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langextract
Knowledge Tutorial

A chapter-by-chapter walkthrough of langextract, generated from its source code and tutorial markdown.

7
Chapters
-
Subsystems
∞
Rabbit Holes
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System Architecture

How the pieces fit

langextract is organized as connected concepts and components. Start broad, then drill down chapter by chapter.

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Extraction Orchestrator
Extraction Orchestrator
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Format Handling
Format Handling
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Provider Routing & Factory
Provider Routing & Factory
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Smart Chunking
Smart Chunking
⚙️
Prompt Engineering
Prompt Engineering
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Language Model Interface
Language Model Interface
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Batch Inference
Batch Inference
langextract — bash
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◆ Scanning numbered chapters
◆ Building navigation and Mermaid diagrams
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Repository Overview

Intro and Architecture Diagram

LangExtract is a framework designed to turn unstructured text into structured data (like JSON or YAML) using Large Language Models (LLMs). It orchestrates the entire pipeline by breaking long documents into smart chunks, selecting the appropriate model provider, and enforcing strict formatting rules to ensure accurate and reliable data extraction.

Source Repository: https://github.com/google/langextract

flowchart TD A0["Extraction Orchestrator"] A1["Language Model Interface"] A2["Smart Chunking"] A3["Provider Routing & Factory"] A4["Prompt Engineering"] A5["Format Handling"] A6["Batch Inference"] A0 -->|"Instantiates models via"| A3 A3 -->|"Creates instances of"| A1 A0 -->|"Splits documents using"| A2 A0 -->|"Generates inputs using"| A4 A4 -->|"Formats examples using"| A5 A0 -->|"Parses output with"| A5 A1 -->|"Delegates bulk tasks to"| A6
Tutorial Chapters

All 7 chapters

Follow sequentially or jump to any topic. Start with Extraction Orchestrator.

Ch.01 CORE
Extraction Orchestrator
Welcome to the langextract tutorial! If you have raw text and you need structured data, you are in the right place.
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Ch.02 CORE
Format Handling
In the Extraction Orchestrator chapter, we saw how to magically turn text into data using extract(). But have you ever wondered how the com…
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Ch.03 CORE
Provider Routing & Factory
In Format Handling, we learned how langextract cleans up the messy output from a Language Model (LLM) to give us nice, structured data. But…
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Ch.04 CORE
Smart Chunking
In the previous chapter, Provider Routing & Factory, we learned how to connect to any AI model (like Gemini or OpenAI).
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Ch.05 CORE
Prompt Engineering
In Smart Chunking, we learned how to slice a large document into safe, bite-sized pieces called "chunks."
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Ch.06 CORE
Language Model Interface
In Prompt Engineering, we successfully built the perfect "package"—a prompt containing our instructions, examples, and text chunks.
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Ch.07 CORE
Batch Inference
In the previous chapter, Language Model Interface, we learned how to talk to any AI model using a standardized infer() method. We sent a pr…
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About This Project

Generated by Code IQ

This tutorial was automatically generated by Code IQ and rendered with the shared tutorial site builder. It can be produced for any repository tutorial folder that follows the numbered markdown chapter layout.

View Code IQ ↗
python build_site.py '/home/runner/work/Code-IQ/Code-IQ/output/langextract'

// → 7 chapters
// → source: google/langextract