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Google's cross-platform framework for on-device ML perception: vision, text and audio pipelines
★ 37,2 k étoilesApache-2.0
Voir le guide →langextract est un projet de développement open source, créé par google (google/langextract sur GitHub). Il compte 38 930 étoiles sur GitHub et 2 720 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 6 octobre 2026.
Google's Python library for extracting structured data from unstructured text with LLMs, with grounding
Decide what you want from langextract and check the README and requirements to confirm it fits your setup and Python environment.
Clone the repository from GitHub (github.com/google/langextract) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Extracts structured information from unstructured text using LLMs.
Every extraction is grounded to its exact source span in the document.
Visualize extraction results interactively to verify quality.
Scales to longer documents beyond a single context window.
Supports Vertex AI batch processing for large jobs.
Works with Gemini models and other providers as documented.
Start by getting the source and entering the project directory.
git clone https://github.com/google/langextract.git
cd langextractInstall the langextract package.
pip install langextract # see README Installation sectionDefine your extraction task with examples, run it on text, then visualize the grounded results.
# see README Quick Start for the example codeIt is maintained by google and contributors on GitHub, where the repository github.com/google/langextract is the canonical home for code, issues and releases.
Yes — it is open source under the Apache-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
Yes for cloud models — for example a Gemini API key; setup for cloud models is documented in the README.
Compared with hand-written regex or plain prompting: LangExtract adds few-shot task definition, exact source grounding and visualization on top of raw LLM prompting.
Clone github.com/google/langextract, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
Google's cross-platform framework for on-device ML perception: vision, text and audio pipelines
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Google's cross-platform framework for on-device ML perception: vision, text and audio pipelines
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