Create and query vector indexes

To create a vector index in FalkorDB, you configure an HNSW index by defining embedding dimensions, selecting a similarity function, and setting performance tuning parameters for efficient approximate nearest neighbor search.

HNSW vector similarity search in a graph database works by indexing node embeddings to retrieve ranked results with similarity scores, enabling fast approximate nearest neighbor matching for semantic retrieval workflows.

Yes, you can run semantic search queries against stored embeddings in FalkorDB by executing vector similarity queries that retrieve ranked nodes based on similarity scores for recommendation systems and similarity matching.

Before querying vector embeddings in FalkorDB, you need to configure the HNSW index with specific embedding dimensions, select an appropriate similarity function, and ensure your node properties contain the embedded data.

Use approximate nearest neighbor search for graph embeddings when building semantic search applications, recommendation systems, or similarity matching workflows that require fast retrieval across large datasets instead of exact matching.