TigerGraph Hybrid Search: Graph and Vector for Smarter AI Applications

TigerGraph's Hybrid Search integrates graph traversal and vector embedding search to enhance AI-powered retrieval systems by delivering more accurate and context-aware results. This approach addresses the limitations of traditional AI systems that rely solely on vector search, which often leads to irrelevant or misleading outcomes. Hybrid Search combines the strengths of graph search, which identifies relationships and structures, with vector search, which finds semantically similar entities, to provide a dual-layer precision that ensures both semantic relevance and relational connectivity. This integration is particularly beneficial for applications requiring explainability and deeper insights, such as fraud detection, personalized recommendations, and supply chain optimization. TigerGraph's system supports real-time indexing and high-performance vector searches, providing a scalable solution for enterprise AI applications. The free TigerGraph DB Community Edition offers full hybrid search capabilities, making it accessible for production workloads and enabling the development of more reliable and explainable AI applications.