Advanced RAG with Knowledge Graphs (Neo4J demo)

Today, I am excited to share a demo that showcases the integration of AI language models with graph databases, highlighting their synergistic potential. In this demo, I delve into the concept of graph databases, which structure information into entities and contextual relationships, and discuss how they can be utilized in Retrieval-Augmented Generation (RAG) systems. By feeding unstructured data like PDFs and markdown files into a language model, we can extract entities, nodes, and relationships to create a graph database. The demo illustrates how this graph can be used to answer user queries by combining retrieved information from the database with the language model's generative capabilities. The demo also emphasizes the advantages of using a graph over traditional vector similarity searches for complex, multi-hop queries, as well as the flexibility and scalability of graph databases in accommodating incremental data additions.

Keywords

Graph databases, AI language models, Retrieval-Augmented Generation (RAG) systems, Unstructured data, Neo4J demo, Multihop searches

FAQ

What is the main focus of the demo? The demo focuses on showcasing the integration of AI language models with graph databases to create a Retrieval-Augmented Generation (RAG) system.
How are entities and relationships extracted for the graph database? Entities and relationships are extracted from unstructured data sources like PDFs and markdown files by feeding them into a language model.
Why are graph databases considered advantageous over vector similarity searches for complex queries? Graph databases excel in handling multi-hop searches where information is scattered across different documents, making them more suitable for complex query scenarios.