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")
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'})]
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'})]
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)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4