# Create and query vector indexes

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

- Skill: `falkordb/create-and-query-vector-indexes` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add falkordb/create-and-query-vector-indexes`
- Raw SKILL.md: https://api.skillmd.com/api/skills/falkordb/create-and-query-vector-indexes/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: falkordb (https://skillmd.com/u/falkordb)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/falkordb/create-and-query-vector-indexes

---


# 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

```bash
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