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

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33205b12",
   "metadata": {},
   "source": [
    "# Telegram\n",
    "\n",
    "This notebook covers how to load data from Telegram into a format that can be ingested into LangChain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90b69c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import TelegramChatLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13deb0f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = TelegramChatLoader(\"example_data/telegram.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ccc1e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content=\"Henry on 2020-01-01T00:00:02: It's 2020...\\n\\nHenry on 2020-01-01T00:00:04: Fireworks!\\n\\nGrace 🧤 ðŸ\\x8d’ on 2020-01-01T00:00:05: You're a minute late!\\n\\n\", lookup_str='', metadata={'source': 'example_data/telegram.json'}, lookup_index=0)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e64cac2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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