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chore: import upstream snapshot with attribution
2026-07-13 12:26:52 +08:00

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "53fab4d2",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/TwitterDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "09e10de8",
"metadata": {},
"source": [
"# Twitter Reader"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bb6b5c5a",
"metadata": {},
"outputs": [],
"source": [
"%pip install llama-index-readers-twitter"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "367a6eae",
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import sys\n",
"\n",
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e4fbb649",
"metadata": {},
"source": [
"If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ee8a99b",
"metadata": {},
"outputs": [],
"source": [
"!pip install llama-index"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21d03e9b-8a47-45b2-ab27-295b7397ecad",
"metadata": {},
"outputs": [],
"source": [
"from llama_index.core import VectorStoreIndex\n",
"from llama_index.readers.twitter import TwitterTweetReader\n",
"from IPython.display import Markdown, display\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef5d2334-9661-4648-a823-a335ea277826",
"metadata": {},
"outputs": [],
"source": [
"# create an app in https://developer.twitter.com/en/apps\n",
"BEARER_TOKEN = \"<bearer_token>\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d6a1153-1383-4aaf-b39d-72c1fc9cc428",
"metadata": {},
"outputs": [],
"source": [
"# create reader, specify twitter handles\n",
"reader = TwitterTweetReader(BEARER_TOKEN)\n",
"documents = reader.load_data([\"@twitter_handle1\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca319024-88e7-424f-b1d8-4daa06c6bc6a",
"metadata": {},
"outputs": [],
"source": [
"index = VectorStoreIndex.from_documents(documents)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78680a17-9088-419e-97cf-ac3d5783a709",
"metadata": {},
"outputs": [],
"source": [
"# set Logging to DEBUG for more detailed outputs\n",
"query_engine = index.as_query_engine()\n",
"response = query_engine.query(\"<query_text>\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f0f92a7-cdd9-478f-9765-0a122d6e8508",
"metadata": {},
"outputs": [],
"source": [
"display(Markdown(f\"<b>{response}</b>\"))"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}