Azure

Connect Azure to your preprocessing pipeline, and batch process all your documents using unstructured-ingest to store structured outputs locally on your filesystem.

First you’ll need to install the Azure dependencies as shown here.

pip install "unstructured[azure]"

Run Locally

#!/usr/bin/env bash

unstructured-ingest \
  azure \
  --remote-url abfs://container1/ \
  --account-name azureunstructured1 \
  --output-dir azure-ingest-output \
  --num-processes 2

from unstructured.ingest.connector.fsspec.azure import (
    AzureAccessConfig,
    SimpleAzureBlobStorageConfig,
)
from unstructured.ingest.interfaces import (
    PartitionConfig,
    ProcessorConfig,
    ReadConfig,
)
from unstructured.ingest.runner import AzureRunner

if __name__ == "__main__":
    runner = AzureRunner(
        processor_config=ProcessorConfig(
            verbose=True,
            output_dir="azure-ingest-output",
            num_processes=2,
        ),
        read_config=ReadConfig(),
        partition_config=PartitionConfig(),
        connector_config=SimpleAzureBlobStorageConfig(
            access_config=AzureAccessConfig(
                account_name="azureunstructured1",
            ),
            remote_url="abfs://container1/",
        ),
    )
    runner.run()

Run via the API

You can also use upstream connectors with the unstructured API. For this you’ll need to use the --partition-by-api flag and pass in your API key with --api-key.

#!/usr/bin/env bash

unstructured-ingest \
  azure \
  --remote-url abfs://container1/ \
  --account-name azureunstructured1 \
  --output-dir azure-ingest-output \
  --num-processes 2 \
  --partition-by-api \
  --api-key "<UNSTRUCTURED-API-KEY>"

import os

from unstructured.ingest.connector.fsspec.azure import (
    AzureAccessConfig,
    SimpleAzureBlobStorageConfig,
)
from unstructured.ingest.interfaces import (
    PartitionConfig,
    ProcessorConfig,
    ReadConfig,
)
from unstructured.ingest.runner import AzureRunner

if __name__ == "__main__":
    runner = AzureRunner(
        processor_config=ProcessorConfig(
            verbose=True,
            output_dir="azure-ingest-output",
            num_processes=2,
        ),
        read_config=ReadConfig(),
        partition_config=PartitionConfig(
            partition_by_api=True,
            api_key=os.getenv("UNSTRUCTURED_API_KEY"),
        ),
        connector_config=SimpleAzureBlobStorageConfig(
            access_config=AzureAccessConfig(
                account_name="azureunstructured1",
            ),
            remote_url="abfs://container1/",
        ),
    )
    runner.run()

Additionally, you will need to pass the --partition-endpoint if you’re running the API locally. You can find more information about the unstructured API here.

For a full list of the options the CLI accepts check unstructured-ingest azure --help.

NOTE: Keep in mind that you will need to have all the appropriate extras and dependencies for the file types of the documents contained in your data storage platform if you’re running this locally. You can find more information about this in the installation guide.