A knowledge graph excels at representing structured relationships—who, what, where, and how concepts connect. However, it struggles with unstructured or fuzzy information such as text similarity or image relationships. A vector database enhances the graph by adding semantic understanding. Text, documents, or other high-dimensional data can be converted into embeddings and stored in the vector database. When a user issues a query, the system can find conceptually similar entities even if the exact keywords or links are missing.
This hybrid approach unites the strengths of symbolic and semantic reasoning. The knowledge graph manages explicit connections—like "user A bought product B"—while the vector layer captures latent similarities, such as “products with similar descriptions.” Queries can move fluidly between both layers: a semantic search identifies relevant entities in Milvus, then the graph traces their contextual relationships. This produces richer, more flexible results.
In practical terms, Milvus or Zilliz provides the high-performance retrieval infrastructure that keeps similarity searches fast even at scale. Developers can index millions of embeddings with millisecond latency and link them to graph nodes via shared IDs. The result is a knowledge system that combines logic-driven structure with adaptive discovery, useful in domains like recommendation, research, and enterprise intelligence.