LarkSuite is an enterprise collaboration platform developed by ByteDance.
This notebook covers how to load data from the LarkSuite REST API into a format that can be ingested into LangChain, along with example usage for text summarization. The LarkSuite API requires an access token (tenant_access_token or user_access_token), checkout LarkSuite open platform document for API details. 
from getpass import getpass

from langchain_community.document_loaders.larksuite import (
    LarkSuiteDocLoader,
    LarkSuiteWikiLoader,
)

DOMAIN = input("larksuite domain")
ACCESS_TOKEN = getpass("larksuite tenant_access_token or user_access_token")
DOCUMENT_ID = input("larksuite document id")

​
Load From Document

from pprint import pprint

larksuite_loader = LarkSuiteDocLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID)
docs = larksuite_loader.load()

pprint(docs)

[Document(page_content='Test Doc\nThis is a Test Doc\n\n1\n2\n3\n\n', metadata={'document_id': 'V76kdbd2HoBbYJxdiNNccajunPf', 'revision_id': 11, 'title': 'Test Doc'})]

​
Load From Wiki

from pprint import pprint

DOCUMENT_ID = input("larksuite wiki id")
larksuite_loader = LarkSuiteWikiLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID)
docs = larksuite_loader.load()

pprint(docs)

[Document(page_content='Test doc\nThis is a test wiki doc.\n', metadata={'document_id': 'TxOKdtMWaoSTDLxYS4ZcdEI7nwc', 'revision_id': 15, 'title': 'Test doc'})]

# see https://python.langchain.com/docs/use_cases/summarization for more details
from langchain.chains.summarize import load_summarize_chain
from langchain_community.llms.fake import FakeListLLM

llm = FakeListLLM()
chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs)