> For the complete documentation index, see [llms.txt](https://docs.apryse.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction/rag-guide/python-only.md).

# Using Smart Data Extraction to Augment Contextual LLM Queries - View the Python Guide

Enhance Large Language Model performance with Apryse Data Extraction Module. Learn how to integrate structured data from document collections to build AI applications. Available in Python on Windows o

This guide discusses how to use the [Apryse Data Extraction Module](/core/smart-data-extraction/smart-data-extraction.md) to enhance Large Language Model (LLM) performance and scope by providing structured data from a document collection as context. This allows developers to build AI applications that integrate with private documents (for example, using Retrieval Augmented Generation).

Currently, this guide is **only available in Python on Windows or Linux.**

While not available in other languages, the content discussed in the guide is applicable across languages and could be adapted to the language of your choosing. Please visit [Using Smart Data Extraction to Augment Contextual LLM Queries](/core/augmenting-llms-with-smart-data-extraction/rag-guide.md) to read further.


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