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After partitioning, you can have Unstructured generate representations of each detected table in HTML markup format. This table-to-HTML output is generated by using agent AI or a vision language model (VLM). The agentic AI option typically provides more accurate table-to-HTML output than the VLM option. Here is an example of the HTML markup output of a detected table using GPT-4o. Note specifically the text_as_html field that is added. Line breaks have been inserted here for readability. The output will not contain these line breaks. 
{
    "type": "Table",
    "element_id": "31aa654088742f1388d46ea9c8878272",
    "text": "Inhibitor Polarization Corrosion be (V/dec) ba (V/dec) Ecorr (V) icorr 
        (AJcm?) concentration (g) resistance (Q) rate (mmj/year) 0.0335 0.0409 
        \u20140.9393 0.0003 24.0910 2.8163 1.9460 0.0596 .8276 0.0002 121.440 
        1.5054 0.0163 0.2369 .8825 0.0001 42121 0.9476 s NO 03233 0.0540 
        \u20140.8027 5.39E-05 373.180 0.4318 0.1240 0.0556 .5896 5.46E-05 
        305.650 0.3772 = 5 0.0382 0.0086 .5356 1.24E-05 246.080 0.0919",
    "metadata": {
        "text_as_html": "```html\n
            <table>\n
                <tr>\n<th>Inhibitor concentration (g)</th>\n
                    <th>bc (V/dec)</th>\n<th>ba (V/dec)</th>\n<th>Ecorr (V)</th>\n
                    <th>icorr (A/cm\u00b2)</th>\n<th>Polarization resistance (\u03a9)</th>\n
                    <th>Corrosion rate (mm/year)</th>\n
                </tr>\n  
                <tr>\n
                    <td>0</td>\n<td>0.0335</td>\n<td>0.0409</td>\n<td>\u22120.9393</td>\n
                    <td>0.0003</td>\n<td>24.0910</td>\n<td>2.8163</td>\n  
                </tr>\n
                <tr>\n   
                    <td>2</td>\n<td>1.9460</td>\n<td>0.0596</td>\n<td>\u22120.8276</td>\n<td>0.0002</td>\n<td>121.440</td>\n<td>1.5054</td>\n  
                </tr>\n
                <tr>\n
                    <td>4</td>\n<td>0.0163</td>\n<td>0.2369</td>\n<td>\u22120.8825</td>\n<td>0.0001</td>\n<td>42.121</td>\n<td>0.9476</td>\n  
                </tr>\n  
                <tr>\n
                    <td>6</td>\n<td>0.3233</td>\n<td>0.0540</td>\n<td>\u22120.8027</td>\n<td>5.39E-05</td>\n<td>373.180</td>\n<td>0.4318</td>\n  
                </tr>\n  
                <tr>\n
                    <td>8</td>\n<td>0.1240</td>\n<td>0.0556</td>\n<td>\u22120.5896</td>\n<td>5.46E-05</td>\n<td>305.650</td>\n<td>0.3772</td>\n  
                </tr>\n  
                <tr>\n
                    <td>10</td>\n<td>0.0382</td>\n<td>0.0086</td>\n<td>\u22120.5356</td>\n<td>1.24E-05</td>\n<td>246.080</td>\n<td>0.0919</td>\n
                </tr>\n
            </table>\n```",
        "filetype": "application/pdf",
        "languages": [
            "eng"
        ],
        "page_number": 1,
        "image_base64": "/9j...<full results omitted for brevity>...//Z",
        "image_mime_type": "image/jpeg",
        "filename": "embedded-images-tables.pdf",
        "data_source": {}
    }
}
The image_base64 field is generated only for documents or PDF pages that are partitioned 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, 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 HTML table output for only its own element.
  • None of the these TableChunk elements will contain an image_base64 field.

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Generate table-to-HTML output

To generate table-to-HTML output, for an Enrichment node in a workflow, do the following:
  1. For Input Type, click Table.
  2. To use agentic AI to generate the table-to-HTML output, for Provider, click Agentic Table Parsing. To use a VLM to generate the table-to-HTML output instead, do the following: a. For Provider, click Anthropic or OpenAI.
    b. For Model, click one of the available models that are shown.
    c. For Task, click Table to HTML.
You can change a workflow’s table description settings only through Custom workflow settings.For workflows that use chunking, the Chunker node should be placed after all Enrichment nodes. Placing the Chunker node before a table-to-HTML output Enrichment node could cause incomplete or no table-to-HTML output 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-to-HTML output 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-to-HTML output node is added.
  • With a Partitioner node set to use the VLM partitioning strategy. No table-to-HTML output node is needed (or allowed).
Even with these configurations, Unstructured actually generates table-to-HTML output 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-to-HTML output 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.

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