Vector Search in OpenSearch: Embeddings, k-NN Indexes, and HNSW
· 6 min read
Keyword search breaks when users phrase queries differently from the words in your documents. Vector search fixes this by comparing meaning rather than tokens. This post walks through registering a sentence embedding model, building a k-NN index with an ingest pipeline, and running semantic queries against a FoundryDB-managed OpenSearch 2.19.1 cluster. All scores and outputs are from a real test run.
All commands use YOUR_OPENSEARCH_HOST and YOUR_PASSWORD as placeholders. Replace them with your cluster domain and app_user password from the FoundryDB dashboard.
pgvector similarity search · query → HNSW → top-k
TOP-K vector → HNSW index → filter → nearest rows
Queryvector | text→ANN · HNSWcosine <=>AND filter →Top-kby distance