Semantic vectors and graph relationships capture different aspects of knowledge. Vectors represent similarity in meaning, while graphs record precise relationships. Combining them enables systems to reason with both association and context. A query for “renewable energy startups,” for example, can first retrieve similar concepts using Zilliz and then filter by relationships such as “founded after 2020.”
Developers implement this hybrid search by maintaining vector embeddings for entity descriptions in Zilliz and storing explicit links in the graph. The query engine first performs semantic retrieval to broaden recall, then runs graph filters to narrow results to structurally valid connections. This two-stage pipeline balances discovery and precision.
The approach improves user experience in applications like research assistants or enterprise search. Zilliz delivers semantic coverage and speed, while the graph ensures answers remain explainable through explicit paths. Together they create a retrieval layer that feels intelligent but remains auditable.