Graph RAG Improves Evidence-Based Answers by Incorporating Relationships

The Challenge of Relational Evidence

Traditional vector search in RAG can retrieve relevant documents based on semantic similarity, but it often fails to establish the relationships between facts. For example, identifying which team owns a service dependent on a vulnerable library, or which customers are affected by a specific service, requires understanding connections beyond simple text similarity.

The missing evidence in such scenarios lies in the connections between data points. An agent needs to infer relationships like a specific service version using a library, that service supporting a customer environment, and the customer's contract defining a notification window. Without these explicit relationships, the system may struggle to distinguish relevant connections from similar but unrelated information.

When to Use Graph RAG

Graph RAG is justified when the relationships among facts are an integral part of the evidence required for an answer. While vector retrieval remains effective for finding relevant documents and records, a graph component is necessary when semantic similarity alone cannot prove why one fact applies to another.

This approach is particularly useful for questions involving ownership, dependency, authorization, or policy scope. A similarity score indicates an opinion about relevance, but conditions like tenant boundaries, effective dates, or account identifiers are hard constraints that a system must enforce, which graphs can represent.

Limitations of Vector Retrieval

Vector retrieval excels at finding text or records with similar meanings, even if different words are used. This makes it suitable for many RAG applications dealing with documentation, support articles, and other unstructured knowledge where answers are often contained within one or two passages.

However, vector search does not establish operational connections. Two records can be semantically similar without any direct operational link, while others can be directly connected with minimal shared language. This highlights the need for a method that can explicitly model and query these relationships.