> 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/iso32000ragexample.md). # Using Smart Data Extraction to Augment Contextual LLM Queries - Document RAG Example 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 ## Example 2 - Document RAG The [previous example](/core/augmenting-llms-with-smart-data-extraction/documentcontextexample.md) works well with small documents, but issues may arise when confronted with larger documents or a large corpus of documents, for a couple of reasons: 1. Pricing of requests to the Open AI Chat Completions API is a function of input (and output) size. By attaching a large document to our query, the operation can get very expensive very quickly. This is also often unnecessary, as the query itself is rarely about the whole document, but rather about some subsection. See [Open AI's documentation](https://openai.com/pricing/) for more information on token limits. 2. Chat Completion Models have a "token limit", which unfortunately constrains how much data we are able to pass to the model. Thus, we have to find a way to pass relevant subsections of the data when our total context exceeds the token limit. See [Open AI's documentation](https://platform.openai.com/docs/models/overview/) for more information on token limits. What can we do about this? Here, we will introduce the concept of Retrieval-Augmented Generation (RAG) (we will also refer to Retrieval-Augmented *Generators* as RAGs, depending on context. These are systems that *employ* Retrieval-Augmented Generation). A RAG can be used to find relevant information to a query from a large corpus of context information. This relevant information can then be attached to the query as context, without needing to attach the *entire* document. To do this, we will expand a bit on the list of steps we provided in [the previous example](/core/augmenting-llms-with-smart-data-extraction/documentcontextexample.md): 1. Extract document structure information using the Apryse Smart Data Extraction Module. 2. Split the document(s) into chunks that we can use to build an *index*. The index can then be used to search for the relevant context for a given query. 3. Generate the required representations of the structured context data (we will use both HTML and plain text representations). 4. Build the index using the [Open AI Embeddings API](https://platform.openai.com/docs/guides/embeddings/). 5. Given a user question / query, search the index for the most suitable context chunk. 6. Attach the most relevant context chunk(s) to the query, and send it to the Open AI chat completion model. For the following example, we will use a very large document, the ISO\_32000-2:2020 PDF standard, to demonstrate these techniques. This document is available for free download from [Adobe](https://developer.adobe.com/document-services/docs/assets/5b15559b96303194340b99820d3a70fa/PDF_ISO_32000-2.pdf). If you haven't already, please download it and place it at the following location: *idp\_rag\_guide/data/pdf/PDF\_ISO\_32000-2.pdf*. To run the example, use the following command (with your virtual environment active, if using): {% tabs %} {% tab title="Shell" %} {% code lineNumbers="true" %} ```sh python3 ./iso32000_rag.py ``` {% endcode %} {% endtab %} {% endtabs %} You should see some text indicating progress, with a question and answer about the document appearing at the end. LLM's aren't guaranteed to produce identical output between runs, but you should see something similar to the following: {% tabs %} {% tab title="Shell" %} {% code lineNumbers="true" %} ```sh Extracting Document Structure from /home/matt/dev/idp-deep-learning/ document-summary-rag/idp_rag_guide/data/pdf/PDF_ISO_32000-2.pdf... Extracted data to /home/matt/dev/idp-deep-learning/document-summary-rag/ idp_rag_guide/data/output/rag_example/PDF_ISO_32000-2/json/PDF_ISO_32000-2.json Using bookmark tree to split the document into sections... Generating HTML and Text representations for each section... Generating embeddings for each section... ================================================================================ Question: What are the meanings of the numeric values used by the Tj Operator? For example, "[(He)20(ll)10(o Wo)10(rld)]TJ"? Detected Context: 9.4.3 Text-showing operators 9.2.3 Achieving special graphical effects 9.2.4 Glyph positioning and metrics Response: The numeric values used by the TJ operator in a text-showing command like "[(He)20(ll)10(o Wo)10(rld)]TJ" represent adjustments to the text position between the glyphs or strings of glyphs. According to Excerpt #1, each element of the array passed to the TJ operator can be either a string or a number. If the element is a string, the operator shows the string. If it is a number, the operator adjusts the text position by that amount. This adjustment is a translation of the text matrix, Tm, and the number is expressed in thousandths of a unit of text space. The effect of this adjustment is to move the next glyph painted either to the left or down by the given amount, depending on the writing mode. In the default coordinate system, a positive adjustment moves the next glyph to the left (in horizontal writing mode) by the amount specified. Therefore, in the example "[(He)20(ll)10(o Wo)10(rld)]TJ": - The "20" after "(He)" moves the next glyph ("ll") 20 thousandths of a unit of text space to the left of where it would normally be placed. - The "10" after "(ll)" moves the next glyph sequence "(o Wo)" 10 thousandths of a unit of text space to the left of its standard position. - Similarly, the "10" after "(o Wo)" adjusts the position of "(rld)" to the left by 10 thousandths of a unit of text space from where it would otherwise be positioned. This mechanism allows for fine control over the spacing between glyphs or groups of glyphs, enabling adjustments for kerning, aesthetic spacing, or other typographic considerations. ``` {% endcode %} {% endtab %} {% endtabs %} ## Next Steps For more details on how to build something like this yourself and a discussion of some of the decisions made for this example, see [the Detailed Discussion](/core/augmenting-llms-with-smart-data-extraction/discussion.md). --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.apryse.com/core/augmenting-llms-with-smart-data-extraction/iso32000ragexample.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.