langchain/docs/modules/indexes/document_loaders/examples/whatsapp_chat.ipynb

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### WhatsApp Chat\n",
"\n",
"This notebook covers how to load data from the WhatsApp Chats into a format that can be ingested into LangChain."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import WhatsAppChatLoader"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"loader = WhatsAppChatLoader(\"example_data/whatsapp_chat.txt\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"loader.load()"
]
}
],
"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": "3.11.1"
},
"vscode": {
"interpreter": {
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"nbformat": 4,
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}