Twitter Reader
%pip install llama-index-readers-twitterimport loggingimport sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))If youβre opening this Notebook on colab, you will probably need to install LlamaIndex π¦.
!pip install llama-indexfrom llama_index.core import VectorStoreIndexfrom llama_index.readers.twitter import TwitterTweetReaderfrom IPython.display import Markdown, displayimport os# create an app in https://developer.twitter.com/en/appsBEARER_TOKEN = "<bearer_token>"# create reader, specify twitter handlesreader = TwitterTweetReader(BEARER_TOKEN)documents = reader.load_data(["@twitter_handle1"])index = VectorStoreIndex.from_documents(documents)# set Logging to DEBUG for more detailed outputsquery_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/