Traditional RAG vs Graph RAG

Author(s): Kalash Vasaniya

Originally published on Towards AI.

Why Graph RAG Outperforms Classical Retrieval: A Smarter Path to Context-Rich AnswersSource: From https://x.com/akshay_pachaar

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Graph RAG is next-level for sure.

top-k retrieval in RAG rarely works.

Legacy RAG methods depend on selecting the “k” most relevant passages or chunks of text. This has some effectiveness but is soon insufficient if you require a complete, cohesive story.

Consider abbreviating a biography where every chapter is dedicated to one accomplishment. If you simply take the most, you will be omitting essential information.

This provides you with an incomplete picture and produces answers that may lack vital context or linkages between accomplishments.

Source: From https://x.com/akshay_pachaar

Graph RAG is not conventional.

Rather than directly utilizing the highest k components, it forms an interconnected graph depicting key individuals and how they interconnect based on the source texts.

To take an example, if you’re summarizing a life story, Graph RAG builds a complete graph wherein the individual (in the interest of argument, name them P) is connected with all the achievements. The strength of the process is that it can present the complete picture by identifying and maintaining relationships within information that would otherwise be lost.

Source: From https://x.com/akshay_pachaar

Collecting Entities and Their Relations One of the key steps in Graph RAG is… Read the full blog for free on Medium.

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Published via Towards AI