LlamaExtract Examples
Collection of examples demonstrating how to use the LlamaExtract Python SDK for document extraction.
Extract Data from Financial Reports with Citations & Reasoning Extract structured data from SEC filings with citations and reasoning. Verify accuracy and understand LLM decisions when extracting from complex financial documents.
Auto-Generate Schema for Extraction Generate extraction schemas with a prompt
Extracting Repeating Entities with Table Row Extraction Extract repeating entities from documents using table row extraction
Resume Book Processing Agent Extract structured data from long, repetitive files like resume books
Production Extraction: Batch Processing, Polling, and Latency Management Batch extraction from multiple files, parse-then-extract workflows, timeout handling, webhooks, and schema management
For more detailed examples on how to use the Python SDK, visit our Python repo or our TypeScript repo.
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/