Design the relational-matching step for a graph with a...

At a vocabulary size of a thousand edge types, literal string matching fails to capture semantic nuance. Conversely, invoking an LLM for every query is prohibitively expensive at production scale. The optimal design uses precomputed embeddings for every edge type’s description. During retrieval, you perform a cosine top-k search against the query phrase. You only escalate to an LLM call when the top scores fall within a small, ambiguous margin, balancing performance with cost-efficiency.