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create-and-query-vector-indexes

Name: Create And Query Vector Indexes
Author: FalkorDB

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

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stars:21

forks:2

updated:4 février 2026 à 21:41

SKILL.md

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name	Create and query vector indexes
description	Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search with embeddings

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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package.json

"author": "FalkorDB"

"repository": "FalkorDB/skills"

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$ download --local

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Architectes de bases de donnéesProfessions informatiques et mathématiques15-1243L4

name	Create and query vector indexes
description	Use HNSW vector indexes for Approximate Nearest Neighbor (ANN) search with embeddings

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

create-and-query-vector-indexes

Create and query vector indexes

Usage

Example

Notes

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Create and query vector indexes

Usage

Example

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