When working with vector search, dimension limits matter. Many embedding models fit comfortably inside 768, 1,024, or 1,536 dimensions. But some use […]
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Why HNSW Indexes Are So Fast in YugabyteDB
Vector search gets expensive fast. Without an index, every query has to compare your search embedding against every row in the table. […]
Read MoreAI Search on YugabyteDB with Native pgvector Integration + GCP
🌍 The Problem: Search is Still… Dumb Most application search still relies on: ● LIKE ‘%term%’ ● exact keyword matching ● brittle […]
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