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## What does this PR do? Adds a pgvector index tuning guide covering: - IVFFlat vs HNSW index selection - Parameter tuning (`lists`, `probes`, `m`, `ef_construction`) - Performance trade-offs - Recommendations for production workloads (100k+ embeddings) ## Why? There is currently limited guidance on index tuning for semantic search workloads. This helps developers optimize query latency and accuracy when using Supabase for AI applications. ## Type of change - [x] Documentation update Closes #44598 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Documentation** * Added a semantic search guide section with practical index-tuning recommendations for large datasets, including IVFFlat and HNSW index explanations, best-use scenarios, and sample index creation guidance to balance performance and accuracy. * **Style** * Minor import formatting cleanup in a UI utility module (no behavior changes). <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: Chris Chinchilla <chris@chrischinchilla.com> Co-authored-by: Chris Chinchilla <chris.ward@supabase.io>