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LlamaIndex Framework
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Usage Pattern

Each data loader contains a “Usage” section showing how that loader can be used. At the core of using each loader is a download_loader function, which downloads the loader file into a module that you can use within your application.

Example usage:

from llama_index.core import VectorStoreIndex, download_loader
from llama_index.readers.google import GoogleDocsReader
gdoc_ids = ["1wf-y2pd9C878Oh-FmLH7Q_BQkljdm6TQal-c1pUfrec"]
loader = GoogleDocsReader()
documents = loader.load_data(document_ids=gdoc_ids)
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
query_engine.query("Where did the author go to school?")
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 — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — 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/