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Found 2 Skills
Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch` / `maxDegree`, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use when building RAG / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.
PostgreSQL 17/18+ performance tuning and optimization. Covers async I/O configuration, query plan forensics, index strategies, autovacuum tuning, and vector search optimization. Use when diagnosing slow queries, configuring async I/O, tuning autovacuum, optimizing vector indexes, or analyzing execution plans with EXPLAIN BUFFERS.