AI

Once the data is prepared and stored in a graph database, the next step is to make it usable. For gRAG, this meant building an application layer capable of retrieving and generating insightful, context-rich responses. At the core of this application is LangChain, a framework that seamlessly integrates retrieval and generation workflows.

Retrieval-Augmented Generation (RAG) systems start with a simple but critical requirement: data. But not just any data—it needs to be clean, structured, and primed for meaningful retrieval and generation. With gRAG, my experimental take on RAG, I set out to explore what happens when you introduce a graph database like Neo4j into the mix.

A few months ago, I found myself diving into a side project that I called gRAG, an attempt at building my own version of a Retrieval-Augmented Generation (RAG) system. What set it apart was my decision to experiment with a graph database, aiming to harness its unique strengths to push RAG systems a bit further. It wasn’t a brand-new idea by any means, but more of a way for me to explore how the power of graphs could complement and enhance the RAG framework.