Rather than storing data in rows and columns for traditional searches, or as embeddings for vector search, a knowledge graph represents data points as nodes and edges. A node will be a distinct fact or characteristic, and edges will connect all the nodes that have relevant relationships to that fact. In the example of a product catalog, the nodes may be the individual products while the edges will be similar characteristics that each of those products possess, like size or color.
Sending a query to a knowledge graph involves looking for all the relevant entities to that search, and then creating a knowledge sub-graph that brings all those entities together. This retrieves the relevant information for the query, which can then be returned back to the LLM and used to build the response. This means that you can deal with the problem of having multiple similar data sources. Rather than treating each of these sources as distinct and retrieving the same data multiple times, the data will be retrieved once.
Using a knowledge graph with RAG
To use a knowledge graph with your RAG application, you can either use an existing knowledge graph with data that is tested and known to be correct in advance, or create your own. When you are using your own data—such as your product catalog—you will want to curate the data and check that it is accurate.