> ## 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.
# Extract tables as HTML
The following information applies to the legacy Unstructured Partition Endpoint.
Unstructured recommends that you use the
[on-demand jobs](/api-reference/workflow/overview#run-an-on-demand-job) functionality in the
[Unstructured API](/api-reference/overview) instead. Unstructured's on-demand jobs provide
many benefits over the legacy Unstructured Partition Endpoint, including support for:
* Production-level usage.
* Multiple local input files in batches.
* The latest and highest-performing models.
* Post-transform enrichments.
* All of Unstructured's chunking strategies.
* The generation of vector embeddings.
The Unstructured API also provides support for processing files and data in remote locations.
## Task
You want to get, save, or show the contents of elements that are represented as HTML, such as tables that are embedded in a PDF document.
## Approach
Extract the contents of an element's `text_as_html` JSON object, which is nested inside of its parent `metadata` object.
## To run this example
You will need a document that is one of the document types that can output the `text_as_html` JSON object. For the list of applicable document types, see the entries in the table at the beginning of [Partitioning](/open-source/core-functionality/partitioning) where "Table Support" is "Yes."
This example uses a PDF file with an embedded table.
## Code
For the [Unstructured Python SDK](/api-reference/legacy-api/partition/sdk-python), you'll need:
These environment variables:
* `UNSTRUCTURED_API_KEY` - Your Unstructured API key value.
* `UNSTRUCTURED_API_URL` - Your Unstructured API URL.
```python Python SDK theme={null}
from unstructured_client import UnstructuredClient
from unstructured_client.models import operations, shared
from unstructured.staging.base import elements_from_dicts, elements_to_json
import os, webbrowser
if __name__ == "__main__":
client = UnstructuredClient(
api_key_auth=os.getenv("UNSTRUCTURED_API_KEY")
)
# Source: https://github.com/Unstructured-IO/unstructured-ingest/blob/main/example-docs/pdf/embedded-images-tables.pdf
# Where to get the local file, relative to this .py file.
local_input_filepath = "local-ingest-input-pdf/embedded-images-tables.pdf"
# Where to store the retrieved HTML (and the processed JSON), relative to this .py file.
local_output_filepath = "local-ingest-output"
with open(local_input_filepath, "rb") as f:
files = shared.Files(
content=f.read(),
file_name=local_input_filepath
)
request = operations.PartitionRequest(
shared.PartitionParameters(
files=files,
strategy=shared.Strategy.VLM,
vlm_model="gpt-4o",
vlm_model_provider="openai",
split_pdf_page=True,
split_pdf_allow_failed=True,
split_pdf_concurrency_level=15
)
)
try:
result = await client.general.partition_async(
request=request
)
# Provide some minimal CSS for better table readability.
table_css = ""
for element in result.elements:
if "text_as_html" in element["metadata"]:
# Surround the element's HTML with basic and tags, and add the minimal CSS.
html_string = f"{table_css}{element["metadata"]["text_as_html"]}"
# Save the element's HTML to a local file.
save_path = f"{local_output_path}/{element["element_id"]}.html"
file = open(save_path, 'w')
file.write(html_string)
file.close()
# View the locally saved file in the local default web browser.
webbrowser.open_new(f"file:///{os.getcwd()}/{save_path}")
# Also get the elements for inspection and validation.
dict_elements = elements_from_dicts(
element_dicts=result.elements
)
# Save the elements as JSON.
elements_to_json(
elements=dict_elements,
indent=2,
filename=f"{local_output_filepath}/embedded-images-tables.json"
)
except Exception as e:
print(e)
```
## See also
* [Extract images and tables from documents](/api-reference/legacy-api/partition/extract-image-block-types)
* [Table Extraction from PDF](/examplecode/codesamples/apioss/table-extraction-from-pdf)