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The following information applies to the legacy Unstructured Partition Endpoint.Unstructured recommends that you use the on-demand jobs functionality in the Unstructured API 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.

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Task

You want to get, decode, and show elements, such as images and tables, that are embedded in a PDF document.

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Approach

Extract the Base64-encoded representation of specific elements, such as images and tables, in the document. For each of these extracted elements, decode the Base64-encoded representation of the element into its original visual representation and then show it.

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To run this example

You will need a document that is one of the document types supported by the extract_image_block_types argument. See the extract_image_block_types entry in API Parameters. This example uses a PDF file with embedded images and tables.

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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
import base64
from PIL import Image
import io

if __name__ == "__main__":
    client = UnstructuredClient(
        api_key_auth=os.getenv("UNSTRUCTURED_API_KEY")
    )

    # Source: https://github.com/Unstructured-IO/unstructured/blob/main/example-docs/embedded-images-tables.pdf
    
    # Where to get the input file and store the processed data, relative to this .py file.
    local_input_filepath = "local-ingest-input-pdf/embedded-images-tables.pdf"
    local_output_filepath = "local-ingest-output/embedded-images-tables.json"

    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,
            split_pdf_page=True,
            split_pdf_allow_failed=True,
            split_pdf_concurrency_level=15,
            # Extract the Base64-encoded representation of each
            # processed "Image" and "Table" element. Extract each into
            # an "image_base64" object, as a child of the
            # "metadata" object, for that element in the result.
            # Element type names, such as "Image" and "Table" here,
            # are case-insensitive.
            # Any available Unstructured element type is allowed.
            extract_image_block_types=["Image", "Table"]
        )
    )

    try:
        result = client.general.partition(
            request=request
        )

        for element in result.elements:
            if "image_base64" in element["metadata"]:
                # Decode the Base64-encoded representation of the 
                # processed "Image" or "Table" element into its original
                # visual representation, and then show it.
                image_data = base64.b64decode(element["metadata"]["image_base64"])
                image = Image.open(io.BytesIO(image_data))
                image.show()
        
        # Optionally, prepare to print or save the elements as JSON.
        dict_elements = elements_from_dicts(
            element_dicts=result.elements
        )

        # Print the elements as JSON...
        json_elements = elements_to_json(
            elements=dict_elements,
            indent=2
        )

        print(json_elements)

        # ...or save as JSON.
        elements_to_json(
            elements=dict_elements,
            indent=2,
            filename=local_output_filepath
        )
    except Exception as e:
        print(e)

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See also