Azure
Batch process all your records using unstructured-ingest
to store structured outputs locally on your filesystem and upload those local files to an Azure bucket.
First you’ll need to install the Azure dependencies as shown here.
pip install "unstructured[azure]"
Run Locally
The upstream connector can be any of the ones supported, but for convenience here, showing a sample command using the upstream local connector.
#!/usr/bin/env bash
EMBEDDING_PROVIDER=${EMBEDDING_PROVIDER:-"langchain-huggingface"}
unstructured-ingest \
local \
--input-path example-docs/book-war-and-peace-1225p.txt \
--output-dir local-output-to-azure \
--strategy fast \
--chunk-elements \
--embedding-provider "$EMBEDDING_PROVIDER" \
--num-processes 2 \
--verbose \
azure \
--account-name azureunstructured1 \
--remote-url "<your destination path here, ie 'az://unstructured/war-and-peace-output'>"
from unstructured.ingest.connector.fsspec.azure import (
AzureAccessConfig,
AzureWriteConfig,
SimpleAzureBlobStorageConfig,
)
from unstructured.ingest.connector.local import SimpleLocalConfig
from unstructured.ingest.interfaces import (
ChunkingConfig,
EmbeddingConfig,
PartitionConfig,
ProcessorConfig,
ReadConfig,
)
from unstructured.ingest.runner import LocalRunner
from unstructured.ingest.runner.writers.base_writer import Writer
from unstructured.ingest.runner.writers.fsspec.azure import (
AzureWriter,
)
def get_writer() -> Writer:
return AzureWriter(
connector_config=SimpleAzureBlobStorageConfig(
access_config=AzureAccessConfig(account_name="azureunstructured1"),
remote_url="az://unstructured/war-and-peace-output",
),
write_config=AzureWriteConfig(),
)
if __name__ == "__main__":
writer = get_writer()
runner = LocalRunner(
processor_config=ProcessorConfig(
verbose=True,
output_dir="local-output-to-azure",
num_processes=2,
),
connector_config=SimpleLocalConfig(
input_path="example-docs/book-war-and-peace-1225p.txt",
),
read_config=ReadConfig(),
partition_config=PartitionConfig(),
chunking_config=ChunkingConfig(chunk_elements=True),
embedding_config=EmbeddingConfig(
provider="langchain-huggingface",
),
writer=writer,
writer_kwargs={},
)
runner.run()
For a full list of the options the CLI accepts check unstructured-ingest <upstream connector> 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.