# Document classification

> Sort incoming documents by type, file the ones decisio can stand behind on their own, and send only the unsure ones to a person.

Source: https://opendecisio.com/docs/use-cases/document-classification/

This script asks for the document’s type and files it only on a [certified answer](https://opendecisio.com/docs/glossary/#certified-answer):

 

```
import json
import urllib.request

QUESTION = {
    "type": "choice",
    "instructions": "What type of document is this?",
    "criteria": {
        "invoice": "A bill asking for payment",
        "contract": "An agreement between parties",
        "purchase_order": "An order placed with a supplier",
        "receipt": "A proof of payment",
        "other": "Anything else"
    }
}

def route(text):
    request = urllib.request.Request(
        "http://127.0.0.1:8000/v1/systemone",
        data=json.dumps({"state": text, "questions": {"type": QUESTION}}).encode(),
        headers={"Content-Type": "application/json"},
    )
    with urllib.request.urlopen(request) as response:
        answer = json.load(response)["answers"]["type"]
    # A certified answer acts on its own; anything else goes to a person.
    if answer.get("certified"):
        return f"file as {answer['choice']}"
    return "to a person"

print(route("INVOICE No. 2026-117. Amount due: 1,240.00 EUR by 30 November."))
```

 

Run against the CPU stand-in at v0.18.1 on 2026-10-19. It printed:

```
to a person
```

 

Until the [task](https://opendecisio.com/docs/glossary/#task) is certified (step 2), every answer goes to a person, as on the stand-in.

 

Good fit

- A fixed set of document types, and past documents sorted into them.
- Documents whose text fits the server’s length, or whose first pages decide the type.

Not a good fit

- Documents whose type depends on images or layout more than on their text.
- Very long documents read whole: the first read grows with the length ([Scale](https://opendecisio.com/docs/scale/#2-plan-for-the-first-read)).

 

## 1 Write the question

 

One `choice` [question](https://opendecisio.com/docs/glossary/#question), with a description for each type and an `other` type. Send the document’s text, or its first pages, as the text.

 

## 2 Teach it and certify it

 

Teach the question from past documents and certify it at your [error target](https://opendecisio.com/docs/glossary/#error-target).

 

Send at least 118 labelled documents. The server holds back half to certify, and a 5% certificate needs 59 of those. More examples certify a larger share.

 

```
POST /v1/tasks
{"id": "document-classification",
 "examples": [{"request": {"state": "<a past item of yours>", "questions": {"type": <the question above>}},
               "answer": "invoice"},
              ...],
 "certify": {"alpha": 0.05}}
```

 

A shape to fill in, not a command to run.

 

[Certify answers](https://opendecisio.com/docs/tasks/certify/#2-teach-the-task-with-a-certificate) runs this call on a server, with its answer.

 

## 3 Route on the certificate

 

- A certified answer files the document by its type.
- Everything else goes to a person.
- When a new kind of document arrives, certification is suspended until you certify again ([Watch for drift](https://opendecisio.com/docs/tasks/drift/)).

 

## What it did

 

- A recipe: decisio is measured on short texts, such as the intent sets of the support inbox; no run on long documents is published.

 

## What next

 

- [Scale](https://opendecisio.com/docs/scale/): long texts, and what the first read costs.
- [Requirements and limits](https://opendecisio.com/docs/limits/): how long a text each model reads.
- [Use cases](https://opendecisio.com/docs/use-cases/): the other recipes.