Pure vector search is transformative, but it has a glaring weakness: precision. While semantic search excels at understanding intent (e.g., mapping "guidelines for visual design" to "style guide"), it often fails miserably at specific keyword matching. If a user searches for a specific error code ("ERR-902"), a SKU, or a proper noun, vector embeddings often "hallucinate" associations or drown out the exact match with conceptually similar but irrelevant results. This is the Dense vs. Sparse vector problem . To build a production-grade search (RAG) system, you need Hybrid Search . This technique combines the conceptual understanding of embeddings (Dense) with the precise matching of Full-Text Search (Sparse). Here is a rigorous guide to implementing Hybrid Search in Supabase using pgvector and Reciprocal Rank Fusion (RRF). The Root Cause: Why Vector Search Isn't Enough Embeddings work by compressing high-dimensional data (text) into a low...
Android, .NET C#, Flutter, and Many More Programming tutorials.