Neural Sparse Search in OpenSearch: Semantic Matching Without a GPU
· 5 min read
Dense vector search (k-NN) is powerful but requires embedding both documents and queries with a neural model at query time. Neural sparse search takes a different approach: expand tokens with learned weights at index time, store them as a rank_features field, and at query time do a fast lookup rather than a vector computation. The result is semantic search with no GPU requirement at query time. This post shows the full setup on a live OpenSearch 2.19.1 cluster managed by FoundryDB.
All commands use YOUR_OPENSEARCH_HOST and YOUR_PASSWORD as placeholders.
pgvector similarity search · query → HNSW → top-k
TOP-K vector → HNSW index → filter → nearest rows
Queryvector | text→ANN · HNSWcosine <=>AND filter →Top-kby distance