Airbyte Salesforce Loader

bash

pip install llama-index-readers-airbyte-salesforce

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

Usage

Here's an example usage of the AirbyteSalesforceReader.

python

from llama_index.readers.airbyte_salesforce import AirbyteSalesforceReader

salesforce_config = {
    # ...
}
reader = AirbyteSalesforceReader(config=salesforce_config)
documents = reader.load_data(stream_name="asset")

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-salesforce/source_salesforce/spec.yaml.

The general shape looks like this:

python

{
    "client_id": "<oauth client id>",
    "client_secret": "<oauth client secret>",
    "refresh_token": "<oauth refresh token>",
    "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",
    "is_sandbox": False,  # set to True if you're using a sandbox environment
    "streams_criteria": [  # Array of filters for salesforce objects that should be loadable
        {
            "criteria": "exacts",
            "value": "Account",
        },  # Exact name of salesforce object
        {"criteria": "starts with", "value": "Asset"},  # Prefix of the name
        # Other allowed criteria: ends with, contains, starts not with, ends not with, not contains, not exacts
    ],
}

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

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