langchain/docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb

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feat (documents): add LarkSuite document loader (#6420) <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> ### Summary This PR adds a LarkSuite (FeiShu) document loader. > [LarkSuite](https://www.larksuite.com/) is an enterprise collaboration platform developed by ByteDance. ### Tests - an integration test case is added - an example notebook showing usage is added. [Notebook preview](https://github.com/yaohui-wyh/langchain/blob/master/docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb) <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ### Who can review? - PTAL @eyurtsev @hwchase17 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @hwchase17 VectorStores / Retrievers / Memory - @dev2049 --> --------- Co-authored-by: Yaohui Wang <wangyaohui.01@bytedance.com>
2023-06-28 06:08:05 +00:00
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "33205b12",
"metadata": {},
"source": [
"# LarkSuite (FeiShu)\n",
"\n",
">[LarkSuite](https://www.larksuite.com/) is an enterprise collaboration platform developed by ByteDance.\n",
"\n",
"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",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "90b69c94",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-19T10:05:03.645161Z",
"start_time": "2023-06-19T10:04:49.541968Z"
},
"tags": []
},
"outputs": [],
"source": [
"from getpass import getpass\n",
"from langchain.document_loaders.larksuite import LarkSuiteDocLoader\n",
"\n",
"DOMAIN = input(\"larksuite domain\")\n",
"ACCESS_TOKEN = getpass(\"larksuite tenant_access_token or user_access_token\")\n",
"DOCUMENT_ID = input(\"larksuite document id\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "13deb0f5",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-19T10:05:36.016495Z",
"start_time": "2023-06-19T10:05:35.360884Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[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"
]
}
],
"source": [
"from pprint import pprint\n",
"\n",
"larksuite_loader = LarkSuiteDocLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID)\n",
"docs = larksuite_loader.load()\n",
"\n",
"pprint(docs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ccc1e2f",
"metadata": {},
"outputs": [],
"source": [
"# see https://python.langchain.com/docs/use_cases/summarization for more details\n",
"from langchain.chains.summarize import load_summarize_chain\n",
"\n",
"chain = load_summarize_chain(llm, chain_type=\"map_reduce\")\n",
"chain.run(docs)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}