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Found 3 Skills
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
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.