In today’s data-rich world, users expect highly relevant search results. While traditional keyword search excels at precision, it often misses context. Vector embeddings, on the other hand, understand semantics but can sometimes lack exact term matching. Hybrid search combines the strengths of both approaches, offering a powerful solution that delivers both precise and contextually relevant results. This article dives into the architecture, implementation strategies, and benefits of integrating keyword and vector search for a truly intelligent search experience.