> ## Documentation Index
> Fetch the complete documentation index at: https://unstructured-53-docs-245-multimodal.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Table descriptions
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": "
......
",
"filetype": "application/pdf",
"languages": [
"eng"
],
"page_number": 1,
"image_base64": "/9j......//Z",
"image_mime_type": "image/jpeg",
"filename": "7f239e1d4ef3556cc867a4bd321bbc41.pdf",
"data_source": {}
}
}
```
The `image_base64` field is generated only for documents or PDF pages that are [partitioned](/ui/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.
Here are two examples of the descriptions for detected tables. These descriptions are generated with GPT-4o by OpenAI:
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.
The `image_base64` field is generated only for documents or PDF pages that are [partitioned](/ui/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.
For workflows that use [chunking](/ui/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 generate table descriptions, in an **Enrichment** node in a workflow, select **Table**, and then choose one of the available provider (and model) combinations that are shown.
Make sure after you choose the provider and model, that **Table Description** is also displayed. If **Table Description** and **Table to HTML** are both
displayed, be sure to select **Table Description**.
You can change a workflow's table description settings only through [Custom](/ui/workflows#create-a-custom-workflow) workflow settings.
For workflows that use [chunking](/ui/chunking), the **Chunker** node should be placed after all **Enrichment** nodes. Placing the
**Chunker** node before a table descriptions **Enrichment** node could cause incomplete or no table descriptions to be generated.
The following models are no longer available as of the following dates:
* Amazon Bedrock Claude Sonnet 3.5: October 22, 2025
* Anthropic Claude Sonnet 3.5: October 22, 2025
Unstructured recommends the following actions:
* For new workflows, do not use any of these models.
* For any workflow that uses any of these models, update that workflow as soon as possible to use a different model.
Workflows that attempt to use any of these models on or after its associated date will return errors.
Unstructured can potentially generate table summary descriptions only for workflows that are configured as follows:
* With a **Partitioner** node set to use the **Auto** or **High Res** partitioning strategy, and a table summary description node is added.
* With a **Partitioner** node set to use the **VLM** partitioning strategy. No table summary description node is needed (or allowed).
Even with these configurations, Unstructured actually generates table summary descriptions only for files that contain tables and are also eligible
for processing with the following partitioning strategies:
* **High Res**, when the workflow's **Partitioner** node is set to use **Auto** or **High Res**.
* **VLM** or **High Res**, when the workflow's **Partitioner** node is set to use **VLM**.
Unstructured never generates table summary descriptions for workflows that are configured as follows:
* With a **Partitioner** node set to use the **Fast** partitioning strategy.
* With a **Partitioner** node set to use the **Auto**, **High Res**, or **VLM** partitioning strategy, for all files that Unstructured encounters that do not contain tables.
## Learn more
* [How to Extract Data from Complex Tables](https://unstructured.io/events/how-to-extract-data-from-complex-tables)
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.