When to Use

Creating a vector index (CREATE VECTOR INDEX) on nodes or relationships
Running vector similarity / nearest-neighbor search
Storing embeddings on graph nodes during ingestion
Indexing/querying embeddings already written by GDS algorithms
Choosing similarity function, dimensions, HNSW params, or quantization
Using SEARCH clause (2026.01+) or db.index.vector.queryNodes() (2025.x)
Batch-updating embeddings after model change
Combining vector results with immediate graph neighborhood (full retrieval_query pipelines → neo4j-graphrag-skill)
Hybrid search that combines vector results with fulltext or other ranked sources

When NOT to Use

GraphRAG pipelines (VectorCypherRetriever, HybridCypherRetriever, retrieval_query) → neo4j-graphrag-skill
Fulltext-only / keyword-only search (FULLTEXT INDEX, db.index.fulltext.queryNodes) → neo4j-cypher-skill
Computing GDS graph embeddings (FastRP, Node2Vec, GraphSAGE) → neo4j-gds-skill
Index admin (list all indexes, drop range/text/lookup indexes) → neo4j-cypher-skill