Graph-based RAG (2/2) KG Query Pipeline

Steps

1. Overview
2. Pre-requisites
3. Illustration of the Process Through an Example
4. Expand Your Knowledge

• Teaser

1 Overview

Description

While there are multiple ways of leveraging Knowledge Graphs (KG) for Retrieval Augmented Generation (RAG), here we illustrate a significant example of how Knowledge Graphs fit in an AI query pipeline, using a Knowledge Graph to enrich a Large Language Model (LLM) prompt for extracting relevant domain information from documents. We explain this scenario in the context of a specific business use-case that deals with equipment industry standards.

We build on the foundation of the Graph-based RAG KG Creation Best Practice where we created a Knowledge Graph, and now we show how to use the Knowledge Graph to answer practical business questions using a simple and repeatable workflow.

Expected Outcome

By following this guide, you can setup a simple process where business facts are found in documents and traced back to their definitions in your knowledge domain. This can improve the accuracy of your AI results and help you explain where each answer comes from.