> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Table descriptions

<iframe width="560" height="315" src="https://www.youtube.com/embed/3UIW5PDck74" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen />

After partitioning, you can have Unstructured generate text-based summaries of detected tables.

This summarization is done by using models offered through various model providers.

Here is an example of the output of a detected table using GPT-4o. Note specifically the `text` field that is added.
Line breaks have been inserted here for readability. The output will not contain these line breaks.

```json theme={null}
{
    "type": "Table",
    "element_id": "5713c0e90194ac7f0f2c60dd614bd24d",
    "text": "The table consists of 6 rows and 7 columns. The columns represent 
        inhibitor concentration (g), bc (V/dec), ba (V/dec), Ecorr (V), icorr 
        (A/cm\u00b2), polarization resistance (\u03a9), and corrosion rate 
        (mm/year). As the inhibitor concentration increases, the corrosion 
        rate generally decreases, indicating the effectiveness of the 
        inhibitor. Notably, the polarization resistance increases with higher 
        inhibitor concentrations, peaking at 6 grams before slightly 
        decreasing. This suggests that the inhibitor is most effective at 
        6 grams, significantly reducing the corrosion rate and increasing 
        polarization resistance. The data provides valuable insights into the 
        optimal concentration of the inhibitor for corrosion prevention.",
    "metadata": {
        "text_as_html": "<table>...<full results omitted for brevity>...</table>",
        "filetype": "application/pdf",
        "languages": [
            "eng"
        ],
        "page_number": 1,
        "image_base64": "/9j...<full results omitted for brevity>...//Z",
        "image_mime_type": "image/jpeg",
        "filename": "7f239e1d4ef3556cc867a4bd321bbc41.pdf",
        "data_source": {}
    }
}
```

<Note>
  The `image_base64` field is generated only for documents or PDF pages that are [partitioned](/concepts/partitioning) by using the High Res strategy. This field is not generated for
  documents or PDF pages that are partitioned by using the Fast or VLM strategy.
</Note>

Here are two examples of the descriptions for detected tables. These descriptions are generated with GPT-4o by OpenAI:

<img src="https://mintcdn.com/unstructured-53/ognmPfo7rw6i-YTz/img/enriching/Table-Description-1.png?fit=max&auto=format&n=ognmPfo7rw6i-YTz&q=85&s=35d5ed3a55a463deb9450e6841e15a89" alt="Description of a table with information about endoscopic datasets" width="2978" height="562" data-path="img/enriching/Table-Description-1.png" />

<img src="https://mintcdn.com/unstructured-53/ognmPfo7rw6i-YTz/img/enriching/Table-Description-2.png?fit=max&auto=format&n=ognmPfo7rw6i-YTz&q=85&s=69356d0e8ff919facc848645ca04e2df" alt="Description of a table with information about potentiodynamic polarization of stainless steel" width="3108" height="544" data-path="img/enriching/Table-Description-2.png" />

The generated table's summary will overwrite any text that Unstructured had previously extracted from that table into the `text` field.
The table's original content is available in the `image_base64` field.

<Note>
  The `image_base64` field is generated only for documents or PDF pages that are [partitioned](/concepts/partitioning) by using the High Res strategy. This field is not generated for
  documents or PDF pages that are partitioned by using the Fast or VLM strategy.
</Note>

For workflows that use [chunking](/concepts/chunking), note the following changes:

* If a `Table` element must be chunked, the `Table` element is replaced by a set of related `TableChunk` elements.
* Each of these `TableChunk` elements will contain a summary description only for its own element, as part of the element's `text` field.
* These `TableChunk` elements will not contain an `image_base64` field.

Any embeddings that are produced after these summaries are generated will be based on the new `text` field's contents.

## Generate table descriptions

To have Unstructured generate image descriptions, do the following:

* For **Unstructured UI** users, add an [Enrichment node](/ui/workflows#custom-workflow-node-types) of type **Image Description**
  to an Unstructured [custom workflow](/ui/workflows#create-a-custom-workflow).
* For **Unstructured API** users, add a [Table Description task](/api-reference/workflow/nodes/enrichment/enrichment-table-description). You add this task
  as either as an object in a `workflow_nodes` array
  (for curl) or as a `WorkflowNode` in a `WorkflowNodes` collection (for Python). This object or collection applies whenever you
  [create a workflow](/api-reference/api/workflow/create-workflow),
  [update a workflow](/api-reference/api/workflow/update-workflow), or
  [create an on-demand workflow job](/api-reference/api/job/create-job).

## Learn more

* <Icon icon="video" />  [How to Extract Data from Complex Tables](https://unstructured.io/events/how-to-extract-data-from-complex-tables)