Extract tables as HTML

The following information applies to the legacy Unstructured Partition Endpoint.Unstructured recommends that you use the local-file processing jobs functionality in the Unstructured Pipeline API instead. Unstructured’s local-file processing 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 where “Table Support” is “Yes.” This example uses a PDF file with an embedded table.

Code

For the Unstructured Python SDK, you’ll need: These environment variables:

UNSTRUCTURED_API_KEY - Your Unstructured API key value.
UNSTRUCTURED_API_URL - Your Unstructured API URL.

Python SDK

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 = "<head><style>table, th, td { border: 1px solid; }</style></head>"

        for element in result.elements:
            if "text_as_html" in element["metadata"]:
                # Surround the element's HTML with basic <html> and <body> tags, and add the minimal CSS.
                html_string = f"<!DOCTYPE html><html>{table_css}<body>{element["metadata"]["text_as_html"]}</body></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)

Unstructured API

Pipeline API

API Reference

Legacy APIs

Troubleshooting

Task

Approach

To run this example

Code

See also

​Task

​Approach

​To run this example

​Code

​See also

Task

Approach

To run this example

Code

See also