mirror of
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104 lines
2.8 KiB
Plaintext
104 lines
2.8 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "33205b12",
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"metadata": {},
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"source": [
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"# LarkSuite (FeiShu)\n",
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"\n",
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">[LarkSuite](https://www.larksuite.com/) is an enterprise collaboration platform developed by ByteDance.\n",
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"\n",
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"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.\n",
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"\n",
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"The LarkSuite API requires an access token (tenant_access_token or user_access_token), checkout [LarkSuite open platform document](https://open.larksuite.com/document) for API details."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "90b69c94",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-19T10:05:03.645161Z",
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"start_time": "2023-06-19T10:04:49.541968Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"from getpass import getpass\n",
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"from langchain.document_loaders.larksuite import LarkSuiteDocLoader\n",
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"\n",
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"DOMAIN = input(\"larksuite domain\")\n",
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"ACCESS_TOKEN = getpass(\"larksuite tenant_access_token or user_access_token\")\n",
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"DOCUMENT_ID = input(\"larksuite document id\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "13deb0f5",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-19T10:05:36.016495Z",
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"start_time": "2023-06-19T10:05:35.360884Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[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'})]\n"
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]
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}
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],
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"source": [
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"from pprint import pprint\n",
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"\n",
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"larksuite_loader = LarkSuiteDocLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID)\n",
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"docs = larksuite_loader.load()\n",
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"\n",
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"pprint(docs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9ccc1e2f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# see https://python.langchain.com/docs/use_cases/summarization for more details\n",
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"from langchain.chains.summarize import load_summarize_chain\n",
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"\n",
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"chain = load_summarize_chain(llm, chain_type=\"map_reduce\")\n",
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"chain.run(docs)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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