Unique insights through graph databases

One of the most recent applications of graph databases lies in the realm of Generative AI. Specifically, Retrieval-Augmented Generation (RAG) models can leverage graph databases as their knowledge store.

When it comes to harnessing the power of Large Language Models (like GPT) in a business setting, we often encounter two main hurdles:

• Firstly, the issue of ‘hallucinations’, where the model generates information that isn’t based on any real data.
• Secondly, the model’s lack of awareness about your company-specific data.

The good news is that both these challenges can be effectively tackled with the use of a graph database. By storing your unique data in the graph database, you can leverage the language capabilities of the Large Language Model to generate high-quality output. This approach is rooted in real data, eliminating the need for more complex and less effective methods like Fine-Tuning or In-Context-Learning.

RAG models combine the best of both worlds from retrieval-based and generative models. They retrieve relevant documents from a knowledge store and use them to inform a generative model. When the knowledge store is a graph database, the model can efficiently navigate through the interconnected data, retrieving highly relevant information. This results in more accurate, context-aware responses, opening up new possibilities for AI applications.

Graph databases also pave the way for Graph Data Science (GDS). This emerging field focuses on using graph theory to understand complex systems and solve challenging problems.

By representing data as nodes (entities) and edges (relationships), graph data science enables the analysis of relationships and patterns within the data. This can lead to more accurate predictions, better decision-making, and deeper insights. From detecting community structures in networks to predicting protein interactions in bioinformatics, graph data science is set to revolutionize numerous industries.

GDS employs a variety of graph algorithms to extract insights from data. These include:

The adoption of graph databases presents a wealth of opportunities. By enabling more efficient data management, enhancing Generative AI, and powering the new field of graph data science, graph databases are set to play a pivotal role in the future of technology. Gartner predicts that “by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the enterprise” (Source: Gartner "Market Guide: Graph Database Management Solutions" Merv Adrian, Afraz Jaffri 30 August 2022). As a software consulting company, we are excited to help businesses harness these opportunities and drive innovation.