Knowledge Graphs

Retrieve Multi-Hop Evidence with Graph RAG

Retrieval-augmented generation (RAG) usually starts with a simple idea: find text chunks similar to a question, place the most relevant chunks in the model’s context, and ask the model to answer from that evidence. This works well when the answer is stated in one passage or in several passages that are independently easy to retrieve. Some questions are harder because the useful evidence is connected by relationships, not just by similar wording. A developer may need to answer, “Which service depends on the library maintained by the team that owns the payment API?” No single chunk has to contain all of those words. The answer may require following several links across services, libraries, teams, and APIs.