> For the complete documentation index, see [llms.txt](https://docs.waveline.ai/extract/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.waveline.ai/extract/examples/order-table-extraction.md).

# Order Table Extraction

In this example, we are a company that sells many different products. Our customer sends us a small table of all the items they want to buy. However, since the customer creates the table, it can look slightly different every time. For example, they write Product ID instead of `product_id`, `Qty` instead of `Quantity`, and the shipping times have no clear structure.

<table><thead><tr><th width="188">Product ID</th><th width="133.33333333333331">Qty</th><th width="124">Unit price</th><th>Manufacturar</th><th>Shipping Time</th></tr></thead><tbody><tr><td>TZX22-EHZ2</td><td>100</td><td>2.76$</td><td>Tusp</td><td>1-2 days</td></tr><tr><td>ZUI23-772L6</td><td>250</td><td>5.00$</td><td>-</td><td>1 week</td></tr><tr><td>UIUU-13BMW</td><td>340'000</td><td>0.001$</td><td>puma</td><td>About a month</td></tr><tr><td>QUE2-AIME2</td><td>56</td><td>45.56$</td><td>lebra</td><td>Tomorrow</td></tr></tbody></table>

{% file src="/files/B7ZLbkf1MlEAzPskHf5S" %}

Waveline Extract makes it easy to unify these fields into one format we define.

Let's construct a [Shape](/extract/types/shape.md) to extract `product_id`, `unit_price`, `quantity`**,** and `shipping_time` for each product:

```json
[
  {
    "name": "products",
    "type": "object",
    "description": "All products from the table",
    "isArray": true,
    "elements": [
      {
        "name": "product_id",
        "type": "string",
        "description": "The id of that product. aka product number",
        "isArray": false
      },
      {
        "name": "quantity",
        "type": "number",
        "description": "Quantity of how many units. Aka Qty",
        "isArray": false
      },
      {
        "name": "unit_price",
        "type": "number",
        "description": "Unit price of that product in dollars",
        "isArray": false
      },
      {
        "name": "shipping_time",
        "type": "string",
        "description": "Time it takes to ship this product. (In days)",
        "isArray": false
      }
    ]
  }
]
```

We can now call the [`/extract-document`](/extract/endpoints/extract-document.md) endpoint with this shape and the table as the payload to create the job:

```bash
curl -X POST "https://waveline.ai/api/v1/extract-document" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer YOUR_API_KEY" \
     -d '{
          "fileName": "OrderTable.txt",
          "contentType": "application/pdf",
          "base64Content": "JVBERi0xLjMKMSAwIG9iago8PC9UeXBlL0NhdGF...",
          "shape": YOUR_SHAPE
        }'
```

After some time, we query for the result of this job with the [job](/extract/endpoints/jobs-id.md) endpoint and get the following in the `result` field:

```json
{
  "products": [
    {
      "product_id": "TZX22-EHZ2",
      "quantity": "100",
      "unit_price": 2.76,
      "shipping_time": "1-2 days"
    },
    {
      "product_id": "ZUI23-772L6",
      "quantity": "250",
      "unit_price": 5,
      "shipping_time": "1 week"
    },
    {
      "product_id": "UIUU-13BMW",
      "quantity": "340000",
      "unit_price": 0.001,
      "shipping_time": "About a month"
    },
    {
      "product_id": "QUE2-AIME2",
      "quantity": "56",
      "unit_price": 45.56,
      "shipping_time": "Tomorrow"
    }
  ]
}
```

As we can see above, all the fields have successfully been unified into the format we defined!


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