LangExtract: A library for extracting structured data from language models
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<p align="center"> <a href="https://github.com/google/langextract"> <img src="https://raw.githubusercontent.com/google/langextract/main/docs/_static/logo.svg" alt="LangExtract Logo" width="128" /> </a> </p> # LangExtract [](https://pypi.org/project/langextract/) [](https://github.com/google/langextract)  [](https://doi.org/10.5281/zenodo.17015089) ## Table of Contents - [Introduction](#introduction) - [Why LangExtract?](#why-langextract) - [Quick Start](#quick-start) - [Installation](#installation) - [API Key Setup for Cloud Models](#api-key-setup-for-cloud-models) - [Adding Custom Model Providers](#adding-custom-model-providers) - [Using OpenAI Models](#using-openai-models) - [Using Local LLMs with Ollama](#using-local-llms-with-ollama) - [More Examples](#more-examples) - [*Romeo and Juliet* Full Text Extraction](#romeo-and-juliet-full-text-extraction) - [Medication Extraction](#medication-extraction) - [Radiology Report Structuring: RadExtract](#radiology-report-structuring-radextract) - [Community Providers](#community-providers) - [Contributing](#contributing) - [Testing](#testing) - [Disclaimer](#disclaimer) ## Introduction LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions. It processes materials such as clinical notes or reports, identifying and organizing key details while ensuring the extracted data corresponds to the source text. ## Why LangExtract? 1. **Precise Source Grounding:** Maps every extraction to its exact location in the source text, enabling visual highlighting for easy traceability and verification. 2. **Reliable Structu…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.