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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.

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Approach

Extract the contents of an element’s text_as_html JSON object, which is nested inside of its parent metadata object.

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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.

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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)

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See also

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