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Create and query vector indexes

Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search with embeddings

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Create and query vector indexes

Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search.

Usage

Create vector indexes with specific dimension and similarity configurations, then query them using db.idx.vector.queryNodes.

Example

redis-cli GRAPH.QUERY social "CREATE VECTOR INDEX FOR (p:Product) ON (p.embedding)
OPTIONS {dimension: 768, similarityFunction: 'cosine', M: 32, efConstruction: 200}"

redis-cli GRAPH.QUERY social "CALL db.idx.vector.queryNodes('Product', 'embedding', 5, vecf32([0.1, 0.2, 0.3]))
YIELD node, score RETURN node.name, score"

Notes

  • Vector indexes use HNSW (Hierarchical Navigable Small World) algorithm
  • Configure dimension to match your embedding size
  • Similarity functions include 'cosine', 'euclidean', etc.
  • M and efConstruction parameters tune index performance and accuracy
  • Use vecf32() to pass vector values in queries
  • Returns nodes with their similarity scores

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