},
    "pages": [0],
}
schema = {'type': 'object',
 'properties': {'invoice_number': {'type': ['string', 'null']},
                'supplier': {'type': ['string', 'null']},
                'invoice_date': {'type': ['string', 'null']},
                'currency': {'type': ['string', 'null']},
                'subtotal': {'type': ['string', 'null']},
                'tax': {'type': ['string', 'null']},
                'total_due': {'type': ['string', 'null']}},
 'required': ['invoice_number',
              'supplier',
              'invoice_date',
              'currency',
              'subtotal',
              'tax',
              'total_due'],
 'additionalProperties': False}
payload["document_annotation_format"] = {
    "type": "json_schema",
    "json_schema": {"name": "extraction", "strict": True, "schema": schema},
}
payload["document_annotation_prompt"] = (
    'Extract the named invoice fields. Preserve printed amounts and currency.'
    ' Distinguish subtotal, tax, and total due. Use null when absent or '
    'unreadable.'
)
response = requests.post(BASE + "/mistral/v1/ocr", headers=HEADERS,
                         json=payload, timeout=120)
response.raise_for_status()
result = response.json()
import json
fields = result.get("document_annotation")
if isinstance(fields, str):
    fields = json.loads(fields)
if fields is None:
    raise ValueError("No document annotation returned; inspect the OCR response")
print(fields)

Validate the currency and reconcile subtotal, tax, and total in your application before posting to accounting. The extraction does not approve a payment. Structured annotations use the Document AI rate. See the Mistral request policy for document URLs, page selection, and accepted options. OCR returns recognized content; check unclear scans against the source.

Tool costs

Tool used in the example	Price per call
Mistral — structured extraction	$0.005275 / call (1 page)

Prices in USD. Usage-based tools have no fixed per-call price. View pricing.

The problem

How NativePort helps

Let your agent build it

Technical implementation

Tool costs

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