Build RAG systems and knowledge graphs

We will demonstrate how to build Retrieval-Augmented Generation (RAG) systems and knowledge graphs using Gathr’s GenAI fabric.

RAG combines retrieval-based and generation-based models to produce accurate and contextually relevant responses, significantly enhancing the effectiveness of natural language processing. At the same time, knowledge graphs provide a structured framework to represent and organize complex knowledge and relationships, enhancing AI systems’ understanding and enabling knowledge discovery.

For this demo, we will take a use case from the HR and Recruitment domain. We will build a RAG solution to improve productivity of recruiters and HR professionals, by helping them navigate and extract specific information from large resume collections. We will showcase how to extract skills and other information from resumes, model them in a graph databases such as Neo4J, and query the graph in natural language.