# Upgrading from Vectors to Graphs: Knowledge Graph Embeddings and Graph-RAG.

In the ever-evolving landscape of machine learning, moving from traditional vector-based representations to more intricate graph structures marks a significant shift in how we understand and process information. <mark style="color:blue;">**Knowledge Graph Embeddings (KGE)**</mark> offer a powerful way to capture relationships between entities in a graph format, going beyond the limitations of static vectors. By embedding these complex structures, we unlock new potentials in tasks like entity linking, relationship extraction, and semantic search, where understanding the context and connections between data points is crucial.&#x20;

Checkout the GitHub repository at [https://github.com/dsgiitr/kge-clip.git](https://github.com/dsgiitr/kge-clip.git).

This blog delves into the world of KGE and introduces Graph-RAG, a cutting-edge method that leverages these embeddings to enable more nuanced reasoning and retrieval across graph databases.

This project has been a collaborative effort by members within DSG,  to understand embeddings, graph representation and application.

From understanding the basics of embedding vectors to visualizing and implementing Graph-RAG, we'll guide you through the transformative journey of upgrading from vectors to graphs, providing both technical insights and practical examples.
