Airtable
Connect Airtable 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 Airtable dependencies as shown here.
pip install "unstructured[airtable]"
Run Locally
#!/usr/bin/env bash
unstructured-ingest \
airtable \
--metadata-exclude filename,file_directory,metadata.data_source.date_processed \
--personal-access-token "$AIRTABLE_PERSONAL_ACCESS_TOKEN" \
--output-dir airtable-ingest-output \
--num-processes 2 \
--reprocess
import os
from unstructured.ingest.connector.airtable import AirtableAccessConfig, SimpleAirtableConfig
from unstructured.ingest.interfaces import (
PartitionConfig,
ProcessorConfig,
ReadConfig,
)
from unstructured.ingest.runner import AirtableRunner
if __name__ == "__main__":
runner = AirtableRunner(
processor_config=ProcessorConfig(
verbose=True,
output_dir="airtable-ingest-output",
num_processes=2,
),
read_config=ReadConfig(),
partition_config=PartitionConfig(),
connector_config=SimpleAirtableConfig(
access_config=AirtableAccessConfig(
personal_access_token=os.getenv("AIRTABLE_PERSONAL_ACCESS_TOKEN")
),
),
)
runner.run()
Run via the API
You can also use upstream connectors with the unstructured
API. For this you’ll need to use the --partition-by-api
flag and pass in your API key with --api-key
.
#!/usr/bin/env bash
unstructured-ingest \
airtable \
--metadata-exclude filename,file_directory,metadata.data_source.date_processed \
--personal-access-token "$AIRTABLE_PERSONAL_ACCESS_TOKEN" \
--output-dir airtable-ingest-output \
--num-processes 2 \
--reprocess \
--partition-by-api \
--api-key "<UNSTRUCTURED-API-KEY>"
import os
from unstructured.ingest.connector.airtable import AirtableAccessConfig, SimpleAirtableConfig
from unstructured.ingest.interfaces import (
PartitionConfig,
ProcessorConfig,
ReadConfig,
)
from unstructured.ingest.runner import AirtableRunner
if __name__ == "__main__":
runner = AirtableRunner(
processor_config=ProcessorConfig(
verbose=True,
output_dir="airtable-ingest-output",
num_processes=2,
),
read_config=ReadConfig(),
partition_config=PartitionConfig(
partition_by_api=True,
api_key=os.getenv("UNSTRUCTURED_API_KEY"),
),
connector_config=SimpleAirtableConfig(
access_config=AirtableAccessConfig(
personal_access_token=os.getenv("AIRTABLE_PERSONAL_ACCESS_TOKEN")
),
),
)
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 airtable --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.