Airbyte Stripe Loader

bash

pip install llama-index-readers-airbyte-stripe

The Airbyte Stripe Loader allows you to access different Stripe objects.

Usage

Here's an example usage of the AirbyteStripeReader.

python

from llama_index.readers.airbyte_stripe import AirbyteStripeReader

stripe_config = {
    # ...
}
reader = AirbyteStripeReader(config=stripe_config)
documents = reader.load_data(stream_name="invoices")

Configuration

Check out the Airbyte documentation page for details about how to configure the reader. The JSON schema the config object should adhere to can be found on Github: https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-stripe/source_stripe/spec.yaml.

The general shape looks like this:

python

{
    "client_secret": "<secret key>",
    "account_id": "<account id>",
    "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",
}

By default all fields are stored as metadata in the documents and the text is set to the JSON representation of all the fields. Construct the text of the document by passing a record_handler to the reader:

python

def handle_record(record, id):
    return Document(
        doc_id=id, text=record.data["title"], extra_info=record.data
    )


reader = AirbyteStripeReader(
    config=stripe_config, record_handler=handle_record
)

Lazy loads

The reader.load_data endpoint will collect all documents and return them as a list. If there are a large number of documents, this can cause issues. By using reader.lazy_load_data instead, an iterator is returned which can be consumed document by document without the need to keep all documents in memory.

Incremental loads

This loader supports loading data incrementally (only returning documents that weren't loaded last time or got updated in the meantime):

python

reader = AirbyteStripeReader(config={...})
documents = reader.load_data(stream_name="invoices")
current_state = reader.last_state  # can be pickled away or stored otherwise

updated_documents = reader.load_data(
    stream_name="invoices", state=current_state
)  # only loads documents that were updated since last time

This loader is designed to be used as a way to load data into LlamaIndex.