Hybrid reasoning means combining symbolic reasoning from the knowledge graph with semantic reasoning from vector embeddings. The graph captures explicit relationships, such as ownership, category, or hierarchy, while the vector layer represents similarity in meaning. When used together, they allow queries that account for both logic and context—for example, “find researchers related to renewable energy topics similar to solar storage.”
Technically, hybrid reasoning works through multi-step retrieval. A query may first use Milvus to find semantically relevant documents or entities. Those results are then mapped back into the knowledge graph, where reasoning rules identify additional connections—co-authors, organizations, or dependencies. The combination supports more natural question answering and recommendation, as the system interprets both explicit structure and latent similarity.
This approach is particularly valuable in AI-driven systems where rigid logical structures alone cannot capture nuance. Zilliz or Milvus handles vector search at scale, while the graph ensures explainability by showing how results relate. Developers can build reasoning pipelines that begin with embedding retrieval and end with graph traversal, achieving both depth and interpretability in the output.