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Twitter Reader

%pip install llama-index-readers-twitter
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex πŸ¦™.

!pip install llama-index
from llama_index.core import VectorStoreIndex
from llama_index.readers.twitter import TwitterTweetReader
from IPython.display import Markdown, display
import os
# create an app in https://developer.twitter.com/en/apps
BEARER_TOKEN = "<bearer_token>"
# create reader, specify twitter handles
reader = TwitterTweetReader(BEARER_TOKEN)
documents = reader.load_data(["@twitter_handle1"])
index = VectorStoreIndex.from_documents(documents)
# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("<query_text>")
display(Markdown(f"<b>{response}</b>"))
Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL β€” e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ β€” search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/