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68 lines
1.3 KiB
Plaintext
68 lines
1.3 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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"metadata": {},
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"source": [
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"### WhatsApp Chat\n",
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"\n",
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"This notebook covers how to load data from the WhatsApp Chats into a format that can be ingested into LangChain."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import WhatsAppChatLoader"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = WhatsAppChatLoader(\"example_data/whatsapp_chat.txt\")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"loader.load()"
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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": "3.11.1"
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},
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"vscode": {
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"interpreter": {
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"hash": "384707f4965e853a82006e90614c2e1a578ea1f6eb0ee07a1dd78a657d37dd67"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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