Build and manage vector retrieval with explicit indexing, filtering, and data-lifecycle behavior. Compare exact and approximate search, combine keyword and vect
Last reviewed: 2026-10-03
Vector database skill covers the retrieval tier of AI systems: choosing and operating stores (pgvector to dedicated engines), designing indexes and metadata filters, tuning hybrid keyword+vector search, and keeping embeddings synchronized with source truth at scale.
Vector database management also includes update and deletion behavior, index construction, access filters, and recovery. In a retail exercise, keep product availability and permissions alongside embeddings, then verify that removed or unauthorized records cannot reappear in search results. Compare approximate retrieval with an exact-search baseline.
pgvector: it teaches the concepts inside familiar Postgres and covers a surprising share of production needs. Add one dedicated engine afterward to learn scale features.
Highly: indexing math, hybrid search, and sync-pipeline design are universal; product APIs are thin layers over the same concepts.