Traditional RAG vs. Graph RAG, clearly explained (with visuals):

top-k retrieval in RAG rarely works. Imagine you want to summarize a biography where each chapter details a specific accomplishment of an individual. Traditional RAG struggles because it retrieves only top-k chunks while it needs the entire context.

Graph RAG solves this by: - Building a graph with entities and relationships from docs. - Traversing the graph for context retrieval. - Sending the entire context to the LLM for a response. The visual shows how its different from naive RAG:

Let's see how Graph RAG solves the above problem. First, a system (typically an LLM) will create a graph from documents. This graph will have a subgraph for the person (P) where each accomplishment is one-hop away from the entity node of P.

During summarization, the system can do a graph traversal to fetch all the relevant context related to P's accomplishments.

The entire context will help the LLM produce a complete answer, while naive RAG won't. Graph RAG systems are also better than naive RAG systems because LLMs are inherently adept at reasoning with structured data.

I hope this clarifies what Graph RAG is and the problems it can solve! I'll leave you with a visual representation of how it works compared to traditional RAG.

If you found it insightful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!