forked from Archives/langchain
85 lines
1.8 KiB
Plaintext
85 lines
1.8 KiB
Plaintext
{
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"cells": [
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{
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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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"# Telegram\n",
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"\n",
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"This notebook covers how to load data from Telegram 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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"id": "90b69c94",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TelegramChatLoader"
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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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"id": "13deb0f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = TelegramChatLoader(\"example_data/telegram.json\")"
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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": "9ccc1e2f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[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)]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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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"cell_type": "code",
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"execution_count": null,
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"id": "3e64cac2",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.9.1"
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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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