Dense neural vectors excel at understanding fuzzy semantic concepts, but they can struggle with exact term lookups like serial numbers, localized product IDs, or specific legal codes. To build a robust search system, you need to combine keyword matching with vector logic.
Hybrid search combines traditional keyword retrieval models (like BM25) with dense vector mathematical spaces into a single index query. When a user submits a query, both lookup paths execute in parallel across your Azure or Pinecone database environments.
The crucial step occurs during the ranking synthesis phase using Reciprocal Rank Fusion (RRF) equations. This algorithm takes the distinct score sheets from both lookups, balances them based on customizable weight configurations, and compiles a clean context payload to feed your model.