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
Using Saved Configurations Save and reuse parse and extract configurations for consistent, repeatable extraction workflows
For a hands-on walkthrough covering all Extract V2 features (schema generation, citations, confidence scores, tier comparison, per-page extraction, and saved configurations), try the Complete Walkthrough Cookbook — runnable directly in Google Colab.
For more SDK examples, visit our Python repo or our TypeScript repo.