AirbyteLoader

Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.

This covers how to load any source from Airbyte into LangChain documents

Installation

In order to use AirbyteLoader you need to install the langchain-airbyte integration package.

%pip install -qU langchain-airbyte

Note: Currently, the airbyte library does not support Pydantic v2. Please downgrade to Pydantic v1 to use this package.

Note: This package also currently requires Python 3.10+.

Loading Documents

By default, the AirbyteLoader will load any structured data from a stream and output yaml-formatted documents.

from langchain_airbyte import AirbyteLoader

loader = AirbyteLoader(
    source="source-faker",
    stream="users",
    config={"count": 10},
)
docs = loader.load()
print(docs[0].page_content[:500])

\`\`\`yaml
academic_degree: PhD
address:
  city: Lauderdale Lakes
  country_code: FI
  postal_code: '75466'
  province: New Jersey
  state: Hawaii
  street_name: Stoneyford
  street_number: '1112'
age: 44
blood_type: "O\u2212"
created_at: '2004-04-02T13:05:27+00:00'
email: [e-mail]
gender: Fluid
height: '1.62'
id: 1
language: Belarusian
name: Moses
nationality: Dutch
occupation: Track Worker
telephone: [phone]
title: M.Sc.Tech.
updated_at: '2024-02-27T16:41:01+00:00'
weight: 6

You can also specify a custom prompt template for formatting documents:

from langchain_core.prompts import PromptTemplate

loader_templated = AirbyteLoader(
    source="source-faker",
    stream="users",
    config={"count": 10},
    template=PromptTemplate.from_template(
        "My name is {name} and I am {height} meters tall."
    ),
)
docs_templated = loader_templated.load()
print(docs_templated[0].page_content)

My name is Verdie and I am 1.73 meters tall.

Lazy Loading Documents

One of the powerful features of AirbyteLoader is its ability to load large documents from upstream sources. When working with large datasets, the default .load() behavior can be slow and memory-intensive. To avoid this, you can use the .lazy_load() method to load documents in a more memory-efficient manner.

import time

loader = AirbyteLoader(
    source="source-faker",
    stream="users",
    config={"count": 3},
    template=PromptTemplate.from_template(
        "My name is {name} and I am {height} meters tall."
    ),
)

start_time = time.time()
my_iterator = loader.lazy_load()
print(
    f"Just calling lazy load is quick! This took {time.time() - start_time:.4f} seconds"
)

Just calling lazy load is quick! This took 0.0001 seconds

And you can iterate over documents as they’re yielded:

for doc in my_iterator:
    print(doc.page_content)

My name is Andera and I am 1.91 meters tall.
My name is Jody and I am 1.85 meters tall.
My name is Zonia and I am 1.53 meters tall.

You can also lazy load documents in an async manner with .alazy_load():

loader = AirbyteLoader(
    source="source-faker",
    stream="users",
    config={"count": 3},
    template=PromptTemplate.from_template(
        "My name is {name} and I am {height} meters tall."
    ),
)

my_async_iterator = loader.alazy_load()

async for doc in my_async_iterator:
    print(doc.page_content)

My name is Carmelina and I am 1.74 meters tall.
My name is Ali and I am 1.90 meters tall.
My name is Rochell and I am 1.83 meters tall.

Configuration

AirbyteLoader can be configured with the following options:

• source (str, required): The name of the Airbyte source to load from.
• stream (str, required): The name of the stream to load from (Airbyte sources can return multiple streams)
• config (dict, required): The configuration for the Airbyte source
• template (PromptTemplate, optional): A custom prompt template for formatting documents
• include_metadata (bool, optional, default True): Whether to include all fields as metadata in the output documents

The majority of the configuration will be in config, and you can find the specific configuration options in the “Config field reference” for each source in the Airbyte documentation.