Invoice with line items

Extracting metadata and line items from invoices with examples in Python and Node.

Overview

This guide covers how to extract metadata and line items from multiple supplier invoices with examples in Python and Node.

You will extract the following metadata fields:

Name of the supplier
Name of the receiver
Invoice number
Purchase order number
Issue date
Pay due date
Total amount

You will extract the following line item fields:

Product number
Product description
Quantity
Price

This guide shows you how to

Getting your API Key

The Authorization header for your API key is: Token YOUR-API-KEY (Login if you do not see one). You can also obtain the API key by visiting the Settings page.

1. Create a new document type

Before you start extracting data, you need to define a document type. Navigate to the Dashboard page and click on the New document type button in the top right corner of the table. Next, select the Line items invoice card. The wizard will already pre-fill all the needed extraction fields along with the document type configuration. Click on the Create document type.

This will create a new document type with name line-item-invoice with the following fields:

supplier_name
invoice_number
purchase_order_number
receiver_name
issue_date
pay_due_date
total_amount

and line items fields:

product_description
product_number
price
quantity

2. Add suppliers

Once your document type is created, you need to add data to the dataset of your document type. To do that, download the following invoices:

Each invoice is from a different supplier.

To add a document to the dataset, use the add-document endpoint or use the training room, where you can easily upload a file and fill out the necessary information.

The dataset is created by uploading an original file with the correct value for each field defined inside the document type.

Example code:

1 Open file as base64 string (Lines 6-7)

Make sure that you are pointing to the correct path.

2 Specify payload (Lines 9-65)

3 Specify headers (Lines 70-74)

4 Make POST request (Lines 76-78)

import requests
import base64

file_name = 'amazing_company_1.pdf'

with open(file_name, 'rb') as file:
    base64_data = base64.b64encode(file.read()).decode('utf-8')

payload = {
    "file": base64_data,
    "file_name": file_name,
    "document_type_name": "line-item-invoice",
    "learning_fields": [
        {
            "name": "supplier_name",
            "value": "Amazing Company"
        },
        {
            "name": "receiver_name",
            "value": "Amazing Client"
        },
        {
            "name": "invoice_number",
            "value": "333"
        },
        {
            "name": "purchase_order_number",
            "value": "234778"
        },
        {
            "name": "pay_due_date",
            "value": "2021-03-31"
        },
        {
            "name": "issue_date",
            "value": "2021-02-01"
        },
        {
            "name": "total_amount",
            "value": "15.0000"
        }
    ],
    "line_items": [
        [
            {
                "name": "product_number",
                "value": None
            },
            {
                "name": "product_description",
                "value": "Amazing service"
            },
            {
                "name": "quantity",
                "value": "1"
            },
            {
                "name": "price",
                "value": "15.0000"
            }

        ]

    ]
}


url = "https://developers.typless.com/api/add-document"

headers = {
    "Accept": "application/json",
    "Content-Type": "application/json",
    "Authorization": "<<apikey>>"
}

response = requests.request("POST", url, json=payload, headers=headers)

print(response.json())

const fetch = require('node-fetch');
const fs = require('fs');

const fileName = 'amazing_company_1.pdf';
const base64File = fs.readFileSync(fileName, {encoding: 'base64'});

let url = 'https://developers.typless.com/api/add-document';

const payload = {
  file: base64File,
  file_name: fileName,
  document_type_name: "line-item-invoice",
  learning_fields: [
    {
      name: "supplier_name",
      value: "Amazing Company"
    },
    {
      name: "receiver_name",
      value: "Amazing Client"
    },
    {
      name: "invoice_number",
      value: "333"
    },
    {
      name: "purchase_order_number",
      value: "234778"
    },
    {
      name: "pay_due_date",
      value: "2021-03-31"
    },
    {
      name: "issue_date",
      value: "2021-02-01"
    },
    {
      name: "total_amount",
      value: "15.0000"
    }
  ],
  line_items: [
    [
      {
        name: "product_number",
        value: null
      },
      {
        name: "product_description",
        value: "Amazing service"
      },
      {
        name: "quantity",
        value: "1"
      },
      {
        name: "price",
        value: "15.0000"
      }

    ]

  ]
};

const headers = {
  'Accept': 'application/json',
  'Content-Type': 'application/json',
  'Authorization': '<<apikey>>'
}

let options = {
  method: 'POST',
  headers: headers,
  body: JSON.stringify(payload)
};

fetch(url, options)
  .then(res => res.json())
  .then(json => console.log(json))
  .catch(err => console.error('error:' + err));

Response:

{
    "details":["0cb9d3a652781bae68a2cba92a55ef308560bf4c"],
    "message":"Document added successfully."
}

As you can see, to achieve high accuracy, Typless only needs the values that are in the document. Nevertheless, there are some rules to keep in mind when providing values.

Applying these rules to the Amazing Company example, you changed three fields:

total_amount value was converted with number type rules from 15,00 to 15.0000
issue_date value was converted with date type rules from the word Feb 1, 2021, to 2021-02-01
pay_due_date value was converted with date type rules from the word Mar 31, 202,1 to 2021-03-31

The same rules were also applied to the Good Services example.

You will have two suppliers added to your document type after you run both code examples.

3. Execute training

👍 Training is executed automatically every day at 10 PM CET

For all of your suppliers with new documents in the dataset of all your document types. Free of charge

To immediately see results, you can trigger the training process on the Dashboard page. Look for the line-item-invoice document type in the list, and click on .

Need more information about training? Read more about training.

4. Extract data from documents

After the training is finished, you can start precisely extracting data from documents from trained suppliers. Here you have two new invoices from the trained suppliers:

Download them and extract the data using the code:

1 Open file as base64 string (Lines 6-7)

Make sure that you are pointing to the correct path.

2 Specify payload (Lines 8-12)

3 Specify headers (Lines 16-20)

4 Make POST request (Lines 22-24)

import requests
import base64

file_name = 'amazing_company_2.pdf'
with open(file_name, 'rb') as file:
    base64_data = base64.b64encode(file.read()).decode('utf-8')

payload = {
    "file": base64_data,
    "file_name": file_name,
    "document_type_name": "line-item-invoice"
}

url = "https://developers.typless.com/api/extract-data"

headers = {
    "Accept": "application/json",
    "Content-Type": "application/json",
    "Authorization": "<<apikey>>"
}

response = requests.request("POST", url, json=payload, headers=headers)

for field in response.json()['extracted_fields']:
    print(f'{field["name"]}: {field["values"][0]["value"]}')

const fetch = require('node-fetch');
const fs = require('fs');

const fileName = 'amazing_company_2.pdf';
const base64File = fs.readFileSync(fileName, {encoding: 'base64'});

const url = 'https://developers.typless.com/api/extract-data';

const payload = {
  file: base64File,
  file_name: fileName,
  document_type_name: "line-item-invoice"
}

const headers = {
  'Accept': 'application/json',
  'Content-Type': 'application/json',
  'Authorization': '<<apikey>>'
}

let options = {
  method: 'POST',
  headers: headers,
  body: JSON.stringify(payload)
};

fetch(url, options)
  .then(res => res.json())
  .then(json => {
      json.extracted_fields.forEach(field => console.log(`${field.name}: ${field.values[0].value}`))
      json.line_items.forEach(
        line_item => {
          console.log('Line item')
          line_item.forEach(field => console.log(`${field.name}: ${field.values[0].value}`))
        }
      )
    }
  )
  .catch(err => console.error('error:' + err));

Response:

// Example extraction response - the provided recipe will not produce equal results
{
    "file_name": "invoice_2.pdf",
    "object_id": "1cb25cc8-c9fa-4149-9a83-b4ed6a2173b9",
    "extracted_fields": [
        {
            "name": "supplier",
            "values": [
                {
                    "x": -1,
                    "y": -1,
                    "width": -1,
                    "height": -1,
                    "value": "ScaleGrid",
                    "confidence_score": "0.968",
                    "page_number": -1
                }
            ],
            "data_type": "AUTHOR"
        },
        {
            "name": "invoice_number",
            "values": [
                {
                    "x": 1989,
                    "y": 545,
                    "width": 323,
                    "height": 54,
                    "value": "20190500005890",
                    "confidence_score": "0.250",
                    "page_number": 0
                },
                {
                    "x": 167,
                    "y": 574,
                    "width": 391,
                    "height": 54,
                    "value": "GB123456789",
                    "confidence_score": "0.250",
                    "page_number": 0
                }
            ],
            "data_type": "STRING"
        },
        {
            "name": "issue_date",
            "values": [
                {
                    "x": 2072,
                    "y": 628,
                    "width": 240,
                    "height": 54,
                    "value": "2019-06-05",
                    "confidence_score": "0.358",
                    "page_number": 0
                }
            ],
            "data_type": "DATE"
        },
        {
            "name": "total_amount",
            "values": [
                {
                    "x": 2146,
                    "y": 1196,
                    "width": 126,
                    "height": 54,
                    "value": "47.5300",
                    "confidence_score": "0.990",
                    "page_number": 0
                }
            ],
            "data_type": "NUMBER"
        }
    ],
    "line_items": [
        [
            {
                "name": "Description",
                "values": [
                    {
                        "x": 208,
                        "y": 1196,
                        "width": 1022,
                        "height": 50,
                        "value": "5/2019-MongoBackend-MgmtStandalone-Small-744 hours",
                        "confidence_score": "0.661",
                        "page_number": 0
                    }
                ],
                "data_type": "STRING"
            },
            {
                "name": "Price",
                "values": [
                    {
                        "x": 2146,
                        "y": 1196,
                        "width": 126,
                        "height": 54,
                        "value": "47.5300",
                        "confidence_score": "0.582",
                        "page_number": 0
                    }
                ],
                "data_type": "NUMBER"
            },
            {
                "name": "Quantity",
                "values": [
                    {
                        "x": 1979,
                        "y": 1196,
                        "width": 23,
                        "height": 54,
                        "value": "1",
                        "confidence_score": "0.647",
                        "page_number": 0
                    }
                ],
                "data_type": "NUMBER"
            }
        ]
    ],
    "customer": null
}

Need a more in-depth explanation of the response? You can read about it here.

5. Continuously improve models

Typless embraces the fact that the world is changing all the time. That's why you can improve models on the fly by providing correct data after extraction. Let's say your company has a new partner, Best Supplier. You don't need to start over with building the dataset. You can simply extract and send the correct data after they are verified by your users. You can learn more about providing feedback on the building dataset page.

📘 Closed workflow loop - improve models live!

Use every action from your users to adapt and improve Typless models without any extra costs.

To send feedback, use the add-document-feedback with the object_id.

Running Typless live

The only thing that you need to do to automate your manual data entry is to integrate those simple API calls into your system.

Have any questions or need some help? Contact us via chat or email support@typless.com

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