> 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.md). # Augmenting LLMs with Smart Data Extraction - [Using Smart Data Extraction to Augment Contextual LLM Queries - Overview](https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction/rag-guide.md): Learn how to enhance Large-Language Models (LLMs) with structured data using Apryse Data Extraction Module. Set up your environment to run examples efficiently. The Apryse Server SDK streamlines secur - [Using Smart Data Extraction to Augment Contextual LLM Queries - View the Python Guide](https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction/rag-guide/python-only.md): 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 - [Using Smart Data Extraction to Augment Contextual LLM Queries - Setup for Linux and Windows](https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction/setup.md): Learn how to set up and run examples using the IDP RAG guide. Obtain licenses, install Python modules, and export keys for Apryse SDK and Open AI API. Get started now! The Apryse Server SDK streamline - [Using Smart Data Extraction to Augment Contextual LLM Queries - Document Context Example](https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction/documentcontextexample.md): Learn how to include document context in a query to an LLM with this example. Extract document structure info, convert to HTML, and run the command for results. Discover Retrieval Augmented Generation - [Using Smart Data Extraction to Augment Contextual LLM Queries - Document RAG Example](https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction/iso32000ragexample.md): Learn how to optimize large documents for Open AI Chat Completions API with Retrieval-Augmented Generation (RAG) in this comprehensive guide. Find out how to efficiently handle document size and token - [Using Smart Data Extraction to Augment Contextual LLM Queries - Detailed Discussion](https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction/discussion.md): Discover the 6 basic steps for building a RAG in this detailed discussion. Learn how to split documents into chunks, generate structured context data, and more. Find out the rationale behind each deci --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.