llama-index-readers-airbyte-hubspot 0.3.0

llama-index readers airbyte_hubspot integration

Airbyte Hubspot Loader

pip install llama-index-readers-airbyte-hubspot

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

Usage

Here's an example usage of the AirbyteHubspotReader.

from llama_index.readers.airbyte_hubspot import AirbyteHubspotReader

hubspot_config = {
    # ...
}
reader = AirbyteHubspotReader(config=hubspot_config)
documents = reader.load_data(stream_name="products")

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-hubspot/source_hubspot/spec.yaml.

The general shape looks like this:

{
    "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",
    "credentials": {
        "credentials_title": "Private App Credentials",
        "access_token": "<access token of your private app>",
    },
}

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:

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


reader = AirbyteHubspotReader(
    config=hubspot_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):

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

updated_documents = reader.load_data(
    stream_name="products", 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.